Harri Lähdesmäki

发表

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

Nicholas S. Flann, Olli Yli-Harja, Alexander Skupin, 2018, bioRxiv.

Computational methods for systems biology: analysis of high-throughput measurements and modeling of genetic regulatory networks

Harri Lähdesmäki, H. Lähdesmäki, 2005 .

On Learning Gene Regulatory Networks Under the Boolean Network Model

Ilya Shmulevich, Olli Yli-Harja, Harri Lähdesmäki, 2003, Machine Learning.

Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning

Harri Lähdesmäki, Antti Larjo, H. Lähdesmäki, 2015, EURASIP J. Bioinform. Syst. Biol..

Integrative genomics and transcriptomics analysis of human embryonic and induced pluripotent stem cells

Riitta Lahesmaa, Lingjia Kong, Harri Lähdesmäki, 2014, BioData Mining.

Inference of Boolean Networks Using Sensitivity Regularization

Edward R. Dougherty, Wenbin Liu, Ilya Shmulevich, 2008, EURASIP J. Bioinform. Syst. Biol..

Selected proceedings of Machine Learning in Systems Biology: MLSB 2016

Juho Rousu, Aalt D. J. van Dijk, Dick de Ridder, 2016, BMC Bioinformatics.

Efficient learning of signaling network model structures using Bayesian networks

Harri Lähdesmäki, Antti Larjo, H. Lähdesmäki, 2010 .

Inferring genetic regulatory interactions from time-collapsed Boolean summary variables

Harri Lähdesmäki, Ilya Shmulevich, Timo Erkkilä, 2010 .

The role of certain Post classes in Boolean network models of genetic networks

Jaakko Astola, Ilya Shmulevich, Edward R Dougherty, 2003, Proceedings of the National Academy of Sciences of the United States of America.

sBGMM: A Stratified Beta-Gaussian Mixture Model for Clustering Genes with Multiple Data Sources

Olli Yli-Harja, Harri Lähdesmäki, Xiaofeng Dai, 2008, 2008 International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies.

Systematic Analysis of Disease-Related Regulatory Mutation Classes Reveals Distinct Effects on Transcription Factor Binding

Harri Lähdesmäki, Kirsti Laurila, H. Lähdesmäki, 2009, Silico Biol..

A subpopulation model to analyze heterogeneous cell differentiation dynamics

Jukka Intosalmi, Harri Lähdesmäki, Sini Rautio, 2016, Bioinform..

LEARNING STOCHASTIC DIFFERENTIAL EQUATIONS WITH GAUSSIAN PROCESSES WITHOUT GRADIENT MATCHING

Markus Heinonen, Henrik Mannerström, Jukka Intosalmi, 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

Deep learning with differential Gaussian process flows pdf

Samuel Kaski, Markus Heinonen, Harri Lähdesmäki, 2018, AISTATS.

A unified probabilistic framework for clustering genes from gene expression and protein-protein interaction data

Harri Lähdesmäki, Xiaofeng Dai, H. Lähdesmäki, 2009 .

Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data

Riitta Lahesmaa, Henrik Mannerström, Jukka Intosalmi, 2015, BMC Systems Biology.

Determining epitope specificity of T cell receptors with TCRGP

Markus Heinonen, Harri Lähdesmäki, Emmi Jokinen, 2019, bioRxiv.

Estimation and inversion of the effects of cell population asynchrony in gene expression time-series

Heikki Huttunen, Ronald K. Pearson, Olli Yli-Harja, 2003, Signal Process..

Probabilistic analysis of gene expression measurements from heterogeneous tissues

Pekka Ruusuvuori, Ilya Shmulevich, Harri Lähdesmäki, 2010, Bioinform..

mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion

Markus Heinonen, Harri Lähdesmäki, Emmi Jokinen, 2018, Bioinform..

Generative Models for Quantification of DNA Modifications.

Richard Bonneau, Harri Lähdesmäki, Tarmo Äijö, 2018, Methods in molecular biology.

Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation

Christopher B. Burge, Riitta Lahesmaa, Harri Lähdesmäki, 2014, Bioinform..

In silico microdissection of microarray data from heterogeneous cell populations

Ilya Shmulevich, Olli Yli-Harja, Wei Zhang, 2005, BMC Bioinformatics.

Spectral methods for testing membership in certain post classes and the class of forcing functions

Karen O. Egiazarian, Ilya Shmulevich, Harri Lähdesmäki, 2004, IEEE Signal Processing Letters.

Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data

Harri Lähdesmäki, Juhani Kähärä, H. Lähdesmäki, 2013, BMC Bioinformatics.

Inference of Genetic Regulatory Networks via Best-Fit Extensions

Jaakko Astola, Ilya Shmulevich, Olli Yli-Harja, 2003 .

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

Juho Rousu, Samuel Kaski, Markus Heinonen, 2015, AISTATS.

Learning the structure of dynamic Bayesian networks from time series and steady state measurements

Ilya Shmulevich, Harri Lähdesmäki, H. Lähdesmäki, 2008, Machine Learning.

Computational Methods for Systems Biology

Harri Lähdesmäki, H. Lähdesmäki, 2005 .

Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics

Harri Lähdesmäki, Tarmo Äijö, H. Lähdesmäki, 2009, Bioinform..

A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation

Jukka Intosalmi, Harri Lähdesmäki, Kari Nousiainen, 2019, AlCoB.

A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data

Olli Yli-Harja, Harri Lähdesmäki, Xiaofeng Dai, 2009, BMC Bioinformatics.

MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis

Harri Lähdesmäki, Sini Rautio, H. Lähdesmäki, 2015, BMC Bioinformatics.

SCHiRM: Single Cell Hierarchical Regression Model to detect dependencies in read count data

Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki, 2018 .

Comparing multiple time-course differentiation profiles of human T helper cells using a Bayesian approach

Riitta Lahesmaa, Harri Lähdesmäki, Tarmo Äijö, 2010 .

Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks

Jukka Intosalmi, Harri Lähdesmäki, Helena Ahlfors, 2016, Bioinform..

LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs

Harri Lähdesmäki, Tarmo Äijö, Anjana Rao, 2016, Bioinform..

Structure learning for Bayesian networks as models of biological networks.

Ilya Shmulevich, Harri Lähdesmäki, Antti Larjo, 2013, Methods in molecular biology.

Bayesian metabolic flux analysis reveals intracellular flux couplings

Juho Rousu, Samuel Kaski, Janne Wallenius, 2018, Bioinform..

Sorad: a systems biology approach to predict and modulate dynamic signaling pathway response from phosphoproteome time-course measurements

Harri Lähdesmäki, Tarmo Äijö, Kirsi Granberg, 2013, Bioinform..

Active learning for Bayesian network models of biological networks using structure priors

Harri Lähdesmäki, Antti Larjo, H. Lähdesmäki, 2013, 2013 IEEE International Workshop on Genomic Signal Processing and Statistics.

An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

Aki Vehtari, Tommi Vatanen, Riitta Lahesmaa, 2019, Nature Communications.

LonGP: an additive Gaussian process regression model for longitudinal study designs

Aki Vehtari, Tommi Vatanen, Riitta Lahesmaa, 2018, bioRxiv.

BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data

Harri Lähdesmäki, Juhani Kähärä, H. Lähdesmäki, 2015, Bioinform..

Learning unknown ODE models with Gaussian processes

Markus Heinonen, Henrik Mannerström, Jukka Intosalmi, 2018, ICML.

ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks pdf

Markus Heinonen, Harri Lähdesmäki, Çagatay Yildiz, 2019, NeurIPS.

Continuous-Time Model-Based Reinforcement Learning

Markus Heinonen, Harri Lähdesmäki, Çagatay Yildiz, 2021, ICML.

Scalable mixed-domain Gaussian processes pdf

Harri Lähdesmäki, Juho Timonen, 2021, ArXiv.

LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis

Harri Lähdesmäki, Viivi Halla-aho, Maia H. Malonzo, 2022, BMC Bioinform..

Bayesian inference of ODEs with Gaussian processes pdf

Samuel Kaski, Markus Heinonen, Harri Lähdesmäki, 2021, ArXiv.

Longitudinal Variational Autoencoder

Harri Lähdesmäki, Gleb Tikhonov, Siddharth Ramchandran, 2020, AISTATS.

A protein–protein interaction guided method for competitive transcription factor binding improves target predictions

Olli Yli-Harja, Harri Lähdesmäki, Kirsti Laurila, 2009, Nucleic acids research.

Differential gene and protein expression in primary breast malignancies and their lymph node metastases as revealed by combined cDNA microarray and tissue microarray analysis

Chunlei Wu, Ilya Shmulevich, Harri Lähdesmäki, 2004, Cancer.

Control of Foxp3 stability through modulation of TET activity

Harri Lähdesmäki, Tarmo Äijö, Pandurangan Vijayanand, 2016, The Journal of experimental medicine.

Dissecting the dynamic changes of 5-hydroxymethylcytosine in T-cell development and differentiation

Harri Lähdesmäki, Tarmo Äijö, Yun Huang, 2014, Proceedings of the National Academy of Sciences.

The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes.

Tommi Vatanen, Harri Lähdesmäki, Jorma Ilonen, 2015, Cell host & microbe.

An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation

Riitta Lahesmaa, Harri Lähdesmäki, Antti Larjo, 2012, BMC Genomics.

Genome-wide profiling of interleukin-4 and STAT6 transcription factor regulation of human Th2 cell programming.

Matej Oresic, Tero Aittokallio, Riitta Lahesmaa, 2010, Immunity.

Reconstruction and Validation of RefRec: A Global Model for the Yeast Molecular Interaction Network

Pekka Ruusuvuori, Olli Yli-Harja, Christophe Roos, 2010, PloS one.

A probabilistic method for quantifying chromatin interactions

Viivi Halla-Aho, Henrik Mannerström, Harri Lähdesmäki, 2015, NIPS 2015.

Synthetic Transcription Amplifier System for Orthogonal Control of Gene Expression in Saccharomyces cerevisiae

Juho Rousu, Merja Penttilä, Harri Lähdesmäki, 2016, PloS one.

Innate Immune Activity Is Detected Prior to Seroconversion in Children With HLA-Conferred Type 1 Diabetes Susceptibility

Olli Simell, Riitta Lahesmaa, Harri Lähdesmäki, 2014, Diabetes.

Genome-wide Analysis of STAT3-Mediated Transcription during Early Human Th17 Cell Differentiation.

Riitta Lahesmaa, Harri Lähdesmäki, Cisca Wijmenga, 2017, Cell reports.

TET proteins regulate the lineage specification and TCR-mediated expansion of iNKT cells

Harri Lähdesmäki, Sini Rautio, Lukas Chavez, 2016, Nature Immunology.

LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis

Harri Lähdesmäki, Viivi Halla-aho, Maia H. Malonzo, 2018, bioRxiv.

Modulation of TET2 expression and 5-methylcytosine oxidation by the CXXC domain protein IDAX

L. Aravind, Harri Lähdesmäki, Patrick G. Hogan, 2013, Nature.

Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans

Tommi Vatanen, Raivo Kolde, Harri Lähdesmäki, 2016, Cell.

A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways

Henrik Mannerström, Harri Lähdesmäki, Tarmo Äijö, 2016, Genome Biology.

Distinguishing key biological pathways between primary breast cancers and their lymph node metastases by gene function-based clustering analysis.

Wei Zhang, Ilya Shmulevich, Olli Yli-Harja, 2004, International journal of oncology.

Transcriptional Repressor HIC1 Contributes to Suppressive Function of Human Induced Regulatory T Cells

Riitta Lahesmaa, Laura L. Elo, Harri Lähdesmäki, 2018, Cell reports.

Serum Proteomes Distinguish Children Developing Type 1 Diabetes in a Cohort With HLA-Conferred Susceptibility

Olli Simell, Riitta Lahesmaa, Harri Lähdesmäki, 2015, Diabetes.

Transcriptional Repressor HIC 1 Contributes to Suppressive Function of Human Induced Regulatory T Cells

Riitta Lahesmaa, Laura L. Elo, Ubaid Ullah, 2018 .

Identification of early gene expression changes during human Th17 cell differentiation.

Riitta Lahesmaa, Harri Lähdesmäki, Brigitta Stockinger, 2012, Blood.

Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood

Aki Vehtari, Riitta Lahesmaa, Harri Lähdesmäki, 2018, Scientific Reports.

Continuous Hypoxic Culturing of Human Embryonic Stem Cells Enhances SSEA-3 and MYC Levels

Riitta Lahesmaa, Harri Lähdesmäki, Asta Laiho, 2013, PloS one.