Submitted, preprints (selected)
Scalable mixed-domain Gaussian
process modeling and model reduction for longitudinal data,
Juho Timonen, Harri
Lähdesmäki
[arxiv] [software]
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]
Latent mixed-effect models
for high-dimensional longitudinal data,
Priscilla Ong, Manuel
Haussmann, Otto Lönnroth, Harri Lähdesmäki
[arxiv]
[software]
Modeling randomly observed spatiotemporal dynamical
systems,
Valerii Iakovlev, Harri Lähdesmäki
[arxiv]
[software]
Field-based
molecule generation,
Alexandru Dumitrescu, Dani
Korpela, Markus Heinonen, Yogesh Verma,
Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki
[arxiv]
[software]
DeconV: Probabilistic Cell Type Deconvolution from Bulk
RNA-sequencing Data
Artur Gynter, Dimitri Meistermann, Harri Lähdesmäki, Helena
Kilpinen
[
biorxiv]
[software]
High-dimensional Bayesian optimisation using
conditional variational autoencoders with Gaussian
process priors,
Siddharth Ramchandran, Manuel Haussman, Harri Lähdesmäki,
[arxiv] [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]
[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.
[arxiv] [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]
[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]
[software]
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.]
[arxiv] [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] [pubmed, html,
pdf]
[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.]
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.]
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.
[biorxiv]
[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]
[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,
Neural Information Processing Systems (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,
Neural Information Processing Systems (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)
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)
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 Sep 20, 2024 by HL