Detecting Periodicity in Nonideal Datasets. - Ronald K. Pearson; Harri Lähdesmäki; Heikki Huttunen; Olli Yli-Harja (SDM, 2003)

On Learning Gene Regulatory Networks Under the Boolean Network Model. - Harri Lähdesmäki; Ilya Shmulevich; Olli Yli-Harja (Mach. Learn., 2003)

Estimation and inversion of the effects of cell population asynchrony in gene expression time-series. - Harri Lähdesmäki; Heikki Huttunen; Tommi Aho; Marja-Leena Linne; Jari Niemi; Juha Kesseli; Ronald K. Pearson; Olli Yli-Harja (Signal Process., 2003)

Spectral methods for testing membership in certain post classes and the class of forcing functions. - Ilya Shmulevich; Harri Lähdesmäki; Karen O. Egiazarian (IEEE Signal Process. Lett., 2004)

Robust detection of periodic time series measured from biological systems. - Miika Ahdesmäki; Harri Lähdesmäki; Ronald K. Pearson; Heikki Huttunen; Olli Yli-Harja (BMC Bioinform., 2005)

In silico microdissection of microarray data from heterogeneous cell populations. - Harri Lähdesmäki; Ilya Shmulevich; Valerie Dunmire; Olli Yli-Harja; Wei Zhang 011 (BMC Bioinform., 2005)

Computational methods for systems biology: analysis of high-throughput measurements and modeling of genetic regulatory networks. - Harri Lähdesmäki (2005)

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks. - Harri Lähdesmäki; Sampsa Hautaniemi; Ilya Shmulevich; Olli Yli-Harja (Signal Process., 2006)

Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data. - Miika Ahdesmäki; Harri Lähdesmäki; Andrew Gracey; Ilya Shmulevich; Olli Yli-Harja (BMC Bioinform., 2007)

sBGMM: A Stratified Beta-Gaussian Mixture Model for Clustering Genes with Multiple Data Sources. - Xiaofeng Dai; Harri Lähdesmäki; Olli Yli-Harja (BIOTECHNO, 2008)

Inference of Boolean Networks Using Sensitivity Regularization. - Wenbin Liu; Harri Lähdesmäki; Edward R. Dougherty; Ilya Shmulevich (EURASIP J. Bioinform. Syst. Biol., 2008)

Learning the structure of dynamic Bayesian networks from time series and steady state measurements. - Harri Lähdesmäki; Ilya Shmulevich (Mach. Learn., 2008)

Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. - Tarmo Äijö; Harri Lähdesmäki (Bioinform., 2009)

A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data. - Xiaofeng Dai; Timo Erkkilä; Olli Yli-Harja; Harri Lähdesmäki (BMC Bioinform., 2009)

Systematic Analysis of Disease-Related Regulatory Mutation Classes Reveals Distinct Effects on Transcription Factor Binding. - Kirsti Laurila; Harri Lähdesmäki (Silico Biol., 2009)

Probabilistic analysis of gene expression measurements from heterogeneous tissues. - Timo Erkkilä; Saara Lehmusvaara; Pekka Ruusuvuori; Tapio Visakorpi; Ilya Shmulevich; Harri Lähdesmäki (Bioinform., 2010)

Novel Data Fusion Method and Exploration of Multiple Information Sources for Transcription Factor Target Gene Prediction. - Xiaofeng Dai; Olli Yli-Harja; Harri Lähdesmäki (EURASIP J. Adv. Signal Process., 2010)

Active learning for Bayesian network models of biological networks using structure priors. - Antti Larjo; Harri Lähdesmäki (GENSiPS, 2013)

Sorad: a systems biology approach to predict and modulate dynamic signaling pathway response from phosphoproteome time-course measurements. - Tarmo Äijö; Kirsi Granberg; Harri Lähdesmäki (Bioinform., 2013)

Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data. - Juhani Kähärä; Harri Lähdesmäki (BMC Bioinform., 2013)

Integrative genomics and transcriptomics analysis of human embryonic and induced pluripotent stem cells. - Kirsti Laurila; Reija Autio; Lingjia Kong; Elisa Närvä; Samer Hussein; Timo Otonkoski; Riitta Lahesmaa; Harri Lähdesmäki (BioData Min., 2014)

Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation. - Tarmo Äijö; Vincent Butty; Zhi Jane Chen; Verna Salo; Subhash Tripathi; Christopher B. Burge; Riitta Lahesmaa; Harri Lähdesmäki (Bioinform., 2014)

BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data. - Juhani Kähärä; Harri Lähdesmäki (Bioinform., 2015)

MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis. - Sini Rautio; Harri Lähdesmäki (BMC Bioinform., 2015)

Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data. - Jukka Intosalmi; Helena Ahlfors; Sini Rautio; Henrik Mannerström; Zhi Jane Chen; Riitta Lahesmaa; Brigitta Stockinger; Harri Lähdesmäki (BMC Syst. Biol., 2015)

Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning. - Antti Larjo; Harri Lähdesmäki (EURASIP J. Bioinform. Syst. Biol., 2015)

Self-organization and missing values in SOM and GTM. - Tommi Vatanen; Maria Osmala; Tapani Raiko; Krista Lagus; Marko Sysi-Aho; Matej Oresic; Timo Honkela; Harri Lähdesmäki (Neurocomputing, 2015)

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. - Markus Heinonen; Henrik Mannerström; Juho Rousu; Samuel Kaski; Harri Lähdesmäki (AISTATS, 2016)

LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs. - Tarmo Äijö; Xiaojing Yue; Anjana Rao; Harri Lähdesmäki (Bioinform., 2016)

A subpopulation model to analyze heterogeneous cell differentiation dynamics. - Yat Hin Chan; Jukka Intosalmi; Sini Rautio; Harri Lähdesmäki (Bioinform., 2016)

Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks. - Jukka Intosalmi; Kari Nousiainen; Helena Ahlfors; Harri Lähdesmäki (Bioinform., 2016)

Selected proceedings of Machine Learning in Systems Biology: MLSB 2016. - Aalt D. J. van Dijk; Harri Lähdesmäki; Dick de Ridder; Juho Rousu (BMC Bioinform., 2016)

Learning unknown ODE models with Gaussian processes. - Markus Heinonen; Çagatay Yildiz; Henrik Mannerström; Jukka Intosalmi; Harri Lähdesmäki (ICML, 2018)

Learning stochastic differential equations with Gaussian Processes without Gradient Matching. - Çagatay Yildiz; Markus Heinonen; Jukka Intosalmi; Henrik Mannerström; Harri Lähdesmäki (MLSP, 2018)

mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion. - Emmi Jokinen; Markus Heinonen; Harri Lähdesmäki (Bioinform., 2018)

snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions. - Kari Nousiainen; Kartiek Kanduri; Isis Ricaño-Ponce; Cisca Wijmenga; Riitta Lahesmaa; Vinod Kumar 004; Harri Lähdesmäki (Bioinform., 2018)

Deep learning with differential Gaussian process flows. - Pashupati Hegde; Markus Heinonen; Harri Lähdesmäki; Samuel Kaski (AISTATS, 2019)

A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation. - Kari Nousiainen; Jukka Intosalmi; Harri Lähdesmäki (AlCoB, 2019)

ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. - Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (NeurIPS, 2019)

Bayesian metabolic flux analysis reveals intracellular flux couplings. - Markus Heinonen; Maria Osmala; Henrik Mannerström; Janne Wallenius; Samuel Kaski; Juho Rousu; Harri Lähdesmäki (Bioinform., 2019)

ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks. - Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (CoRR, 2019)

Latent Gaussian process with composite likelihoods for data-driven disease stratification. - Siddharth Ramchandran; Miika Koskinen; Harri Lähdesmäki (CoRR, 2019)

An interpretable probabilistic machine learning method for heterogeneous longitudinal studies. - Juho Timonen; Henrik Mannerström; Aki Vehtari; Harri Lähdesmäki (CoRR, 2019)

A Probabilistic Framework for Molecular Network Structure Inference by Means of Mechanistic Modeling. - Juho Timonen; Henrik Mannerström; Harri Lähdesmäki; Jukka Intosalmi (IEEE ACM Trans. Comput. Biol. Bioinform., 2019)

LuxHS: DNA Methylation Analysis with Spatially Varying Correlation Structure. - Viivi Halla-aho; Harri Lähdesmäki (IWBBIO, 2020)

LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation. - Viivi Halla-aho; Harri Lähdesmäki (Bioinform., 2020)

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns. - Maria Osmala; Harri Lähdesmäki (BMC Bioinform., 2020)

Longitudinal Variational Autoencoder. - Siddharth Ramchandran; Gleb Tikhonov; Miika Koskinen; Harri Lähdesmäki (CoRR, 2020)

Sample-efficient reinforcement learning using deep Gaussian processes. - Charles W. L. Gadd; Markus Heinonen; Harri Lähdesmäki; Samuel Kaski (CoRR, 2020)

Latent Gaussian process with composite likelihoods and numerical quadrature. - Siddharth Ramchandran; Miika Koskinen; Harri Lähdesmäki (AISTATS, 2021)

Longitudinal Variational Autoencoder. - Siddharth Ramchandran; Gleb Tikhonov; Kalle Kujanpää; Miika Koskinen; Harri Lähdesmäki (AISTATS, 2021)

Learning continuous-time PDEs from sparse data with graph neural networks. - Valerii Iakovlev; Markus Heinonen; Harri Lähdesmäki (ICLR, 2021)

Continuous-time Model-based Reinforcement Learning. - Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (ICML, 2021)

lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data. - Juho Timonen; Henrik Mannerström; Aki Vehtari; Harri Lähdesmäki (Bioinform., 2021)

Bayesian inference of ODEs with Gaussian processes. - Pashupati Hegde; Çagatay Yildiz; Harri Lähdesmäki; Samuel Kaski; Markus Heinonen (CoRR, 2021)

Scalable mixed-domain Gaussian processes. - Juho Timonen; Harri Lähdesmäki (CoRR, 2021)

Predicting recognition between T cell receptors and epitopes with TCRGP. - Emmi Jokinen; Jani Huuhtanen; Satu Mustjoki; Markus Heinonen; Harri Lähdesmäki (PLoS Comput. Biol., 2021)

A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data. - Mine Ögretir; Siddharth Ramchandran; Dimitrios Papatheodorou; Harri Lähdesmäki (ICMLA, 2022)

Variational multiple shooting for Bayesian ODEs with Gaussian processes. - Pashupati Hegde; Çagatay Yildiz; Harri Lähdesmäki; Samuel Kaski; Markus Heinonen (UAI, 2022)

ChromDMM: a Dirichlet-multinomial mixture model for clustering heterogeneous epigenetic data. - Maria Osmala; Gökcen Eraslan; Harri Lähdesmäki (Bioinform., 2022)

Probabilistic modeling methods for cell-free DNA methylation based cancer classification. - Viivi Halla-aho; Harri Lähdesmäki (BMC Bioinform., 2022)

LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis. - Maia H. Malonzo; Viivi Halla-aho; Mikko Konki; Riikka J. Lund; Harri Lähdesmäki (BMC Bioinform., 2022)

PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies. - Michele Vantini; Henrik Mannerström; Sini Rautio; Helena Ahlfors; Brigitta Stockinger; Harri Lähdesmäki (Comput. Biol. Medicine, 2022)

Modeling binding specificities of transcription factor pairs with random forests. - Anni A. Antikainen; Markus Heinonen; Harri Lähdesmäki (BMC Bioinform., 2022)

Latent Neural ODEs with Sparse Bayesian Multiple Shooting. - Valerii Iakovlev; Çagatay Yildiz; Markus Heinonen; Harri Lähdesmäki (ICLR, 2023)

Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States. - Valerii Iakovlev; Markus Heinonen; Harri Lähdesmäki (NeurIPS, 2023)

TSignal: a transformer model for signal peptide prediction. - Alexandru Dumitrescu; Emmi Jokinen; Anja Paatero; Juho Kellosalo; Ville O. Paavilainen; Harri Lähdesmäki (Bioinform., 2023)

Learning Space-Time Continuous Neural PDEs from Partially Observed States. - Valerii Iakovlev; Markus Heinonen; Harri Lähdesmäki (CoRR, 2023)

TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs. - Emmi Jokinen; Alexandru Dumitrescu; Jani Huuhtanen; Vladimir Gligorijevic; Satu Mustjoki; Richard Bonneau; Markus Heinonen; Harri Lähdesmäki (Bioinform., 2023)

EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings. - Dani Korpela; Emmi Jokinen; Alexandru Dumitrescu; Jani Huuhtanen; Satu Mustjoki; Harri Lähdesmäki (Bioinform., 2023)

LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model. - Maia H. Malonzo; Harri Lähdesmäki (BMC Bioinform., 2023)

Estimating treatment effects from single-arm trials via latent-variable modeling. - Manuel Haussmann; Tran Minh Son Le; Viivi Halla-aho; Samu Kurki; Jussi Leinonen; Miika Koskinen; Samuel Kaski; Harri Lähdesmäki (AISTATS, 2024)

Latent variable model for high-dimensional point process with structured missingness. - Maksim Sinelnikov; Manuel Haussmann; Harri Lähdesmäki (ICML, 2024)

Field-based Molecule Generation. - Alexandru Dumitrescu; Dani Korpela; Markus Heinonen; Yogesh Verma; Valerii Iakovlev; Vikas Garg 001; Harri Lähdesmäki (CoRR, 2024)

Modeling Randomly Observed Spatiotemporal Dynamical Systems. - Valerii Iakovlev; Harri Lähdesmäki (CoRR, 2024)

SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data. - Mine Ögretir; Miika Koskinen; Juha Sinisalo; Risto Renkonen; Harri Lähdesmäki (CoRR, 2024)

Learning conditional variational autoencoders with missing covariates. - Siddharth Ramchandran; Gleb Tikhonov; Otto Lönnroth; Pekka Tiikkainen; Harri Lähdesmäki (Pattern Recognit., 2024)

E(3)-equivariant models cannot learn chirality: Field-based molecular generation. - Alexandru Dumitrescu; Dani Korpela; Markus Heinonen; Yogesh Verma; Valerii Iakovlev; Vikas Garg 001; Harri Lähdesmäki (ICLR, 2025)

Learning Spatiotemporal Dynamical Systems from Point Process Observations. - Valerii Iakovlev; Harri Lähdesmäki (ICLR, 2025)

High-Dimensional Bayesian Optimisation with Gaussian Process Prior Variational Autoencoders. - Siddharth Ramchandran; Manuel Haussmann; Harri Lähdesmäki (ICLR, 2025)

Latent mixed-effect models for high-dimensional longitudinal data. - Priscilla Ong; Manuel Haussmann; Otto Lönnroth; Harri Lähdesmäki (Trans. Mach. Learn. Res., 2025)