Harri Lähdesmäki

94 papers A* 11A 7C 1Journal 69Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Time-Aware Latent Space Bayesian Optimization. CoRR Tuan A. Vu, Julien Martinelli, Harri Lähdesmäki
2025	A*	`conf`	Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models. ICML Mehmet Yigit Balik, Maksim Sinelnikov, Priscilla Ong, Harri Lähdesmäki
2025	A*	`conf`	E(3)-equivariant models cannot learn chirality: Field-based molecular generation. ICLR Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki
2025	A*	`conf`	High-Dimensional Bayesian Optimisation with Gaussian Process Prior Variational Autoencoders. ICLR Siddharth Ramchandran, Manuel Haussmann, Harri Lähdesmäki
2025	J	`jnl`	Incorporating hierarchical information into multiple instance learning for patient phenotype prediction with single-cell RNA-sequencing data. Bioinform. Chau Do, Harri Lähdesmäki
2025	J	`jnl`	Latent mixed-effect models for high-dimensional longitudinal data. Trans. Mach. Learn. Res. Priscilla Ong, Manuel Haussmann, Otto Lönnroth, Harri Lähdesmäki
2025	A*	`conf`	Learning Spatiotemporal Dynamical Systems from Point Process Observations. ICLR Valerii Iakovlev, Harri Lähdesmäki
2024	A	`conf`	Estimating treatment effects from single-arm trials via latent-variable modeling. AISTATS Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki
2024	J	`jnl`	Field-based Molecule Generation. CoRR Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki
2024	J	`jnl`	Latent mixed-effect models for high-dimensional longitudinal data. CoRR Priscilla Ong, Manuel Haußmann, Otto Lönnroth, Harri Lähdesmäki
2024	A*	`conf`	Latent variable model for high-dimensional point process with structured missingness. ICML Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki
2024	J	`jnl`	Latent variable model for high-dimensional point process with structured missingness. CoRR Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki
2024	J	`jnl`	Learning conditional variational autoencoders with missing covariates. Pattern Recognit. Siddharth Ramchandran, Gleb Tikhonov, Otto Lönnroth, Pekka Tiikkainen, Harri Lähdesmäki
2024	J	`jnl`	Modeling Randomly Observed Spatiotemporal Dynamical Systems. CoRR Valerii Iakovlev, Harri Lähdesmäki
2024	J	`jnl`	SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data. CoRR Mine Ögretir, Miika Koskinen, Juha Sinisalo, Risto Renkonen, Harri Lähdesmäki
2023	J	`jnl`	EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings. Bioinform. Dani Korpela, Emmi Jokinen, Alexandru Dumitrescu, Jani Huuhtanen, Satu Mustjoki, Harri Lähdesmäki
2023	J	`jnl`	Estimating treatment effects from single-arm trials via latent-variable modeling. CoRR Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki
2023	A*	`conf`	Latent Neural ODEs with Sparse Bayesian Multiple Shooting. ICLR Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2023	A*	`conf`	Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States. NeurIPS Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
2023	J	`jnl`	Learning Space-Time Continuous Neural PDEs from Partially Observed States. CoRR Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
2023	J	`jnl`	LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model. BMC Bioinform. Maia H. Malonzo, Harri Lähdesmäki
2023	J	`jnl`	TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs. Bioinform. Emmi Jokinen, Alexandru Dumitrescu, Jani Huuhtanen, Vladimir Gligorijevic, Satu Mustjoki, Richard Bonneau, Markus Heinonen, Harri Lähdesmäki
2023	J	`jnl`	TSignal: a transformer model for signal peptide prediction. Bioinform. Alexandru Dumitrescu, Emmi Jokinen, Anja Paatero, Juho Kellosalo, Ville O. Paavilainen, Harri Lähdesmäki
2022	C	`conf`	A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data. ICMLA Mine Ögretir, Siddharth Ramchandran, Dimitrios Papatheodorou, Harri Lähdesmäki
2022	J	`jnl`	A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data. CoRR Mine Ögretir, Siddharth Ramchandran, Dimitrios Papatheodorou, Harri Lähdesmäki
2022	J	`jnl`	ChromDMM: a Dirichlet-multinomial mixture model for clustering heterogeneous epigenetic data. Bioinform. Maria Osmala, Gökcen Eraslan, Harri Lähdesmäki
2022	J	`jnl`	Latent Neural ODEs with Sparse Bayesian Multiple Shooting. CoRR Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2022	J	`jnl`	Learning Conditional Variational Autoencoders with Missing Covariates. CoRR Siddharth Ramchandran, Gleb Tikhonov, Otto Lönnroth, Pekka Tiikkainen, Harri Lähdesmäki
2022	J	`jnl`	LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis. BMC Bioinform. Maia H. Malonzo, Viivi Halla-aho, Mikko Konki, Riikka J. Lund, Harri Lähdesmäki
2022	J	`jnl`	Modeling binding specificities of transcription factor pairs with random forests. BMC Bioinform. Anni A. Antikainen, Markus Heinonen, Harri Lähdesmäki
2022	J	`jnl`	PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies. Comput. Biol. Medicine Michele Vantini, Henrik Mannerström, Sini Rautio, Helena Ahlfors, Brigitta Stockinger, Harri Lähdesmäki
2022	J	`jnl`	Probabilistic modeling methods for cell-free DNA methylation based cancer classification. BMC Bioinform. Viivi Halla-aho, Harri Lähdesmäki
2022	A	`conf`	Variational multiple shooting for Bayesian ODEs with Gaussian processes. UAI Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen
2021	J	`jnl`	Bayesian inference of ODEs with Gaussian processes. CoRR Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen
2021	J	`jnl`	Continuous-Time Model-Based Reinforcement Learning. CoRR Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2021	A*	`conf`	Continuous-time Model-based Reinforcement Learning. ICML Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2021	A	`conf`	Latent Gaussian process with composite likelihoods and numerical quadrature. AISTATS Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki
2021	A*	`conf`	Learning continuous-time PDEs from sparse data with graph neural networks. ICLR Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
2021	A	`conf`	Longitudinal Variational Autoencoder. AISTATS Siddharth Ramchandran, Gleb Tikhonov, Kalle Kujanpää, Miika Koskinen, Harri Lähdesmäki
2021	J	`jnl`	Predicting recognition between T cell receptors and epitopes with TCRGP. PLoS Comput. Biol. Emmi Jokinen, Jani Huuhtanen, Satu Mustjoki, Markus Heinonen, Harri Lähdesmäki
2021	J	`jnl`	Scalable mixed-domain Gaussian processes. CoRR Juho Timonen, Harri Lähdesmäki
2021	J	`jnl`	lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data. Bioinform. Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki
2020	J	`jnl`	Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns. BMC Bioinform. Maria Osmala, Harri Lähdesmäki
2020	J	`jnl`	Learning continuous-time PDEs from sparse data with graph neural networks. CoRR Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
2020	J	`jnl`	Longitudinal Variational Autoencoder. CoRR Siddharth Ramchandran, Gleb Tikhonov, Miika Koskinen, Harri Lähdesmäki
2020	—	`conf`	LuxHS: DNA Methylation Analysis with Spatially Varying Correlation Structure. IWBBIO Viivi Halla-aho, Harri Lähdesmäki
2020	J	`jnl`	LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation. Bioinform. Viivi Halla-aho, Harri Lähdesmäki
2020	J	`jnl`	Sample-efficient reinforcement learning using deep Gaussian processes. CoRR Charles W. L. Gadd, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
2019	—	`conf`	A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation. AlCoB Kari Nousiainen, Jukka Intosalmi, Harri Lähdesmäki
2019	J	`jnl`	A Probabilistic Framework for Molecular Network Structure Inference by Means of Mechanistic Modeling. IEEE ACM Trans. Comput. Biol. Bioinform. Juho Timonen, Henrik Mannerström, Harri Lähdesmäki, Jukka Intosalmi
2019	J	`jnl`	An interpretable probabilistic machine learning method for heterogeneous longitudinal studies. CoRR Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki
2019	J	`jnl`	Bayesian metabolic flux analysis reveals intracellular flux couplings. Bioinform. Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki
2019	A	`conf`	Deep learning with differential Gaussian process flows. AISTATS Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
2019	J	`jnl`	Latent Gaussian process with composite likelihoods for data-driven disease stratification. CoRR Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki
2019	J	`jnl`	ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks. CoRR Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2019	A*	`conf`	ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. NeurIPS Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
2018	J	`jnl`	Bayesian Metabolic Flux Analysis reveals intracellular flux couplings. CoRR Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki
2018	J	`jnl`	Deep learning with differential Gaussian process flows. CoRR Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski
2018	J	`jnl`	Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching. CoRR Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki
2018	—	`conf`	Learning stochastic differential equations with Gaussian Processes without Gradient Matching. MLSP Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki
2018	A*	`conf`	Learning unknown ODE models with Gaussian processes. ICML Markus Heinonen, Çagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki
2018	J	`jnl`	mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion. Bioinform. Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki
2018	J	`jnl`	snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions. Bioinform. Kari Nousiainen, Kartiek Kanduri, Isis Ricaño-Ponce, Cisca Wijmenga, Riitta Lahesmaa, Vinod Kumar, Harri Lähdesmäki
2016	J	`jnl`	A subpopulation model to analyze heterogeneous cell differentiation dynamics. Bioinform. Yat Hin Chan, Jukka Intosalmi, Sini Rautio, Harri Lähdesmäki
2016	J	`jnl`	Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks. Bioinform. Jukka Intosalmi, Kari Nousiainen, Helena Ahlfors, Harri Lähdesmäki
2016	J	`jnl`	LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs. Bioinform. Tarmo Äijö, Xiaojing Yue, Anjana Rao, Harri Lähdesmäki
2016	A	`conf`	Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. AISTATS Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki
2016	J	`jnl`	Selected proceedings of Machine Learning in Systems Biology: MLSB 2016. BMC Bioinform. Aalt D. J. van Dijk, Harri Lähdesmäki, Dick de Ridder, Juho Rousu
2015	J	`jnl`	Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data. BMC Syst. Biol. Jukka Intosalmi, Helena Ahlfors, Sini Rautio, Henrik Mannerström, Zhi Jane Chen, Riitta Lahesmaa, Brigitta Stockinger, Harri Lähdesmäki
2015	J	`jnl`	BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data. Bioinform. Juhani Kähärä, Harri Lähdesmäki
2015	J	`jnl`	MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis. BMC Bioinform. Sini Rautio, Harri Lähdesmäki
2015	J	`jnl`	Self-organization and missing values in SOM and GTM. Neurocomputing Tommi Vatanen, Maria Osmala, Tapani Raiko, Krista Lagus, Marko Sysi-Aho, Matej Oresic, Timo Honkela, Harri Lähdesmäki
2015	J	`jnl`	Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning. EURASIP J. Bioinform. Syst. Biol. Antti Larjo, Harri Lähdesmäki
2014	J	`jnl`	Integrative genomics and transcriptomics analysis of human embryonic and induced pluripotent stem cells. BioData Min. Kirsti Laurila, Reija Autio, Lingjia Kong, Elisa Närvä, Samer Hussein, Timo Otonkoski, Riitta Lahesmaa, Harri Lähdesmäki
2014	J	`jnl`	Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation. Bioinform. Tarmo Äijö, Vincent Butty, Zhi Jane Chen, Verna Salo, Subhash Tripathi, Christopher B. Burge, Riitta Lahesmaa, Harri Lähdesmäki
2013	—	`conf`	Active learning for Bayesian network models of biological networks using structure priors. GENSiPS Antti Larjo, Harri Lähdesmäki
2013	J	`jnl`	Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data. BMC Bioinform. Juhani Kähärä, Harri Lähdesmäki
2013	J	`jnl`	Sorad: a systems biology approach to predict and modulate dynamic signaling pathway response from phosphoproteome time-course measurements. Bioinform. Tarmo Äijö, Kirsi Granberg, Harri Lähdesmäki
2010	J	`jnl`	Novel Data Fusion Method and Exploration of Multiple Information Sources for Transcription Factor Target Gene Prediction. EURASIP J. Adv. Signal Process. Xiaofeng Dai, Olli Yli-Harja, Harri Lähdesmäki
2010	J	`jnl`	Probabilistic analysis of gene expression measurements from heterogeneous tissues. Bioinform. Timo Erkkilä, Saara Lehmusvaara, Pekka Ruusuvuori, Tapio Visakorpi, Ilya Shmulevich, Harri Lähdesmäki
2009	J	`jnl`	A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data. BMC Bioinform. Xiaofeng Dai, Timo Erkkilä, Olli Yli-Harja, Harri Lähdesmäki
2009	J	`jnl`	Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinform. Tarmo Äijö, Harri Lähdesmäki
2009	J	`jnl`	Systematic Analysis of Disease-Related Regulatory Mutation Classes Reveals Distinct Effects on Transcription Factor Binding. Silico Biol. Kirsti Laurila, Harri Lähdesmäki
2008	J	`jnl`	Inference of Boolean Networks Using Sensitivity Regularization. EURASIP J. Bioinform. Syst. Biol. Wenbin Liu, Harri Lähdesmäki, Edward R. Dougherty, Ilya Shmulevich
2008	J	`jnl`	Learning the structure of dynamic Bayesian networks from time series and steady state measurements. Mach. Learn. Harri Lähdesmäki, Ilya Shmulevich
2008	—	`conf`	sBGMM: A Stratified Beta-Gaussian Mixture Model for Clustering Genes with Multiple Data Sources. BIOTECHNO Xiaofeng Dai, Harri Lähdesmäki, Olli Yli-Harja
2007	J	`jnl`	Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data. BMC Bioinform. Miika Ahdesmäki, Harri Lähdesmäki, Andrew Gracey, Ilya Shmulevich, Olli Yli-Harja
2006	J	`jnl`	Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks. Signal Process. Harri Lähdesmäki, Sampsa Hautaniemi, Ilya Shmulevich, Olli Yli-Harja
2005	—	`—`	Computational methods for systems biology: analysis of high-throughput measurements and modeling of genetic regulatory networks. Harri Lähdesmäki
2005	J	`jnl`	Robust detection of periodic time series measured from biological systems. BMC Bioinform. Miika Ahdesmäki, Harri Lähdesmäki, Ronald K. Pearson, Heikki Huttunen, Olli Yli-Harja
2004	J	`jnl`	Spectral methods for testing membership in certain post classes and the class of forcing functions. IEEE Signal Process. Lett. Ilya Shmulevich, Harri Lähdesmäki, Karen O. Egiazarian
2003	A	`conf`	Detecting Periodicity in Nonideal Datasets. SDM Ronald K. Pearson, Harri Lähdesmäki, Heikki Huttunen, Olli Yli-Harja
2003	J	`jnl`	Estimation and inversion of the effects of cell population asynchrony in gene expression time-series. Signal Process. Harri Lähdesmäki, Heikki Huttunen, Tommi Aho, Marja-Leena Linne, Jari Niemi, Juha Kesseli, Ronald K. Pearson, Olli Yli-Harja
2003	J	`jnl`	On Learning Gene Regulatory Networks Under the Boolean Network Model. Mach. Learn. Harri Lähdesmäki, Ilya Shmulevich, Olli Yli-Harja