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Harri Lähdesmäki
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- affiliation: Aalto University, Espoo, Finland
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2020 – today
- 2024
- [j45]Siddharth Ramchandran
, Gleb Tikhonov
Learning conditional variational autoencoders with missing covariates. Pattern Recognit. 147: 110113 (2024) - [c20]Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki:
Estimating treatment effects from single-arm trials via latent-variable modeling. AISTATS 2024: 2926-2934 - [c19]Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki:
Latent variable model for high-dimensional point process with structured missingness. ICML 2024 - [i22]Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki:
Latent variable model for high-dimensional point process with structured missingness. CoRR abs/2402.05758 (2024) - [i21]Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki:
Field-based Molecule Generation. CoRR abs/2402.15864 (2024) - [i20]Valerii Iakovlev, Harri Lähdesmäki:
Modeling Randomly Observed Spatiotemporal Dynamical Systems. CoRR abs/2406.00368 (2024) - [i19]Priscilla Ong, Manuel Haußmann, Otto Lönnroth, Harri Lähdesmäki:
Latent mixed-effect models for high-dimensional longitudinal data. CoRR abs/2409.11008 (2024) - [i18]Mine Ögretir, Miika Koskinen, Juha Sinisalo, Risto Renkonen, Harri Lähdesmäki:
SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data. CoRR abs/2409.12709 (2024) - 2023
- [j44]Emmi Jokinen
TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs. Bioinform. 39(1) (2023) - [j43]Dani Korpela
EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings. Bioinform. 39(12) (2023) - [j42]Alexandru Dumitrescu
TSignal: a transformer model for signal peptide prediction. Bioinform. 39(Supplement-1): 347-356 (2023) - [j41]Maia H. Malonzo, Harri Lähdesmäki:
LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model. BMC Bioinform. 24(1): 58 (2023) - [c18]Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
Latent Neural ODEs with Sparse Bayesian Multiple Shooting. ICLR 2023 - [c17]Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki:
Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States. NeurIPS 2023 - [i17]Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki:
Learning Space-Time Continuous Neural PDEs from Partially Observed States. CoRR abs/2307.04110 (2023) - [i16]Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki:
Estimating treatment effects from single-arm trials via latent-variable modeling. CoRR abs/2311.03002 (2023) - 2022
- [j40]Maria Osmala
ChromDMM: a Dirichlet-multinomial mixture model for clustering heterogeneous epigenetic data. Bioinform. 38(16): 3863-3870 (2022) - [j39]Maia H. Malonzo
LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis. BMC Bioinform. 23(1): 41 (2022) - [j38]Viivi Halla-aho, Harri Lähdesmäki:
Probabilistic modeling methods for cell-free DNA methylation based cancer classification. BMC Bioinform. 23(1): 119 (2022) - [j37]Anni A. Antikainen
Modeling binding specificities of transcription factor pairs with random forests. BMC Bioinform. 23(1): 212 (2022) - [j36]Michele Vantini
PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies. Comput. Biol. Medicine 143: 105268 (2022) - [c16]Mine Ögretir, Siddharth Ramchandran, Dimitrios Papatheodorou, Harri Lähdesmäki:
A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data. ICMLA 2022: 1522-1529 - [c15]Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen:
Variational multiple shooting for Bayesian ODEs with Gaussian processes. UAI 2022: 790-799 - [i15]Siddharth Ramchandran, Gleb Tikhonov, Otto Lönnroth, Pekka Tiikkainen, Harri Lähdesmäki:
Learning Conditional Variational Autoencoders with Missing Covariates. CoRR abs/2203.01218 (2022) - [i14]Mine Ögretir, Siddharth Ramchandran, Dimitrios Papatheodorou, Harri Lähdesmäki:
A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data. CoRR abs/2204.09369 (2022) - [i13]Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
Latent Neural ODEs with Sparse Bayesian Multiple Shooting. CoRR abs/2210.03466 (2022) - 2021
- [j35]Juho Timonen
lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data. Bioinform. 37(13): 1860-1867 (2021) - [j34]Emmi Jokinen
Predicting recognition between T cell receptors and epitopes with TCRGP. PLoS Comput. Biol. 17(3) (2021) - [c14]Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki:
Latent Gaussian process with composite likelihoods and numerical quadrature. AISTATS 2021: 3718-3726 - [c13]Siddharth Ramchandran, Gleb Tikhonov, Kalle Kujanpää, Miika Koskinen, Harri Lähdesmäki:
Longitudinal Variational Autoencoder. AISTATS 2021: 3898-3906 - [c12]Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki:
Learning continuous-time PDEs from sparse data with graph neural networks. ICLR 2021 - [c11]Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
Continuous-time Model-based Reinforcement Learning. ICML 2021: 12009-12018 - [i12]Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
Continuous-Time Model-Based Reinforcement Learning. CoRR abs/2102.04764 (2021) - [i11]Pashupati Hegde, Çagatay Yildiz, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen:
Bayesian inference of ODEs with Gaussian processes. CoRR abs/2106.10905 (2021) - [i10]Juho Timonen, Harri Lähdesmäki:
Scalable mixed-domain Gaussian processes. CoRR abs/2111.02019 (2021) - 2020
- [j33]Viivi Halla-aho
LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation. Bioinform. 36(17): 4535-4543 (2020) - [j32]Maria Osmala
Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns. BMC Bioinform. 21(1): 317 (2020) - [c10]Viivi Halla-aho
LuxHS: DNA Methylation Analysis with Spatially Varying Correlation Structure. IWBBIO 2020: 505-516 - [i9]Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki:
Learning continuous-time PDEs from sparse data with graph neural networks. CoRR abs/2006.08956 (2020) - [i8]Siddharth Ramchandran, Gleb Tikhonov, Miika Koskinen, Harri Lähdesmäki:
Longitudinal Variational Autoencoder. CoRR abs/2006.09763 (2020) - [i7]Charles W. L. Gadd, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski:
Sample-efficient reinforcement learning using deep Gaussian processes. CoRR abs/2011.01226 (2020)
2010 – 2019
- 2019
- [j31]Markus Heinonen, Maria Osmala
Bayesian metabolic flux analysis reveals intracellular flux couplings. Bioinform. 35(14): i548-i557 (2019) - [j30]Juho Timonen
A Probabilistic Framework for Molecular Network Structure Inference by Means of Mechanistic Modeling. IEEE ACM Trans. Comput. Biol. Bioinform. 16(6): 1843-1854 (2019) - [c9]Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski:
Deep learning with differential Gaussian process flows. AISTATS 2019: 1812-1821 - [c8]Kari Nousiainen
A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation. AlCoB 2019: 191-202 - [c7]Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. NeurIPS 2019: 13412-13421 - [i6]Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki:
ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks. CoRR abs/1905.10994 (2019) - [i5]Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki:
Latent Gaussian process with composite likelihoods for data-driven disease stratification. CoRR abs/1909.01614 (2019) - [i4]Juho Timonen, Henrik Mannerström, Aki Vehtari, Harri Lähdesmäki:
An interpretable probabilistic machine learning method for heterogeneous longitudinal studies. CoRR abs/1912.03549 (2019) - 2018
- [j29]Emmi Jokinen
mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion. Bioinform. 34(13): i274-i283 (2018) - [j28]Kari Nousiainen, Kartiek Kanduri, Isis Ricaño-Ponce
snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions. Bioinform. 34(23): 4112-4114 (2018) - [c6]Markus Heinonen, Çagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki:
Learning unknown ODE models with Gaussian processes. ICML 2018: 1964-1973 - [c5]Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström
Learning stochastic differential equations with Gaussian Processes without Gradient Matching. MLSP 2018: 1-6 - [i3]Markus Heinonen, Maria Osmala, Henrik Mannerström, Janne Wallenius, Samuel Kaski, Juho Rousu, Harri Lähdesmäki:
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings. CoRR abs/1804.06673 (2018) - [i2]Çagatay Yildiz, Markus Heinonen, Jukka Intosalmi, Henrik Mannerström, Harri Lähdesmäki:
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching. CoRR abs/1807.05748 (2018) - [i1]Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski:
Deep learning with differential Gaussian process flows. CoRR abs/1810.04066 (2018) - 2016
- [j27]Jukka Intosalmi, Kari Nousiainen, Helena Ahlfors
Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks. Bioinform. 32(12): 288-296 (2016) - [j26]Tarmo Äijö
LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs. Bioinform. 32(17): 511-519 (2016) - [j25]Yat Hin Chan
A subpopulation model to analyze heterogeneous cell differentiation dynamics. Bioinform. 32(21): 3306-3313 (2016) - [j24]Aalt D. J. van Dijk, Harri Lähdesmäki, Dick de Ridder, Juho Rousu:
Selected proceedings of Machine Learning in Systems Biology: MLSB 2016. BMC Bioinform. 17(S-16): 51-52 (2016) - [c4]Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki:
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. AISTATS 2016: 732-740 - 2015
- [j23]Juhani Kähärä, Harri Lähdesmäki:
BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data. Bioinform. 31(17): 2852-2859 (2015) - [j22]Sini Rautio
MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis. BMC Bioinform. 16: 413:1-413:12 (2015) - [j21]Jukka Intosalmi, Helena Ahlfors
Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data. BMC Syst. Biol. 9: 81 (2015) - [j20]Antti Larjo, Harri Lähdesmäki:
Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning. EURASIP J. Bioinform. Syst. Biol. 2015: 6 (2015) - [j19]Tommi Vatanen
Self-organization and missing values in SOM and GTM. Neurocomputing 147: 60-70 (2015) - 2014
- [j18]Kirsti Laurila, Reija Autio
Integrative genomics and transcriptomics analysis of human embryonic and induced pluripotent stem cells. BioData Min. 7: 32 (2014) - [j17]Tarmo Äijö
Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation. Bioinform. 30(12): 113-120 (2014) - 2013
- [j16]Tarmo Äijö
Sorad: a systems biology approach to predict and modulate dynamic signaling pathway response from phosphoproteome time-course measurements. Bioinform. 29(10): 1283-1291 (2013) - [j15]Juhani Kähärä, Harri Lähdesmäki:
Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data. BMC Bioinform. 14(S-10): S2 (2013) - [c3]Antti Larjo, Harri Lähdesmäki:
Active learning for Bayesian network models of biological networks using structure priors. GENSiPS 2013: 78-81 - 2010
- [j14]Timo Erkkilä, Saara Lehmusvaara
Probabilistic analysis of gene expression measurements from heterogeneous tissues. Bioinform. 26(20): 2571-2577 (2010) - [j13]Xiaofeng Dai, Olli Yli-Harja
Novel Data Fusion Method and Exploration of Multiple Information Sources for Transcription Factor Target Gene Prediction. EURASIP J. Adv. Signal Process. 2010 (2010)
2000 – 2009
- 2009
- [j12]Tarmo Äijö
Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinform. 25(22): 2937-2944 (2009) - [j11]Xiaofeng Dai, Timo Erkkilä, Olli Yli-Harja
A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data. BMC Bioinform. 10 (2009) - [j10]Kirsti Laurila, Harri Lähdesmäki:
Systematic Analysis of Disease-Related Regulatory Mutation Classes Reveals Distinct Effects on Transcription Factor Binding. Silico Biol. 9(4): 209-224 (2009) - 2008
- [j9]Wenbin Liu, Harri Lähdesmäki, Edward R. Dougherty, Ilya Shmulevich:
Inference of Boolean Networks Using Sensitivity Regularization. EURASIP J. Bioinform. Syst. Biol. 2008 (2008) - [j8]Harri Lähdesmäki, Ilya Shmulevich:
Learning the structure of dynamic Bayesian networks from time series and steady state measurements. Mach. Learn. 71(2-3): 185-217 (2008) - [c2]Xiaofeng Dai, Harri Lähdesmäki, Olli Yli-Harja
sBGMM: A Stratified Beta-Gaussian Mixture Model for Clustering Genes with Multiple Data Sources. BIOTECHNO 2008: 94-99 - 2007
- [j7]Miika Ahdesmäki
Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data. BMC Bioinform. 8 (2007) - 2006
- [j6]Harri Lähdesmäki, Sampsa Hautaniemi
Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks. Signal Process. 86(4): 814-834 (2006) - 2005
- [b1]Harri Lähdesmäki:
Computational methods for systems biology: analysis of high-throughput measurements and modeling of genetic regulatory networks. University of Tampere, Finland, 2005 - [j5]Harri Lähdesmäki, Ilya Shmulevich, Valerie Dunmire, Olli Yli-Harja
In silico microdissection of microarray data from heterogeneous cell populations. BMC Bioinform. 6: 54 (2005) - [j4]Miika Ahdesmäki
Robust detection of periodic time series measured from biological systems. BMC Bioinform. 6: 117 (2005) - 2004
- [j3]Ilya Shmulevich, Harri Lähdesmäki, Karen O. Egiazarian:
Spectral methods for testing membership in certain post classes and the class of forcing functions. IEEE Signal Process. Lett. 11(2): 289-292 (2004) - 2003
- [j2]Harri Lähdesmäki, Ilya Shmulevich, Olli Yli-Harja
On Learning Gene Regulatory Networks Under the Boolean Network Model. Mach. Learn. 52(1-2): 147-167 (2003) - [j1]Harri Lähdesmäki, Heikki Huttunen, Tommi Aho, Marja-Leena Linne, Jari Niemi, Juha Kesseli, Ronald K. Pearson, Olli Yli-Harja
Estimation and inversion of the effects of cell population asynchrony in gene expression time-series. Signal Process. 83(4): 835-858 (2003) - [c1]Ronald K. Pearson, Harri Lähdesmäki, Heikki Huttunen, Olli Yli-Harja:
Detecting Periodicity in Nonideal Datasets. SDM 2003: 274-278
Coauthor Index
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