Computational Systems Biology
Publications
Submitted, preprints (selected)
SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data,
Mine Ögretir, Miika Koskinen, Juha Sinisalo, Risto Renkonen, Harri Lähdesmäki,
[arxiv] [software]
DeconV: Probabilistic Cell Type Deconvolution from Bulk RNA-sequencing Data
Artur Gynter, Dimitri Meistermann, Harri Lähdesmäki, Helena Kilpinen,
[biorxiv] [software]
2025
Balık M, Sinelnikov M, Ong P, Lähdesmäki H,
Bayesian basis function approximation for scalable Gaussian process priors in deep generative models,
ICML, Proceedings of the 42nd International Conference on Machine Learning (ICML) 2025.
[arxiv] [abstract, pdf] [software]
Timonen J, Lähdesmäki H,
Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal data,
Bayesian Analysis, to appear.
[arxiv] [software]
Ong P, Haussmann M, Lönnroth O, Lähdesmäki H,
Latent mixed-effect models for high-dimensional longitudinal data,
TMLR, Transactions on Machine Learning Research, 2025.
[pdf] [software]
Do C, Lähdesmäki H,
Incorporating hierarchical information into multiple instance learning for patient phenotype prediction with scRNA-seq data,
Bioinformatics (ISMB/ECCB), to appear.
[biorxiv] [software]
Lundgren S, Huuhtanen J, Keränen M, Feng X Patel BA, Ryland GL, Fox LC, Bravo-Perez C, Clemente M, Kerr C, Walldin G, Dufva O, Zaimoku Y, Tuononen T, Myllymäki M, Ebeling F, Jokinen E, Heinonen M, Kasanen T, Klievink J, Lähteenmäki H, Jaatinen T, Kytölä S, Siitonen S, Dulau-Florea A, Braylan R, Heinäniemi M, Nakao S, Hellström-Lindberg E, Maciejewski JP, Blombery P, Young NS, Lähdesmäki H, Mustjoki, S,
Single-cell analysis of aplastic anemia reveals a convergence of NK and NK-like CD8+ T cells with a disease-associated TCR signature,
Science Translational Medicine, Vol 17, No. 787, 2025.
[pubmed, html, pdf]
Ramchandran S, Haussman M, Lähdesmäki H,
High-dimensional Bayesian optimisation with Gaussian process priors variational autoencoders,
ICLR, The Thirteenth International Conference on Learning Representations (ICLR) 2025.
[pdf] [software]
Dumitrescu A, Korpela D, Heinonen M, Verma Y, Iakovlev V, Garg V, Lähdesmäki H,
E(3)-equivariant models cannot learn chirality: Field-based molecular generation,
ICLR, The Thirteenth International Conference on Learning Representations (ICLR) 2025.
[pdf] [software]
Iakovlev V, Lähdesmäki H,
Learning spatiotemporal dynamical systems from point process observations,
ICLR, The Thirteenth International Conference on Learning Representations (ICLR) 2025.
[pdf] [software]
2024
Sinelnikov M, Haussmann M, Lähdesmäki H,
Latent variable model for high-dimensional point process with structured missingness,
ICML, Proceedings of the 41st International Conference on Machine Learning (ICML) 2024.
[arxiv] [abstract, pdf] [software]
Ong P, Haussmann M, Lähdesmäki H,
Learning high-dimensional mixed models via amortized variational inference,
ICML Workshop on Structured Probabilistic Inference & Generative Modeling, 2024.
[openreview] [software]
Haussmann M, Le TMS, Halla-aho V, Kurki S, Leinonen J, Koskinen M, Kaski S, Lähdesmäki H,
Estimating treatment effects from single-arm trials via latent-variable modeling,
AISTATS, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS) 2024.
[arxiv] [abstract, pdf] [software]
Kurki S, Halla-aho V, Haussmann M, Lähdesmäki H, Leinonen J, Koskinen M,
Clinical trial and real-world data: A comparative study in patients with diabetic kidney disease,
Scientific Reports, Vol. 14, Article number, 1731, 2024.
[medrxiv, html, pdf] [software]
Ramchandran S, Tikhonov G, Lönnroth O, Tiikkainen P, Lähdesmäki H,
Learning conditional variational autoencoders with missing covariates,
Pattern Recognition, Vol. 147, 110113, 2024.
[arxiv] [html, pdf] [software]
Huuhtanen J, Adnan-Awad S, Theodoropoulos J, Forsten S, Warfvinge R, Dufva O, Bouhlal J, Dhapola P, Duàn H, Laajala E, Kasanen T, Klievink J, Ilander M, Jaatinen T, Olsson-Strömberg U, Hjorth-Hansen H, Burchert A, Karlsson G, Kreutzman A, Lähdesmäki H, Mustjoki S,
Single-cell analysis of immune recognition in chronic myeloid leukemia patients following tyrosine kinase inhibitor discontinuation,
Leukemia, Vol. 38, No. 1, pp. 109-125, 2024.
[pubmed, html, pdf, suppl.]
2023
Korpela D, Jokinen E, Dumitrescu A, Huuhtanen J, Mustjoki S, Lähdesmäki H,
EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings,
Bioinformatics, Vol. 39, No. 12, btad743, 2023.
[pubmed, html, pdf] [software]
Iakovlev V, Heinonen M, Lähdesmäki H,
Learning space-time continuous neural PDEs from partially observed states,
NeurIPS, Proceedings of Neural Information Processing Systems, 2023.
[arxiv] [abstract, pdf, suppl.] [software]
Dufva O, Gandolfi S, Huuhtanen J, Dashevsky O, Duàn H, Saeed K, Klievink J, Nygren P, Bouhlal J, Lahtela J, Näätänen A, Ghimire B, Hannunen T, Ellonen P, Lähteenmäki H, Rumm P, Theodoropoulos J, Laajala E, Härkönen J, Pölönen P, Heinäniemi M, Hollmen M, Yamano S, Shirasaki R, Barbie D, Roth JA, Romee R, Sheffer M, Lähdesmäki H, Lee DA, Simoes RDM, Kankainen M, Mitsiades CS, Mustjoki S,
Single-cell functional genomics reveals determinants of sensitivity and resistance to natural killer cells in blood cancers,
Immunity, Vol. 56, No. 12, pp. 2816-2835.
[pubmed, html, pdf, suppl.]
Hirvonen MK, Lietzén N, Moulder R, Bhosale S, Koskenniemi J, Vähä-Mäkilä M, Nurmio M, Oresic M, Ilonen J, Toppari J, Veijola R, Hyöty H, Lähdesmäki H, Knip M, Cheng L, Lahesmaa R,
Serum APOC1 levels are decreased in young autoantibody positive children who rapidly progress to type 1 diabetes,
Scientific Reports, 13, No. 15941, 2023.
[pubmed, html, pdf]
Lönnroth O, Ramchandran S, Tiikkainen P, Öğretir M, Leinonen J, Lähdesmäki H,
Adverse event prediction using a task-specific generative model,
ICML Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2023.
[openreview] [software]
Dumitrescu A, Jokinen E, Korpela D, Lähdesmäki H,
Structure-guided T cell receptor and epitope interaction prediction,
ICML Workshop on Computational Biology, 2023.
[link] [software]
Ögretir M, Morton J, Lähdesmäki H,
Longitudinal variational autoencoder for compositional data analysis,
ICML Workshop on Computational Biology, 2023.
[link] [software]
Timonen J, Bales B, Siccha N, Lähdesmäki H, Vehtari A,
An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models,
Stat, Vol. 12, No. 1, e614, 2023.
[arxiv] [html, pdf]
Dumitrescu A, Jokinen E, Kellosalo J, Paavilainen V, Lähdesmäki H,
TSignal: A transformer model for signal peptide prediction,
Bioinformatics (ISMB/ECCB), Vol. 39, No. S1, pp. i347-i356, 2023.
[html, pdf] [software]
Iakovlev V, Yildiz C, Heinonen M, Lähdesmäki H,
Latent neural ODEs with sparse Bayesian multiple shooting,
ICLR, The Eleventh International Conference on Learning Representations (ICLR) 2023.
[arxiv, openreview] [software]
Huuhtanen J, Kasanen H, Peltola K, Lönnberg T, Glumoff V, Brück O, Dufva O, Peltonen K, Vikkula J, Jokinen E, Ilander M, Lee MH, Mäkelä S, Nyakas M, Li B, Hernberg M, Bono P, Lähdesmäki H, Kreutzman A, Mustjoki S,
Single-cell characterization of anti-LAG3+anti-PD1 treatment in melanoma patients,
The Journal of Clinical Investigations, Vol. 133, No. 6, e164809, 2023.
[pubmed, html, pdf]
Jokinen E, Dumitrescu A, Huuhtanen J, Gligorijevic V, Mustjoki S, Bonneau R, Heinonen M, Lähdesmäki H,
Determining recognition between TCRs and epitopes using contextualized motifs,
Bioinformatics, Vol. 39, No. 1, btac788, 2023.
[pubmed, html, pdf] [software]
Malonzo M, Lähdesmäki H,
LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model,
BMC Bioinformatics, Vol. 24, No. 58, 2023.
[pubmed, html, pdf] [software]
2022
Huuhtanen J, Chen L, Jokinen E, Kasanen H, Lönnberg T, Kreutzman A, Peltola K, Hernberg M, Wang C, Yee C, Lähdesmäki H, Davis MM, Mustjoki S,
Evolution and modulation of antigen-specific T cell responses in melanoma patients,
Nature Communications, 13, No. 5988, 2022.
[pubmed, html, pdf]
Ögretir M, Ramchandran S, Papatheodorou D, Lähdesmäki H,
A variational autoencoder for heterogeneous temporal and longitudinal data,
IEEE ICMLA, IEEE 2022 International Conference on Machine Learning and Applications, 2022.
[arxiv] [software]
Hegde P, Yıldız C, Lähdesmäki H, Kaski S, Heinonen M,
Variational multiple shooting for Bayesian ODEs with Gaussian processes,
UAI, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, 2022.
[abstract, pdf, suppl.] [software]
Osmala M, Eraslan G, Lähdesmäki H,
ChromDMM: A Dirichlet-multinomial mixture model for clustering heterogeneous epigenetic data,
Bioinformatics, Vol. 38, No. 16, pp. 3863-3870, 2022.
[pubmed, html, pdf] [software]
Antikainen AA, Heinonen M, Lähdesmäki H,
Modeling binding specificities of transcription factor pairs with random forests,
BMC Bioinformatics, Vol. 23, No. 212, 2022.
[pubmed, html, pdf]
Probabilistic modeling methods for cell-free DNA methylation based cancer classification,
Halla-aho V, Lähdesmäki H,
BMC Bioinformatics, 23, No. 119, 2022.
[pubmed, html, pdf] [biorxiv] [software]
Huuhtanen J, Bhattacharya D, Lönnberg T, Kankainen M, Kerr C, Theodoropoulos J, Rajala H, Gurnari C, Kasanen T, Braun T, Teramo A, Zambello R, Herling M, Ishida F, Kawakami T, Salmi M, Loughran T, Maciejewski JP, Lähdesmäki H, Kelkka T, Mustjoki S
Single-cell characterization of leukemic and non-leukemic immune repertoires in CD8+ T-cell large granular lymphocytic leukemia,
Nature Communications, 13, No. 1981, 2022.
[pubmed, html, pdf]
Michele Vantini, Henrik Mannerström, Sini Rautio, Helena Ahlfors, Brigitta Stockinger, Harri Lähdesmäki,
PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies,
Computers in Biology and Medicine, Vol. 143, No. 105268, 2022.
[arxiv] [pubmed, html, pdf] [software]
Laajala E, Halla-aho V, Grönroos T, Ullah U, Vähä-Mäkilä M, Nurmio M, Kallionpää H, Lietzén N, Mykkänen J, Rasool O, Toppari J, Orešič M, Knip M, Lund R, Lahesmaa R, Lähdesmäki H,
Permutation-based significance analysis reduces the type 1 error rate in bisulfite sequencing data analysis of human umbilical cord blood samples,
Epigenetics, Vol. 17, No. 12, pp.1608-1627, 2022.
[pubmed, biorxiv]
Laajala E, Ullah U, Grönroos T, Rasool O, Halla-aho V, Konki M, Kattelus R, Mykkänen J, Nurmio M, Vähä-Mäkilä M, Kallionpää H, Lietzén N, Ghimire BR, Laiho A, Hyöty H, Elo LL, Ilonen J, Knip M, Lund RJ, Orešič M, Veijola R, Lähdesmäki H, Toppari J, Lahesmaa, R
Umbilical Cord Blood DNA Methylation in Children Who Later Develop Type 1 Diabetes,
Diabetologia, Vol. 65, No. 9, pp. 1534-1540, 2022.
[pubmed, html, pdf]
Starskaia I, Laajala E, Grönroos T, Junttila S, Kattelus R, Kallionpää H, Laiho A, Suni V, Lähdesmäki H, Elo L, Lund R, Knip M, Kalim UU, Lahesmaa R,
Early DNA methylation changes in children developing beta-cell autoimmunity at a young age,
Diabetologia, 65, pages 844-860, 2022.
[pubmed, html, pdf]
Malonzo M, Halla-aho V, Konki M, Lund R, Lähdesmäki H,
LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis,
BMC Bioinformatics, Vol. 23, No. 41, 2022.
[biorxiv] [html, pdf, software]
Iakovlev V, Heinonen M, Lähdesmäki H,
Enforcing physics-based algebraic constraints for inference of PDE models on unstructured grid,
[pdf] [software]
2021
James T. Morton, Justin Silverman, Gleb Tikhonov, Harri Lähdesmäki, Richard Bonneau,
Scalable estimation of microbial co-occurrence networks with variational autoencoders,
NeurIPS workshop on Learning Meaningful Representations of Life, 2021.
[biorxiv] [software]
Yıldız C, Heinonen M, Lähdesmäki H,
Continuous-time model-based reinforcement learning,
ICML, Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139:12009-12018, 2021.
[arxiv] [abstract, pdf, suppl.] [software]
Ramchandran S, Tikhonov G, Kujanpää K, Koskinen M, Lähdesmäki H,
Longitudinal variational autoencoder,
AISTATS, Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, PMLR 130:3898-3906, 2021.
[arxiv] [abstract, pdf, suppl.] [software]
Ramchandran S, Koskinen M, Lähdesmäki H,
Latent Gaussian process with composite likelihoods and numerical quadrature,
AISTATS, Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, PMLR 130:3718-3726, 2021.
[arxiv] [abstract, pdf, suppl.] [software]
Jokinen E, Huuhtanen J, Mustjoki S, Heinonen M, Lähdesmäki H,
Predicting epitope-specificity of T cell receptors with TCRGP,
PLoS Computational Biology, Vol. 17, No. 3, e1008814, 2021.
[biorxiv] [advance access] [Software]
Iakovlev V, Heinonen M, Lähdesmäki H,
Learning continuous-time PDEs from sparse data with graph neural networks,
ICLR, The Ninth International Conference on Learning Representations (ICLR) 2021.
[arxiv] [abstract, pdf] [software]
Timonen J, Mannerström H, Vehtari A, Lähdesmäki H,
lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data,
Bioinformatics, Vol. 37, No. 13, pp. 1860-1867, 2021.
[arxiv] [pubmed, html, pdf] [software]
Lundgren S, Keränen MAI, Kankainen M, Huuhtanen J, Walldin G, Kerr CM, Clemente M, Ebeling F, Rajala H, Brück O, Lähdesmäki H, Hannula S, Hannunen T, Ellonen P, Young N, Ogawa S, Maciejewski JP, Hellström-Lindberg E, Mustjoki S,
Somatic mutations in lymphocytes in patients with immune-mediated aplastic anemia,
Leukemia, Vol. 35, pp. 1365-1379, 2021.
[pubmed, html, pdf]
Damian R. Plichta, Juhi Somani, Matthieu Pichaud, Zachary S. Wallace, Ana D. Fernandes, Cory A. Perugino, Harri Lähdesmäki, John H. Stone, Hera Vlamakis, Daniel Chung, Dinesh Khanna, Shiv Pillai, Ramnik J. Xavier,
Congruent microbiome signatures in fibrosis-prone autoimmune diseases: IgG4-related disease and systemic sclerosis,
Genome Medicine, Vol. 13, No. 35, 2021.
[pubmed, html, pdf]
2020
Tiina Kelkka, Paula Savola, Dipabarna Bhattacharya, Jani Huuhtanen, Tapio Lönnberg, Matti Kankainen, Kirsi Paalanen, Mikko Tyster, Maija Lepistö, Pekka Ellonen, Johannes Smolander, Samuli Eldfors, Bhabwan Yadav, Sofia Khan, Riitta Koivuniemi, Christopher Sjöwall, Laura L Elo, Harri Lähdesmäki, Yuka Maeda, Hiroyashi Nishikawa, Marjatta Leirisalo-Repo, Tuulikki Sokka-Isler, Satu Mustjoki,
Adult-onset anti-citrullinated peptide antibody-negative destructive rheumatoid arthritis is characterized by a disease-specific CD8+ T lymphocyte signature,
Frontiers in Immunology, Vol. 11, No. 578848, 2020.
[pubmed, html, pdf]
Sanni Voutilainen, Markus Heinonen, Martina Andberg, Emmi Jokinen, Hannu Maaheimo, Johan Pääkkönen, Nina Hakulinen, Juha Rouvinen, Harri Lähdesmäki, Samuel Kaski, Juho Rousu, Merja Penttilä, Anu Koivula,
Substrate specificity of 2-Deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods,
Applied Microbiology and Biotechnology, No. 104, pp. 10515–10529, 2020.
[pubmed, html, pdf]
Halla-aho V, Lähdesmäki H,
LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation,
Bioinformatics, Vol. 36, No. 17, pp. 4535-4543, 2020.
[pubmed, html, pdf] [software]
Maria Osmala, Harri Lähdesmäki,
Enhancer prediction in the human genome by probabilistic modeling of the chromatin feature patterns,
BMC Bioinformatics, Vol. 21, No. 317, 2020.
[pubmed, html, pdf] [software]
Juhi Somani*, Siddharth Ramchandran*, Harri Lähdesmäki,
A personalised approach for identifying disease-relevant pathways in heterogeneous diseases,
npj Systems Biology and Applications, Vol. 6, No. 1:17, 2020.
[pubmed, html, pdf] [software]
Halla-aho V, Lähdesmäki H,
LuxHS: DNA methylation analysis with spatially varying correlation structure,
In Lecture Notes in Bioinformatics (Proceedings of the 8th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2020), pp. 505-516, 2020.
[biorxiv] [html] [software]
Savola P, Martelius T, Kankainen M, Huuhtanen J, Lundgren S, Koski Y, Eldfors S, Kelkka T, Keränen MAI, Ellonen P, Kovanen P, Kytölä S, Saarela J, Lähdesmäki H, Seppänen M, Mustjoki S,
Somatic mutations and T-cell clonality in patients with immunodeficiency,
Haematologica, Vol. 105, No. 12, pp. 2757-2768, 2020.
[pubmed, abstract, pdf]
Charles Gadd, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
Sample-efficient reinforcement learning using deep Gaussian processes,
[arxiv] [software]
2019
Intosalmi J, Scott AC, Hayes M, Flann N, Yli-Harja O, Lähdesmäki H, Dudley AM and Skupin A,
Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies,
BMC Molecular and Cell Biology, Vol. 20, No. 59, 2019.
[pubmed, html, pdf, suppl.]
Yıldız C, Heinonen M, Lähdesmäki H,
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks,
NeurIPS, Proceedings of Neural Information Processing Systems, Vol. 32, 2019.
[abstract, pdf, suppl.] [arxiv] [software]
Kallionpää H*, Somani J*, Tuomela S*, Ullah U*, de Albuquerque R, Lönnberg T, Komsi E, Siljander H, Honkanen J, Härkönen T, Peet A, Tillmann V, Chandra V, Kumar Anagandula M, Frisk G, Otonkoski T, Rasool O, Lund R, Lähdesmäki H, Knip M, Lahesmaa R,
Early detection of peripheral blood cell signature in children developing beta-cell autoimmunity at a young age,
Diabetes, Vol. 68, No. 10, pp. 2024-2034, 2019.
[pubmed, html, pdf]
Konki M, Malonzo M, Karlsson I, Lindgren N, Ghimire B, Smolander J, Scheinin N, Ollikainen M, Laiho A, Elo LL, Lönnberg T, Röyttä M, Pedersen N, Kaprio J, Lähdesmäki H, Rinne J, Lund RJ,
Peripheral blood DNA methylation differences in twin pairs discordant for Alzheimer's disease,
Clinical Epigenetics, Vol. 11, No. 130, 2019.
[pubmed, html, pdf]
[biorxiv]
Cheng L, Ramchandran S, Vatanen T, Lietzen N, Lahesmaa R, Vehtari A and Lähdesmäki H,
An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data,
Nature Communications, Vol. 10, No. 1798, 2019.
[pubmed, html, pdf] [Software]
Heinonen M, Osmala M, Mannerström H, Wallenius J, Kaski S, Rousu J and Lähdesmäki H,
Bayesian metabolic flux analysis reveals intracellular flux couplings,
Bioinformatics (ISMB'19), Vol. 35, No. 14, pp. i548–i557, 2019.
[pubmed, html, pdf] [Software]
Nousiainen K, Intosalmi J, Lähdesmäki H
A mathematical model for enhancer activation kinetics during cell differentiation,
In 6th International Conference on Algorithms for Computational Biology (AlCoB), Berkeley, California, USA - May 28-30, 2019.
[html, pdf/book]
Hegde P, Heinonen M, Lähdesmäki H, Kaski S,
Deep learning with differential Gaussian process flows,
AISTATS 2019, Proceedings of Machine Learning Research, PMLR, 89:1812-1821, 2019.
(This is an extended version of the previous workshop paper.)
[html, pdf, suppl.] [Software]
2018
Hegde P, Heinonen M, Lähdesmäki H, Kaski S,
Deep learning with differential Gaussian process flows,
NeurIPS Workshop on Bayesian Deep Learning, 2018.
[pdf] [poster] [Software]
Vatanen T, Plichta D, Somani J, Münch P, Arthur T, Hall A, Rudolf S, Oakeley E, Ke X, Young R, Haiser H, Kolde R, Yassour M, Luopajärvi K, Siljander H, Virtanen S, Ilonen J, Uibo R, Tillmann V, Mokurov S, Dorshakova N, Porter J, McHardy A, Lähdesmäki H, Vlamakis H, Huttenhower C, Knip M, and Ramnik Xavier
Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life,
Nature Microbiology, Vol. 4, No. 3, pp. 470-479, 2019.
[pubmeb, html, pdf, suppl. materials]
Yildiz C, Heinonen M, Lähdesmäki H,
A non-parametric spatio-temporal SDE model,
NeurIPS Workshop on Modeling and Decision-Making in the Spatiotemporal Domain, 2018.
[pdf] [Software]
Vatanen T, Franzosa EA, Schwager R, Tripathi S, Arthur TD, Vehik K, Lernmark Å, Hagopian WA, Rewers MJ, She J-X, Toppari J, Ziegler A-G, Akolkar B, Krischer JP, Stewart CJ, Ajami NJ, Petrosino JF, Gevers D, Lähdesmäki H, Vlamakis H, Huttenhower C, Xavier RJ,
The human gut microbiome in early-onset type 1 diabetes from the TEDDY study,
Nature, Vol. 562, No. 7728, pp. 589-594, 2018.
[pubmed, html, pdf, suppl. materials]
Äijö T, Bonneau R and Lähdesmäki H,
Generative Models for Quantification of DNA Modifications,
In Mamitsuka H. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, Vol. 1807, pp. 37–50. Humana Press, New York, NY, 2018.
[link]
Yildiz C, Heinonen M, Mannerström H, Intosalmi J, and Lähdesmäki H,
Learning stochastic differential equations with Gaussian processes without gradient matching,
MLSP, IEEE International Workshop on Machine Learning for Signal Processing 2018.
[html, pdf] [arxiv] [Software]
Nousiainen K†, Kanduri K†, Ricaño-Ponce I, Wijmenga C, Lahesmaa R, Kumar V, Lähdesmäki H,
snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions,
Bioinformatics, Vol. 34, No. 23, pp. 4112-4114, 2018.
[pubmed, html, pdf, suppl. materials] [Software]
Heinonen M, Yildiz C, Mannerström H, Intosalmi J, Lähdesmäki H,
Learning unknown ODE models with Gaussian processes,
ICML, Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80:1959-1968, 2018.
[abs, pdf, suppl.] [arxiv] [Software]
Lund RJ, Osmala M, Malonzo M, Lukkarinen M, Leino A, Salmi J, Vuorikoski S, Turunen R, Vuorinen T, Akdis C, Lähdesmäki H, Lahesmaa R, Jartti T,
Atopic asthma after rhinovirus induced wheezing is associated with DNA methylation change in the SMAD3 gene promoter,
Allergy, Vol. 73, No. 8, pp. 1735-1740, 2018.
[pubmed, abstract, html, pdf, suppl. materials]
Mohammad I, Nousiainen K, Bhosale SD, Starskaia I, Moulder R, Rokka A, Cheng F, Mohanasundaram P, Eriksson JE, Goodlett RD, Lähdesmäki H, Chen Z,
Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells,
PLoS Biology, Vol. 16, No. 5, e2004194, 2018.
[pubmed, abstract, html, pdf, suppl. materials]
Jokinen E, Heinonen M and Lähdesmäki H
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion,
Bioinformatics (ISMB2018), Vol. 34, No. 13, pp. i274-i283, 2018.
[pubmed, abstract, html, pdf, suppl. materials]
[arxiv] [Software]
Schmidt A, Marabita F, Kiani NA, Gross CC, Johansson HJ, Éliás1 S, Rautio S, Eriksson M, Fernandes SJ, Silberberg G, Ullah U, Bhatia U, Lähdesmäki H, Lehtiö J, Gomez-Cabrero D, Wiendl H, Lahesmaa R and Tegnér J,
Time-resolved transcriptome and proteome landscape of human regulatory Tcell (Treg) differentiation reveals novel regulators of FOXP3,
BMC Biology, Vol. 16, No. 1, 2018.
[pubmed, abstract, html, pdf, suppl. materials]
Timonen J, Mannerström, Lähdesmäki H and Intosalmi J
A probabilistic framework for molecular network structure inference by means of mechanistic modeling,
IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 16, No. 6, pp. 1843-1854, 2019.
[pubmed, html, pdf, suppl. materials]
Lietzén N, Cheng L, Moulder R, Siljander H, Laajala E, Härkönen T, Peet A, Vehtari A, Tillmann V, Knip M, Lähdesmäki H and Lahesmaa R,
Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood,
Scientific Reports 8, No. 5883, 2018.
[pubmed, abstract, html, pdf, suppl. materials]
Ullah U, Andrabi SBA, Tripathi SK, Dirasantha O, Kanduri K, Rautio S, Gross CC, Lehtimäki S, Bala K, Tuomisto J, Bhatia U, Chakroborty D, Elo L, Lähdesmäki H, Wiendl H, Rasool O and Lahesmaa R,
Transcriptional repressor HIC1 contributes to suppressive function of human induced regulatory T cells,
Cell Reports, Vol. 22, No. 8, pp. 2094-2106, 2018.
[pubmed, abstract, html, pdf, suppl. materials]
Intosalmi J, Mannerström H, Hiltunen S, and Lähdesmäki H,
SCHiRM: Single Cell Hierarchical Regression Model to detect dependencies in read count data,
[biorxiv] [Software]
2017
Tripathi SK, Chen Z, Larjo A, Kanduri K, Nousiainen K, Äijö T, Ricano-Ponce I, Hrdlickova B, Tuomela S, Laajala E, Salo V, Kumar V, Wijmenga C, Lähdesmäki H and Lahesmaa R,
Genome-wide analysis of STAT3-mediated transcription during early human Th17 cell differentiation,
Cell Reports, Vol. 19, pp. 1888-1901, 2017.
[pubmed, abstract, html, pdf, suppl. materials]
Lund RJ, Rahkonen N, Malonzo M, Kauko L, Emani MR, Kivinen V, Närvä E, Kemppainen E, Laiho A, Skottman H, Hovatta O, Rasool O, Nykter M, Lähdesmäki H, Lahesmaa R,
RNA polymerase III subunit POLR3G regulates a specific subset of polyA+ and smallRNA transcriptomes and splicing in human pluripotent stem cells,
Stem Cell Reports, Vol. 8, pp. 1442-1454, 2017.
[pubmed, abstract, html, pdf, suppl. materials]
Tsagaratou A, Gonzalez-Avalos E, Rautio S, Scott-Browne J, Togher S, Pastor WA, Rothenberg EV, Chavez L, Lähdesmäki H, Rao A,
TET proteins regulate the lineage specification and TCR-mediated expansion of iNKT cells,
Nature Immunology, Vol. 18, No. 1, pp. 45-53, 2017.
[pubmed, abstract, html, pdf, suppl. materials]
2016
Chan YH, Intosalmi J, Rautio S, Lähdesmäki H,
A subpopulation model to analyze heterogeneous cell differentiation dynamics,
Bioinformatics, Vol. 32, No. 21, pp. 3306-3313, 2016.
[pubmed, abstract, html, pdf, suppl. materials]
[Software]
Äijö T, Yue X, Rao A, Lähdesmäki H,
LuxGLM: A probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs,
Bioinformatics (ECCB2016), Vol. 32, No. 17, pp. i511-i519, 2016.
[pubmed, abstract, html, pdf, suppl. materials]
[Software]
Intosalmi J, Nousiainen K, Ahlfors H, Lähdesmäki H,
Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks,
Bioinformatics (ISMB2016), Vol. 32, No. 12, pp. i288-i296, 2016.
[pubmed, abstract, html, pdf]
[Software]
Rahkonen N, Stubb A, Malonzo M, Edelman S, Emani MR, Närvä E, Lähdesmäki, Baker-Ruokola H, Lahesmaa R, Lund R,
Mature Let-7 miRNAs fine tune expression of LIN28B proteins in pluripotent human embryonic stem cells,
Stem Cell Research, Vol. 17, No. 3, pp. 498-503, 2016.
[pubmed, abstract, html, pdf]
Vatanen T, Kostic AD, d’Hennezel E, Siljander H, Franzosa EA, Yassour M, Kolde R, Vlamakis H, Arthur TD, Hämäläinen A-M, Peet A, Tillmann V, Uibo R, Mokurov S, Dorshakova N, Ilonen J, Virtanen SM, Szabo SJ, Porter J, Lähdesmäki H, Huttenhower C, Gevers D, Cullen TW, Knip M on behalf of the DIABIMMUNE Study Group, Xavier RJ,
Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans,
Cell, Vol. 165, No. 4, pp. 842-853, 2016.
[pubmed, abstract, html, pdf]
[project website]
Äijö T, Huang Y, Mannerström H, Chavez L, Tsagaratou A, Rao A, Lähdesmäki H,
A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways,
Genome Biology, 17:49, 2016.
[pubmed, html, pdf, suppl. materials]
[Software]
Yue X, Trifari S, Äijö T, Tsagaratou A, Pastor W, Zepeda-Martinez JA, Huang Y, Vijayanand P, Lähdesmäki H, Rao A,
Control of Foxp3 stability through modulation of TET activity,
Journal of Experimental Medicine, Vol. 213, No. 3, pp. 377-397, 2016.
[pubmed, abstract, html, pdf]
Rantasalo A, Czeizler E, Virtanen R, Rousu J, Lähdesmäki H, Penttilä M, Jäntti J, Mojzita D,
Synthetic transcription amplifier system for orthogonal control of gene expression in Saccharomyces cerevisiae,
PLoS ONE, Vol. 11, No. 2, e0148320, 2016.
[pubmed, html, pdf]
Tuomela S, Rautio S, Ahlfors H, Öling V, Salo V, Chen Z, Hämälistö S, Tripathi SK, Ullah U, Äijö T, Soueidan H, Wessels L, Stockinger B, Lähdesmäki H, Lahesmaa R,
Comparative analysis of human and mouse transcriptomes of Th17 cell priming,
Oncotargets, Vol. 7, No. 12, pp. 13416-13428, 2016.
[pubmed, abstract, html, pdf]
Konki M, Pasumarthy K, Malonzo M, Sainio A, Valensisi C, Söderström M, Emani MR, Stubb A, Närvä E, Ghimire B, Laiho A, Järveläinen H, Lahesmaa R, Lähdesmäki H, Hawkins RD, Lund RJ,
Epigenetic silencing of the key antioxidant enzyme catalase in karyotypically abnormal human pluripotent stem cells,
Scientific Reports, Vol. 6, No. 22190, 2016.
[pubmed, html, pdf]
Heinonen M, Mannerström H, Rousu J, Kaski S, Lähdesmäki H,
Non-stationary Gaussian process regression with Hamiltonian Monte Carlo,
In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, to appear.
[abstract, pdf, suppl]
[Software]
2015
Rautio S, Lähdesmäki H,
MixChIP: A probabilistic method for cell type specific protein-DNA binding analysis,
BMC Bioinformatics, Vol. 16, No. 413, 2015.
[pubmed, abstract, html, pdf]
[software]
Intosalmi J, Ahlfors H, Rautio S, Mannerström H, Chen ZJ, Lahesmaa R, Stockinger B, Lähdesmäki H,
Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data,
BMC Systems Biology, Vol. 9, No. 81, 2015.
[pubmed, abstract, html, pdf]
Kanduri K, Tripathi S, Larjo A, Mannerström H, Ullah U, Lund R, Hawkins D, Ren B, Lähdesmäki, Lahesmaa R,
Identification of global regulators of T-helper cell lineage specification,
Genome Medicine, 7:122, 2015.
[pubmed, abstract, html, pdf]
Balasubramani A, Larjo A, Chang X, Hastie R, Togher S, Bassein J, Lähdesmäki, Rao A,
Cancer-associated ASXL1 mutations may act as gain-of-function mutations of the ASXL1-BAP1 complex,
Nature Communications, Vol. 6, No. 7307, 2015.
[abstract, html, pdf]
Larjo A and Lähdesmäki H,
Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning,
EURASIP Journal on Bioinformatics and Systems Biology, Vol. 6, 2015.
[abstract, html, pdf]
Kähärä J, Lähdesmäki H,
BinDNase: A discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data,
Bioinformatics, Vol. 31, No. 17, pp. 2852-2859, 2015.
[abstract, html, pdf]
[software]
Moulder R, Bhosale SD, Erkkilä T, Laajala E, Salmi J, Nguyen EV, Kallionpää H, Mykkänen J, Vähä-Mäkilä M, Hyöty H, Veijola R, Ilonen J, Simell T, Toppari J, Knip M, Goodlett DR, Lähdesmäki H, Simell O, Lahesmaa R,
Serum proteomes distinguish type-1 diabetes developing children in a cohort with HLA-conferred susceptibility,
Diabetes, Vol. 64, No. 6, pp. 2265-2278, 2015.
[abstract, html, pdf]
Kostic AD, Gevers D, Siljander H, Vatanen T, Hyötyläinen T, Hämäläinen A-M, Peet A, Tillman V, Pöho P, Mattila I, Lähdesmäki H, Franzosa EA, Vaarala O, de Goffau M, Harmsen H, Ilonen J, Virtanen S, Clish CB, Oresic M, Huttenhower C, Knip M on behalf of the DIABIMMUNE Study Group, Xavier RJ,
The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes,
Cell Host & Microbe, Vol. 17, No. 2, pp. 260-273, 2015.
[abstract, html, pdf] [pdf]
Martinez GJ, Pereira RM, Äijö T, Kim EY, Marangoni F, Pipkin ME, Togher S, Heissmeyer V, Zhang YC, Crotty S, Lamperti ED, Ansel KM, Mempel TR, Lähdesmäki H, Hogan PG, Rao A,
The transcription factor NFAT promotes exhaustion of activated CD8+ T cells,
Immunity, Vol. 42, No. 2, pp. 265-278, 2015.
[abstract, html, pdf]
Kantojärvi K, Kanduri C, Salo PM, Vanhala R, Buck G, Blancher C Lähdesmäki H, Järvelä I,
The landscape of copy number variations in Finnish families with autism spectrum disorders,
Autism Research, Vol. 9, No. 1, pp. 9-16, 2016.
[abstract, html, pdf]
Heinonen M, Laine A-P, Söderhäll C, Gruzieva O, Rautio S, Melen E, Pershagen G, Lähdesmäki H, Knip M, Ilonen J, Henttinen T, Kere J, Lahesmaa R, The Finnish Pediatric Diabetes Registry,
Gimap GTPase family genes -- potential modifiers in autoimmune diabetes, asthma and allergy,
The Journal of Immunology, Vol. 194, No. 12, pp. 5885-5594.
[abstract, html, pdf]
Kanduri C, Kuusi T, Ahvenainen M, Philips AK, Lähdesmäki H, Järvelä I,
The effect of music performance on the transcriptome of professional musicians,
Scientific Reports, Vol. 5, No. 9506, 2015.
[abstract, html, pdf]
Kanduri C, Raijas P, Ahvenainen M, Philips AK, Ukkola-Vuoti L, Lähdesmäki H, Järvelä I,
The effect of listening to music on human transcriptome,
PeerJ, Vol. 3, e830, 2015.
[abstract, html, pdf]
Vatanen T, Osmala M, Raiko T, Lagus K, Sysi-Aho M, Oresic M, Honkela T, Lähdesmäki H,
Self-organization and missing values in SOM and GTM,
Neurocomputing, Vol. 147, pp. 60-70, 2015.
[abstract, html, pdf]
Kumar V, Gutierrez-Achury J, Kanduri K, Almeida R, Hrdlickova B, Zhernakova DV, Westra H-J, Karjalainen J, Ricaño-Ponce I, Li Y, Stachurska A, Tigchelaar EF, Abdulahad WH, Lähdesmäki H, Hofker MH, Zhernakova A, Franke L, Lahesmaa R, Wijmenga C, Withoff S,
Systematic annotation of celiac disease loci refines pathological pathways and suggests a genetic explanation for increased interferon-gamma levels,
Human Molecular Genetics, Vol. 24, No. 2, pp. 397-409, 2015.
[abstract, html, pdf, supp.]
Heinonen M, Kandury K, Lähdesmäki H, Lahesmaa R, Henttinen T,
Tubulin- and actin-associating GIMAP4 is required for IFN-γ secretion during Th cell differentiation,
Immunology and Cell Biology, Vol. 93, pp. 158-166, 2015.
[abstract, html, pdf, supp.]
Halla-aho V, Mannerström H, Lähdesmäki H,
A probabilistic method for quantifying chromatin interactions,
In Machine Learning in Computational Biology, Montreal, Canada, December 12, 2015.
[pdf]
2014
Tsagaratou A, Äijö T, Lio C-W, Yue X, Huang Y, Jacobsen S, Lähdesmäki H, Rao A,
Dissecting the dynamic changes of 5-hydroxymethylcytosine in T cell development and differentiation,
Proceedings of the National Academy of Sciences of the USA, Vol. 111, No. 32, pp. E3306-E3315, 2014.
[abstract, html, pdf, suppl.]
Äijö T, Butty V, Chen JZ, Salo V, Tripathi S, Burge CB, Lahesmaa R, Lähdesmäki H,
Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation,
Bioinformatics (ISMB’14), Vol. 30, No. 12, pp. i113-i120, 2014.
[abstract, html, pdf, suppl.]
[software]
Kallionpää H, Elo LL, Laajala E, Mykkänen J, Ricanno-Ponce I, Vaarma M, Laajala TD, Hyöty H, Ilonen J, Veijola R, Simell T, Wijmenga C, Knip M, Lähdesmäki H, Simell O and Lahesmaa R,
Innate immune activity is detected prior to seroconversion in children with HLA-conferred T1D susceptibility,
Diabetes, Vol. 63, No. 7, pp. 2402-2414, 2014.
[abstract, html, pdf, suppl.]
Roncagalli R, Hauri S, Fiore F, Liang Y, Chen Z, Kanduri K, Sansoni A, Joly R, Malzac A, Lähdesmäki H, Lahesmaa R, Yamasaki S, Malissen M, Aebersold R, Gstaiger M, Malissen B,
Quantitative proteomic analysis of signalosome dynamics in primary T cells identifies the CD6 surface receptor as a LAT-independent TCR signaling hub,
Nature Immunology, Vol. 15, No. 4, pp. 384-392, 2014.
[abstract, html, pdf]
Laurila K, Autio R, Kong L, Närvä E, Hussein S, Otonkoski T, Lahesmaa R, Lähdesmäki H,
Integrative genomics and transcriptomics analysis of human embryonic and induced pluripotent stem cells,
BioData Mining, 7:32, 2014.
[abstract, html, pdf]
Hrdlickova B, Kumar V, Kanduri K, Zhernakova DV, Tripathi S, Karjalainen J, Lund RJ, Li Y, Ullah U, Modderman R, Abdulahad W, Lähdesmäki H, Franke L, Lahesmaa R, Wijmenga C, Withoff S,
Expression profiles of long non-coding RNAs located in autoim- mune disease-associated regions reveal immune cell type specificity,
Genome Medicine, 6(88), 2014.
[abstract, html, pdf]
Kallionpää H, Laajala E, Öling V, Härkönen T, Tillmann V, Dorshakova NV, Ilonen J, Lähdesmäki H, Knip M, Lahesmaa R,
Standard of hygiene and immune adaptation in newborn infants,
Clinical Immunology, Vol. 155, No. 1, pp. 136-147, 2014.
[abstract, html, pdf]
Noisa P, Lund C, Kanduri K, Lund R, Lähdesmäki H, Lahesmaa R, Lundin K, Chokechuwattanalert H, Otonkoski T, Tuuri T, Raivio T,
Notch signaling regulates neural crest differentiation from human pluripotent stem cells,
Journal of Cell Science, Vol. 127, pp. 2083-2094, 2014.
[abstract, html, pdf]
2013
Hawkins RD¶*, Larjo A*, Tripathi SK*, Wagner U, Luu Y, Lönnberg T, Raghav SK, Lee LK, Lund R, Ren B, Lähdesmäki H¶, Lahesmaa R¶,
Global chromatin state analysis reveals lineage-specific enhancers during the initiation of human T helper 1 and T helper 2 cell polarization,
Immunity, Vol. 38, No. 6, pp. 1271-1284, 2013.
[abstract, html, pdf, suppl.] [pdf]
Närvä E, Pursiheimo J-P, Laiho A, Rahkonen N, Emania MR, Viitala M, Laurila K, Sahla R, Lund R, Lähdesmäki H, Jaakkola P and Lahesmaa R,
Continuous hypoxic culturing of human embryonic stem cells enhances Ssea-3 and Myc levels,
PLoS ONE, Vol. 8, No. 11, e78847, 2013.
[abstract, html, pdf] [pdf]
Ko M, An J, Bandukwala HS, Chavez L, Äijö T, Pastor WA, Segal MF, Li H, Koh KP, Lähdesmäki H, Hogan PG, Aravind L, Rao A,
Modulation of TET2 expression and 5-methylcytosine oxidation by the CXXC domain protein IDAX,
Nature, Vol. 497, No. 7447, pp. 122-126, 2013.
[abstract, html, pdf] [pdf]
Kähärä J, Lähdesmäki H,
Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data,
BMC Bioinformatics, 14(Suppl 10):S2, 2013.
[abstract, html, pdf] [pdf]
Äijö T, Granberg K, Lähdesmäki H,
Sorad: A systems biology approach to predict and modulate dynamic signaling pathway response from phosphoproteome time-course measurements,
Bioinformatics, Vol. 29, No. 10, pp. 1283-1291, 2013.
[abstract, html, pdf, suppl.] [pdf]
[software]
Weirauch MT, Cote A, Norel R, Annala M, Zhao Y, Riley TJ, Saez-Rodriguez J, Cokelaer T, Vedenko A, Talukder S, DREAM5 consortium, Bussemaker HJ, Morris QD, Bulyk ML, Stolovitzky G, Hughes TR,
Evaluation of methods for modeling transcription factor sequence specificity,
Nature Biotechnology, Vol. 31, No. 2, pp. 126-134, 2013.
[abstract, html, pdf, suppl.] [pdf]
Tahvanainen J, Kyläniemi MK, Kanduri K, Gupta B, Lähteenmäki H, Kallonen T, Rajavuori A, Rasool O, Koskinen PJ, Rao KVS, Lähdesmäki H, Lahesmaa R,
Proviral integration site for Moloney murine leukemia virus (PIM) kinases promote human T helper 1 cell differentiation,
The Journal of Biological Chemistry, Vol. 288, No. 5, pp. 3048-3058, 2013.
[abstract, html, pdf] [pdf]
Kanduri C, Ukkola-Vuoti L, Oikkonen J, Buck G, Blancher C, Raijas P, Karma K, Lähdesmäki H, Järvelä I,
The genome wide landscape of copy number variations in the isolated Finnish population: the MUSGEN study provides evidence for a founder effect,
European Journal of Human Genetics, Vol. 288, No. 5, pp. 3048-3058, 2013.
[abstract, html, pdf] [pdf]
Ukkola-Vuoti L, Kanduri C, Oikkonen J, Buck G, Blancher C, Raijas P, Karma K, Lähdesmäki H, Järvelä I,
Genome-wide copy number variation analysis in extended families and unrelated individuals characterized for musical aptitude and creativity in music,
PLoS ONE, Vol. 8, No. 2, e56356, 2013.
[abstract+html] [pdf]
Lehmusvaara S, Erkkilä T, Urbanucci A, Jalava S, Seppälä J, Kaipia A, Kujala P, Lähdesmäki H, Tammela TLJ and Visakorpi T,
Goserelin and bicalutamide treatments alter the expression of microRNAs in prostate,
The Prostate, Vol. 73, No. 1, pp. 101-112, 2013.
[abstract, pdf]
2012
Äijö T, Edelman S, Lönnberg T, Larjo A, Järvenpää H, Tuomela S, Engström E, Lahesmaa R and Lähdesmäki H,
An integrative computational systems biology approach identifies lineage specific dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation,
BMC Genomics, 13:572, 2012.
[abstract, html, pdf, suppl.] [pdf]
[software]
Benson MJ, Äijö T, Chang X, Gagnon J, Pape UJ, Anantharaman V, Aravind L, Pursiheimo J-P, Oberdoerffer S, Liu XS, Lahesmaa R, Lähdesmäki H and Rao A,
Heterogeneous nuclear ribonucleoprotein L-like (hnRNPLL) and elongation factor, RNA polymerase II, 2 (ELL2) are regulators of mRNA processing in plasma cells,
Proceedings of the National Academy of Sciences of the USA, Vol. 109, No. 40, pp. 16252-16257, 2012.
[abstract, pdf, suppl.] [pdf]
Tuomela S, Salo V, Tripathi SK, Chen Z, Laurila K, Äijö T, Gupta B, Oikari L, Stockinger B, Lähdesmäki H and Lahesmaa R,
Identification of early gene expression changes during human Th17 cell differentiation,
Blood, Vol. 119, No. 23, pp. e151-160, 2012.
[abstract, pdf, suppl.] [pdf]
Lehmusvaara S, Erkkilä T, Urbanucci A, Waltering K, Seppälä J, Tuominen V, Isola J, Kujala P, Lähdesmäki H, Kaipia A, Tammela TLJ and Visakorpi T,
Chemical castration and antiandrogens induce differential gene expression in prostate cancer,
The Journal of Pathology, Vol. 227, No. 3, pp. 336--345, 2012.
[abstract, html, pdf, suppl.] [pdf]
Jalava SE, Urbanucci A, Latonen LM, Waltering KK, Sahu B, Jänne OA, Seppälä J, Lähdesmäki H, Tammela TLJ and Visakorpi T,
Androgen-regulated miR-32 targets BTG2 and is overexpressed in castration-resistant prostate cancer,
Oncogene, Vol. 31, No. 41, pp. 4460-4471, 2012.
[abstract, html, pdf, suppl.] [pdf]
Urbanucci A, Sahu B, Seppälä J, Larjo A, Latonen LM, Waltering KK, Tammela TLJ, Vessella RL, Lähdesmäki H, Jänne OA and Visakorpi T,
Overexpression of androgen receptor enhances the binding of the receptor to the chromatin in prostate cancer,
Oncogene, Vol. 31, No. 17, pp. 2153-2163, 2012.
[abstract, html, pdf, suppl.] [pdf]
Närvä E, Rahkonen N, Emani MR, Lund R, Pursiheimo J-P, Nästi J, Autio R, Rasool O, Denessiouk K, Lähdesmäki H, Rao A and Lahesmaa R,
RNA binding protein L1TD1 interacts with LIN28 via RNA and is required for human embryonic stem cell self-renewal and cancer cell proliferation,
Stem Cells, Vol. 30, No. 3, pp. 452-460, 2012.
[abstract, pdf] [pdf]
2011
Annala, M., Laurila, K., Lähdesmäki, H., and Nykter, M.,
A linear model for transcription factor binding affinity prediction in protein binding microarrays,
PLoS ONE, 6(5): e20059, 2011.
[html, pdf] [pdf]
[software]
Porkka, K. P., Ogg, E.-L., Saramäki, O. R., Vessella, R. L., Pukkila, H., Lähdesmäki, H., van Weerden, W. M., Wolf, M., Kallioniemi, O. P., Jenster, G. and Visakorpi, T.,
The miR-15a-miR-16-1 locus is homozygously deleted in a subset of prostate cancers,
Genes, Chromosomes and Cancer, Vol. 50, No. 7, pp. 499-509, 2011.
[abstract, html, pdf] [pdf]
2010
Erkkilä, T., Lehmusvaara, S., Ruusuvuori, P., Visakorpi, T., Shmulevich, I. and Lähdesmäki, H.,
Probabilistic analysis of gene expression measurements from heterogeneous tissues,
Bioinformatics, Vol. 26, No. 20, pp. 2571-2577, 2010.
[abstract, html, pdf, supplementary material] [pdf, supplementary material]
[software, web tool]
Elo, L. L., Järvenpää, H., Tuomela, S., Raghav, S., Ahlfors, H., Laurila, K., Gupta, B., Lund, R. J., Tahvanainen, J., Hawkins, D., Oresic, M., Lähdesmäki, H., Rasool, O., Rao, K. V., Aittokallio, T. and Lahesmaa, R.,
Genome-wide Profiling of Interleukin-4 and STAT6 Transcription Factor Regulation of Human Th2 Cell Programming,
Immunity, Vol. 32, No. 6, pp. 727-862, 2010.
[abstract, html, pdf, supplementary material] [pdf]
Aho, T., Almusa, H., Matilainen, J., Larjo, A., Ruusuvuori, P., Aho, K.-L., Wilhelm, T., Lähdesmäki, H., Beyer, A., Harju, M., Chowdhury, S., Leinonen, K, Roos, C. and Yli-Harja, O.,
Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network,
PLoS ONE, 5(5):e10662, 2010.
[html, pdf] [pdf]
Dai, X. and Lähdesmäki, H.,
Novel data fusion method and exploration of multiple information sources for transcription factor target gene prediction,
EURASIP Journal on Advances in Signal Processing, Special issue on Genomic Signal Processing, Vol. 2010, Article ID 235795, 2010.
[abstract, html, pdf] [pdf]
2009
Laurila, K. and Lähdesmäki, H.,
A protein-protein interaction guided method for competitive transcription factor binding improves target predictions,
Nucleic Acids Research, Vol. 37, No. 22, e146, 2009.
[abstract, html, pdf, supplementary material: pdf]
[software]
Äijö, T. and Lähdesmäki, H.,
Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics,
Bioinformatics, Vol. 25, No. 22, pp. 2937-2944, 2009.
[abstract, pdf] [pdf, supplementary material: pdf]
[software]
Laurila, K. and Lähdesmäki, H.,
Systematic analysis of disease-related regulatory mutation classes reveals distinct effects on transcription factor binding,
In Silico Biology, Vol. 9, 0018, 2009.
[abstract, html] [pdf-preprint]
Dai, X., Erkkilä, T., Yli-Harja, O. and Lähdesmäki, H.,
A joint mixture model for clustering genes from Gaussian and beta distributed data,
BMC Bioinformatics 10:165, 2009.
[abstract, html, pdf] [pdf]
Dai, X., Lähdesmäki, H. and Yli-Harja, O.,
A stratified beta-Gaussian mixture model for clustering genes with multiple data sources,
International Journal on Advances in Life Sciences, Vol. 1, No. 1, pp. 14-25, 2009.
[pdf]
Nykter, M., Lähdesmäki, H., Rust, A. G., Thorsson, V. and Shmulevich, I.,
A data integration framework for prediction of transcription factor targets: a BCL6 case study,
Annals of the New York Academy of Sciences, Vol. 1158, pp. 205-214, 2009.
[abstract, html, pdf] [pdf]
2008
Lähdesmäki, H., Rust, A. G. and Shmulevich, I.,
Probabilistic inference of transcription factor binding from multiple data sources,
PLoS ONE, Vol. 3, No. 3, e1820, 2008.
[pdf, html] [pdf]
[web tool, software]
Lähdesmäki, H. and Shmulevich, I.,
Learning the structure of dynamic Bayesian networks from time series and steady state measurements,
Machine Learning, Vol. 71, No. 2-3, pp. 185-217, 2008.
[abstract, pdf] [pdf]
[software]
Liu, W., Lähdesmäki, H., Dougherty, E. R. and Shmulevich, I.,
Inference of Boolean networks using sensitivity regularization,
EURASIP Journal on Bioinformatics and Systems Biology, Vol. 2008, Article ID 780541, 12 pages, 2008.
[abstract, pdf] [pdf]
2007
Ahdesmäki, M., Lähdesmäki, H., Gracey, A., Shmulevich, I. and Yli-Harja O.,
Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data,
BMC Bioinformatics, 8:233, 2007.
[abstract, html, pdf] [pdf, supplementary material and software]
2006
Lähdesmäki, H., Hautaniemi, S., Shmulevich, I. and Yli-Harja, O.,
Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks,
Signal Processing , Vol. 86, No. 4, pp. 814-834, April 2006.
[pdf] [html, pdf]
2005
Ahdesmäki, M.,* Lähdesmäki, H.,* Pearson, R., Huttunen, H. and Yli-Harja, O.,
Robust detection of periodic time series measured from biological systems,
BMC Bioinformatics, 6:117, 2005.
[abstract, html, pdf] [pdf, supplementary material and software]
(*equally contributing authors)
Lähdesmäki, H., Shmulevich, I., Dunmire, V., Yli-Harja O. and Zhang, W.,
In silico microdissection of microarray data from heterogeneous cell populations,
BMC Bioinformatics, 6:54, 2005.
[abstract, html, pdf] [pdf]
2004
Lähdesmäki, H., Hao, X., Sun, B., Hu, L., Yli-Harja, O., Shmulevich, I. and Zhang, W.,
Distinguishing key biological pathways between primary breast cancers and their lymph node metastases by gene function-based clustering analysis,
International Journal of Oncology, Vol. 24, No. 6, pp. 1589-1596, June 2004.
[pdf]
Hao, X., Sun, B., Hu, L., Lähdesmäki, H., Dunmire, V., Feng, Y., Zhang, S.-W., Wang, H., Wu, C., Wang, H., Fuller, G. N., Symmans, W. F., Shmulevich, I. and Zhang, W.,
Differential gene and protein expression in primary breast malignancies and their lymph node metastases as revealed by combined cDNA microarray and tissue microarray analysis,
Cancer, Vol. 100, No. 6, pp. 1110-1122, 2004.
[pdf] [abstract, html, pdf]
Shmulevich, I. Lähdesmäki, H. and Egiazarian, K.
Spectral methods for testing membership in certain Post classes and the class of forcing functions,
IEEE Signal Processing Letters, Vol. 11, No. 2, pp. 289-292, 2004.
[pdf] [abstract, pdf]
2003
Shmulevich, I., Lähdesmäki, H., Dougherty, E. R., Astola, J. and Zhang, W.,
The role of certain Post classes in Boolean network models of genetic networks,
Proceedings of the National Academy of Sciences of the USA, Vol. 100, No. 19, pp. 10734-10739, 2003.
[pdf] [abstract, html, pdf]
Lähdesmäki, H., Shmulevich, I. and Yli-Harja, O.,
On learning gene regulatory networks under the Boolean network model,
Machine Learning, Vol. 52, No. 1-2, pp. 147-167, June - August 2003.
[pdf] [abstract, pdf]
Lähdesmäki, H., Huttunen, H., Aho, T., Linne, M.-L., Niemi, J., Kesseli, J., Pearson, R. and Yli-Harja, O.,
Estimation and inversion of the effects of cell population asynchrony in gene expression time-series,
Signal Processing, Vol. 83, No. 4, pp. 835-858, April 2003.
[pdf] [abstract, html, pdf]
Book chapters
Larjo A, Shmulevich I and Lähdesmäki H,
Structure learning for Bayesian networks as models of biological networks,
In H. Mamitsuka, C. DeLisi and M. Kanehisa (Eds.), Data Mining for Systems Biology, Methods in Molecular Biology, Volume 939, 2013, pp 35-45, Springer, 2013.
[link]
Lähdesmäki H, Shmulevich I, Yli-Harja O. and Astola J,
Inference of genetic regulatory networks via Best-Fit extensions,
In W. Zhang and I. Shmulevich (Eds.), Computational And Statistical Approaches To Genomics (2nd Ed.), Boston: Kluwer Academic Publishers, pp. 259-278, 2006.
Refereed conference papers
2016
Heinonen M, Mannerström H, Rousu J, Kaski S, Lähdesmäki H,
Non-stationary Gaussian process regression with Hamiltonian Monte Carlo,
In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, to appear.
[abstract, pdf, suppl]
[Software]
2015
Halla-aho V, Mannerström H, Lähdesmäki H,
A probabilistic method for quantifying chromatin interactions,
In Machine Learning in Computational Biology, Montreal, Canada, December 12, 2015.
[pdf]
2013
Larjo A, Lähdesmäki H,
Active learning for Bayesian network models of biological networks using structure priors,
In IEEE International Workshop on Genomic Signal Processing and Statistics, Houston, TX, USA, November 17-19, 2013.
[pdf]
2010
Erkkilä, T., Thorsson, V., Lähdesmäki, H. and Shmulevich, I.,
Inferring genetic regulatory interactions from time-collapsed Boolean summary variables,
In Seventh International Workshop on Computational Systems Biology, WCSB 2010, Luxembourg, June 16-18, 2010.
[pdf]
2009
Laurila, K. and Lähdesmäki, H.,
A probabilistic model for competitive binding of transcription factors,
In Sixth International Workshop on Computational Systems Biology, WCSB 2009, Århus, Denmark, June 10-12, 2009.
[pdf]
Dai, X. and Lähdesmäki, H.,
A unified probabilistic framework for clustering genes from gene expression and protein-protein interaction data,
In Sixth International Workshop on Computational Systems Biology, WCSB 2009, Århus, Denmark, June 10-12, 2009.
[pdf]
2008
Dai, X., Lähdesmäki, H. and Yli-Harja, O.,
sBGMM: a stratified Beta-Gaussian mixture model for clustering genes with multiple data sources,
In International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies, BIOTECHNO 2008, June 29 - July 5, 2008 - Bucharest, Romania.
[pdf]
Laurila, K. and Lähdesmäki, H.,
Effects of disease-related mutations on transcription factor binding,
In Fifth International Workshop on Computational Systems Biology (WCSB08), Leipzig, Germany, June 11-13, 2008.
[pdf]
Larjo, A., Lähdesmäki, H., Facciotti, M., Baliga, N., Yli-Harja, O. and Shmulevich, I.,
Active learning of Bayesian network structure in a realistic setting,
In Fifth International Workshop on Computational Systems Biology (WCSB08), Leipzig, Germany, June 11-13, 2008.
[pdf]
Nikkilä, J., Erkkilä, T. and Lähdesmäki, H.,
Decomposing gene expression into regulatory and differential parts with Bayesian data fusion,
In Fifth International Workshop on Computational Systems Biology (WCSB08), Leipzig, Germany, June 11-13, 2008.
[pdf]
Erkkilä, T., Nykter, M., Lähdesmäki, H., Ahdesmäki, M and Yli-Harja, O.,
Testing for differential expression in simulated and real cDNA microarray data using frequentist and Bayesian methods,
In Fifth International Workshop on Computational Systems Biology (WCSB08), Leipzig, Germany, June 11-13, 2008.
[pdf]
Dai, X., Lähdesmaki, H. and Yli-Harja, O.,
BGMM: a Beta-Gaussian mixture model for clustering genes with multiple data sources,
In Fifth International Workshop on Computational Systems Biology (WCSB08), Leipzig, Germany, June 11-13, 2008.
[pdf]
2007
Lähdesmäki, H. and Shmulevich, I.,
Probabilistic framework for transcription factor binding prediction,
In Fifth IEEE International Workshop on Genomic Signal Processing and Statistics (Gensips'07), Tuusula, FINLAND, June 10-12, 2007.
[pdf]
Ahdesmäki, M., Lähdesmäki, H. and Yli-Harja O.,
Robust Fisher's test for periodicity detection in noisy biological time series,
In Fifth IEEE International Workshop on Genomic Signal Processing and Statistics (Gensips'07), Tuusula, FINLAND, June 10-12, 2007.
[pdf]
2005
Lähdesmäki, H., Yli-Harja, O., Zhang, W. and Shmulevich, I.,
Intrinsic dimensionality in gene expression analysis,
In IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) 2005, Hyatt Regent Hotel, Newport, Rhode Island, May 22 - 24, 2005.
[pdf]
2003
Pearson, R. K., Lähdesmäki, H., Huttunen, H. and Yli-Harja, O.,
Detecting periodicity in nonideal datasets,
In SIAM International Conference on Data Mining (2003), Cathedral Hill Hotel, San Francisco, CA, May 1-3, 2003.
[pdf]
2001
Lähdesmäki, H., Shmulevich, I., Pezzati, L. and Tozzi, A.,
Optimization of edge detectors for topographic maps of cave inscriptions,
In 12th Scandinavian Conference on Image Analysis (SCIA 2001), Bergen, Norway, June 11-14, 2001, pp. 280-287.
[pdf]
Ph.D. theses (written by the research group members)
Valerii Iakovlev,
Deep learning methods for modeling spatiotemporal dynamical systems governed by partial differential equations,
D.Sc. (Tech.) thesis, Aalto University School of Science, 02/2025.
Jani Huuhtanen,
Immunomonitoring in cancer and autoimmune disorders at the single-cell level,
Ph.D. thesis, University of Helsinki, 05/2024.
Maia Malonzo,
Computational analyses of transcriptome and DNA methylation data,
D.Sc. (Tech.) thesis, Aalto University School of Science, April 2024.
Kari Nousiainen,
Computational analysis and modeling of high-throughput data to understand T-helper cell differentiation,
D.Sc. (Tech.) thesis, Aalto University School of Science, January 2023.
Viivi Halla-aho,
Probabilistic modeling of DNA methylation sequencing data,
D.Sc. (Tech.) thesis, Aalto University School of Science, November 2022.
Cagatay Yildiz,
Differential Equations for Machine Learning,
D.Sc. (Tech.) thesis, Aalto University School of Science, February 2022.
[pdf]
Essi Laajala,
Multi-omics Analysis of Early Molecular Mechanisms of Type 1 Diabetes,
Ph.D., University of Turku, December 2021.
[pdf]
Juhi Somani,
Statistical and computational analysis of high-throughput ‘omics’ datasets for understanding the etiology and pathogenesis of autoimmune diseases,
D.Sc. (Tech.) thesis, Aalto University School of Science, August 2021.
[pdf]
Kartiek Kanduri,
Integration of genome-wide datasets to understand regulation of human T-helper cell differentiation,
Ph.D., University of Turku, June 2020.
[pdf]
Rautio S,
Bioinformatic methods to understand gene expression and its regulation,
D.Sc. (Tech.) thesis, Aalto University School of Science, January 2019.
[pdf]
Timo Erkkilä,
Mixture models for probabilistic analysis of genomic data,
D.Sc. (Tech.) thesis, Tampere University of Technology, December 2018.
[pdf]
Vatanen T,
Metagenomic analyses of the human gut microbiome reveal connections to the immune system,
D.Sc. (Tech.) thesis, Aalto University School of Science, March 2017.
[pdf]
Larjo A,
Computational methods for modelling and analysing biological networks,
D.Sc. (Tech.) thesis, Tampere University of Technology, March 2015.
[pdf]
(Co-supervised with O. Yli-Harja from TUT.)
Äijö T,
Computational Methods for Analysis of Dynamic Transcriptome and Its Regulation Through Chromatin Remodeling and Intracellular Signaling,
D.Sc. (Tech.) thesis, Aalto University School of Science, October 2014.
[pdf]
Laurila K,
Computational Approaches for Analyzing Gene Regulatory Processes,
D.Sc. (Tech.) thesis, Tampere University of Technology, August 2011.
[pdf]
(Co-supervised with O. Yli-Harja from TUT. Accepted with distinction. The best PhD thesis in 2011 granted by the Finnish Society for Bioinformatics.)
Lähdesmäki, H.,
Computational Methods for Systems Biology: Analysis of High-Throughput Measurements and Modeling of Genetic Regulatory Networks,
Ph.D. Thesis, Tampere University of Technology, October 2005.
[Table of Contents: pdf][*pdf]
*The doctoral thesis consists of an introduction (94 pages) and nine (9) original publications that are included as an appendix. Please note that the original publications are not included into the pdf-file. Most of the original publications can be found from this web page.
M.Sc. theses (written by the research group members)
Mehmet Balik,
Bayesian basis function approximation for scalable Gaussian process priors in deep generative models,
M.Sc. thesis, Aalto University, 05/2025.
Priscilla Ong,
Latent neural ODE for longitudinal data analysis,
M.Sc. thesis, Aalto University, 01/2025.
Maksim Sinelnikov,
Latent variable model for high-dimensional point process with structured missingness,
M.Sc. thesis, Aalto University, *06/2024.
Otto Lönnroth,
Generative models for analysing longitudinal data,
M.Sc. thesis, Aalto University, 01/2024.
Dimitris Papatheodorou,
Functional Bayesian neural networks with non-stationary GP priors and belief matching,
M.Sc. thesis, Aalto University, 12/2023.
Otto Solatie,
Subject-specific neural ordinary differential equation models,
M.Sc. thesis, Aalto University, 10/2023
Emmi Rehn,
Modeling T-cell receptor repertoires in children at risk of developing type 1 diabetes,
M.Sc. thesis, Aalto University, 06/2023.
Dani Korpela,
A deep learning method for predicting T cell receptor binding to unseen epitopes,
M.Sc. thesis, Aalto University, 12/2022.
Jason Theodoropoulos,
Large-scale analysis of immune receptor repertoires in RNA sequencing data from autoimmune disorders,
M.Sc. thesis, Aalto University, 08/2022,
Alexandru Dumitrescu,
TCR Sequence Representations Using Deep, Contextualized Language Models,
M.Sc. thesis, Aalto University, 03/2021.
[pdf]
Michele Vantini,
Gaussian process modeling of gene expression time series from multi-condition paired experimental design,
M.Sc. thesis, Aalto University, 09/2020.
[pdf]
Saara Hiltunen,
Integrating multi-tube flow cytometry data via deep generative modelling,
M.Sc. thesis, Aalto University, 08/2020.
[pdf]
Valerii Iakovlev,
Learning partial differential equations from data,
M.Sc. thesis, Aalto University, June 2020.
[pdf]
Johanna Vikkula,
Modelling transcriptional velocities and latent dynamics of single cells,
M.Sc. thesis, Aalto University, May 2020.
[pdf]
Qianqian Qin,
Identifying phenotypes based on TCR repertoire using machine learning methods,
M.Sc. thesis, Aalto University, May 2020.
[pdf]
Ranchandran S,
Latent Gaussian processes with composite likelihoods for data-driven disease stratification,
M.Sc. thesis, Aalto University, August 2019.
[link]
Danmei Huang,
Statistical data analysis for biomarker discovery and type 1 diabetes prediction,
M.Sc. thesis, University of Helsinki, December 2018.
[pdf]
Antikainen A,
Modeling protein-DNA binding specificities with random forest,
M.Sc. thesis, Aalto University, January 2018.
[pdf]
Timonen J,
An efficient strategy to infer biochemical networks by means of statistical calibration of mechanistic models,
M.Sc. thesis, Aalto University, November 2017.
[pdf]
Jokinen E,
Modeling protein stability with Gaussian processes,
M.Sc. thesis, Aalto University, August 2016.
[pdf]
Kari M,
A parallel forward selection wrapper for genome wide association studies,
M.Sc. thesis, Aalto University, June 2016.
[pdf]
Chan, Louis Yat Hin
Experimentally-based mathematical modeling to analyze T helper 17 cell differentiation in heterogeneous cell populations,
M.Sc. thesis, University of Helsinki, December 2015.
[pdf]
Halla-aho V,
A probabilistic method for quantifying chromatin interactions,
M.Sc. thesis, Aalto University, November 2015.
[pdf]
Khakipoor B,
Integrated data analysis pipeline for whole human genome transcription factor binding sites prediction,
M.Sc. thesis, Aalto University, June 2015.
[pdf]
Eraslan B,
A probabilistic model for competitive binding of DNA binding proteins using ChIP-seq and MNase-seq data,
M.Sc. thesis, Aalto University, May 2015.
[pdf]
Eraslan G,
A Dirichlet-multinomial mixture model for clustering heterogeneous epigenomics data,
M.Sc. thesis, Aalto University, September 2014.
[pdf]
Kähärä J,
Using DNase I hypersensitivity Data for Transcription Factor Binding Predictions,
M.Sc. thesis, Aalto University, May 2014.
[pdf]
Somani J,
Identifying associations between host genotype and gut microbiota using statistical and computational models,
M.Sc. thesis, Aalto University, October 2013.
[pdf]
Rautio S,
Analyzing time-series RNA-seq data for T helper cell differentiation in mouse and human,
M.Sc. thesis, Aalto University, December 2012.
[pdf]
Malonzo M,
RNA-seq analysis of stem cells for differential gene expression and alternative splicing,
M.Sc. thesis, Aalto University, October 2012.
[pdf]
Prakash K,
A binary combinatorial histone code,
M.Sc. thesis, Aalto University, March 2012.
[pdf]
Laajala E,
Type 1 diabetes biomarkers in human whole blood transcriptome,
M.Sc. thesis, Aalto University, November 2011.
[pdf]
Laurila K,
In silico analysis of point mutation effects on transcription factor binding and protein subcellular localization,
M.Sc. thesis, University of Tampere, April, 2010.
[pdf]
Äijö T,
Learning the structure of an in vitro gene regulatory network using Gaussian processes,
M.Sc. thesis, Tampere University of Technology, July 2009.
[pdf]
(Co-supervised with O. Yli-Harja from TUT.)
B.Sc. theses (written by the research group members)
Sahla R,
Vaihtoehtoisen silmukoinnin tutkiminen RNA-sekvensointimittauksilla,
B.Sc. thesis, Aalto University, May 2012.
[pdf]
Somani J,
Systems biology methods to study naive T helper cell activation at a transcriptome level,
B.Sc. thesis, Aalto University, December 2010.
Äijö T,
Gaussin Prosessit Regressioanalyysissä,
B.Sc. thesis, Tampere University of Technology, May 2008.
Latest update June 13, 2025 by HL