- ODERL: Continuous-time model-based reinforcement learning
- L-VAE: Longitudinal variational autoencoder
- HGPLVM: Latent Gaussian process with composite likelihoods and numerical quadrature
- TCRGP: Determining epitope-specificity of T cell receptors with TCRGP
- MPNN-PDE: Learning continuous-time PDEs from sparse data with graph neural networks
- lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data
- LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation
- ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
- DiffGP: Deep learning with differential Gaussian process flows
- SCHiRM: Single cell hierarchical regression model
- BaMFA: Bayesian metabolic flux analysis
- snpEnrichR: An R package to analyze co-localization of SNPs and their proxies in genomic regions
- npODE: Learning unknown ODE (or SDE) models with Gaussian processes
- mGPfusion: Gaussian process based method for predicting stability changes upon single and multiple mutations
- LonGP: an additive Gaussian process regression model for longitudinal data analysis
- Multiscale: A data-driven multiscale modeling framework for spatio-temporal growth dynamics of yeast colonies
- LuxGLM: A probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs
- Subpop: A subpopulation model to analyze heterogeneous cell differentiation dynamics
- LEM: Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks
- Lux: A probabilistic generative model for quantification of DNA modifications
- AdaptiveGP: An implementation of a fully nonstationary, heteroscedastic Gaussian process for Matlab.
- MixChIP: A probabilistic method for cell type specific protein-DNA binding analysis
- BinDNase: A discriminative approach for transcription factor binding prediction using DNase I hypersensitivity data
- DyNB: A probabilistic method to analyze RNA-seq time series data.
- Sorad: A nonparametric probabilistic method to infer and analyze dynamic signaling pathways.
- LIGAP: A probabilistic method to identify condition/lineage specific time-course profiles.
A probabilistic model and MCMC sampler for reconstructing cell type
specific gene expression profiles from measurements of heterogeneous
A web tool
is also available. DSection is also included in an R package called CellMix.
A probabilistic protein-protein interaction guided method for
competitive transcription factor binding prediction.
Transcription factor binding prediction with multiple data fusion. A
Matlab implementation of
our transcription factor binding prediction method that can incorporate
multiple genome-level data sources: ProbTF
software page. We
have also developed a web tool that is publicly available at www.probtf.org.
- GPODE: Learning
the structure of gene regulatory networks using non-parametric
- DBNRJMCMC (*coming soon): Structural learning of dynamic Bayesian
networks. A set
of Matlab functions
that implement our RJMCMC and approximative MCMC DBN structure learning
from time series and/or steady state measurements will be added
shortly. In the meantime, please contact us directly via email.
- Robust periodicity detection:
- A set of Matlab functions
that implement our robust rank-based periodicity detection method can
be downloaded from the supplementary web
page and from here.
- A set of Matlab functions that implement our robust regression-based
periodicity detection method can be downloaded from the supplementary web
- GeneCycle R package at CRAN.
- Toolbox for Boolean and probabilistic Boolean networks. A
comprehensive toolbox to
work with Boolean networks and probabilistic Boolean networks can be
downloaded from PBN web page
maintained by Ilya