Harri Lähdesmäki

Affiliations:
  • Aalto University, Espoo, Finland


According to our database1, Harri Lähdesmäki authored at least 78 papers between 2003 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Learning conditional variational autoencoders with missing covariates.
Pattern Recognit., March, 2024

SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data.
CoRR, 2024

Latent mixed-effect models for high-dimensional longitudinal data.
CoRR, 2024

Modeling Randomly Observed Spatiotemporal Dynamical Systems.
CoRR, 2024

Field-based Molecule Generation.
CoRR, 2024

Latent variable model for high-dimensional point process with structured missingness.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Estimating treatment effects from single-arm trials via latent-variable modeling.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model.
BMC Bioinform., December, 2023

EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings.
Bioinform., December, 2023

TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs.
Bioinform., January, 2023

Learning Space-Time Continuous Neural PDEs from Partially Observed States.
CoRR, 2023

TSignal: a transformer model for signal peptide prediction.
Bioinform., 2023

Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Latent Neural ODEs with Sparse Bayesian Multiple Shooting.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Modeling binding specificities of transcription factor pairs with random forests.
BMC Bioinform., December, 2022

PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies.
Comput. Biol. Medicine, 2022

LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis.
BMC Bioinform., 2022

Probabilistic modeling methods for cell-free DNA methylation based cancer classification.
BMC Bioinform., 2022

ChromDMM: a Dirichlet-multinomial mixture model for clustering heterogeneous epigenetic data.
Bioinform., 2022

Variational multiple shooting for Bayesian ODEs with Gaussian processes.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data.
Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

2021
Predicting recognition between T cell receptors and epitopes with TCRGP.
PLoS Comput. Biol., 2021

Scalable mixed-domain Gaussian processes.
CoRR, 2021

Bayesian inference of ODEs with Gaussian processes.
CoRR, 2021

lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data.
Bioinform., 2021

Continuous-time Model-based Reinforcement Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

Learning continuous-time PDEs from sparse data with graph neural networks.
Proceedings of the 9th International Conference on Learning Representations, 2021

Longitudinal Variational Autoencoder.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Latent Gaussian process with composite likelihoods and numerical quadrature.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Sample-efficient reinforcement learning using deep Gaussian processes.
CoRR, 2020

Longitudinal Variational Autoencoder.
CoRR, 2020

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns.
BMC Bioinform., 2020

LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation.
Bioinform., 2020

LuxHS: DNA Methylation Analysis with Spatially Varying Correlation Structure.
Proceedings of the Bioinformatics and Biomedical Engineering, 2020

2019
A Probabilistic Framework for Molecular Network Structure Inference by Means of Mechanistic Modeling.
IEEE ACM Trans. Comput. Biol. Bioinform., 2019

An interpretable probabilistic machine learning method for heterogeneous longitudinal studies.
CoRR, 2019

Latent Gaussian process with composite likelihoods for data-driven disease stratification.
CoRR, 2019

ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks.
CoRR, 2019

Bayesian metabolic flux analysis reveals intracellular flux couplings.
Bioinform., 2019

ODE2VAE: Deep generative second order ODEs with Bayesian neural networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation.
Proceedings of the Algorithms for Computational Biology - 6th International Conference, 2019

Deep learning with differential Gaussian process flows.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions.
Bioinform., 2018

mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion.
Bioinform., 2018

Learning stochastic differential equations with Gaussian Processes without Gradient Matching.
Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018

Learning unknown ODE models with Gaussian processes.
Proceedings of the 35th International Conference on Machine Learning, 2018

2016
Selected proceedings of Machine Learning in Systems Biology: MLSB 2016.
BMC Bioinform., 2016

Data-driven mechanistic analysis method to reveal dynamically evolving regulatory networks.
Bioinform., 2016

A subpopulation model to analyze heterogeneous cell differentiation dynamics.
Bioinform., 2016

LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs.
Bioinform., 2016

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Self-organization and missing values in SOM and GTM.
Neurocomputing, 2015

Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning.
EURASIP J. Bioinform. Syst. Biol., 2015

Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data.
BMC Syst. Biol., 2015

MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis.
BMC Bioinform., 2015

BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data.
Bioinform., 2015

2014
Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation.
Bioinform., 2014

Integrative genomics and transcriptomics analysis of human embryonic and induced pluripotent stem cells.
BioData Min., 2014

2013
Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data.
BMC Bioinform., 2013

Sorad: a systems biology approach to predict and modulate dynamic signaling pathway response from phosphoproteome time-course measurements.
Bioinform., 2013

Active learning for Bayesian network models of biological networks using structure priors.
Proceedings of the 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, 2013

2010
Novel Data Fusion Method and Exploration of Multiple Information Sources for Transcription Factor Target Gene Prediction.
EURASIP J. Adv. Signal Process., 2010

Probabilistic analysis of gene expression measurements from heterogeneous tissues.
Bioinform., 2010

2009
Systematic Analysis of Disease-Related Regulatory Mutation Classes Reveals Distinct Effects on Transcription Factor Binding.
Silico Biol., 2009

A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data.
BMC Bioinform., 2009

Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics.
Bioinform., 2009

2008
Learning the structure of dynamic Bayesian networks from time series and steady state measurements.
Mach. Learn., 2008

Inference of Boolean Networks Using Sensitivity Regularization.
EURASIP J. Bioinform. Syst. Biol., 2008

sBGMM: A Stratified Beta-Gaussian Mixture Model for Clustering Genes with Multiple Data Sources.
Proceedings of the International Conference on Biocomputation, 2008

2007
Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data.
BMC Bioinform., 2007

2006
Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks.
Signal Process., 2006

2005
Computational methods for systems biology: analysis of high-throughput measurements and modeling of genetic regulatory networks.
PhD thesis, 2005

<i>In silico </i>microdissection of microarray data from heterogeneous cell populations.
BMC Bioinform., 2005

Robust detection of periodic time series measured from biological systems.
BMC Bioinform., 2005

2004
Spectral methods for testing membership in certain post classes and the class of forcing functions.
IEEE Signal Process. Lett., 2004

2003
Estimation and inversion of the effects of cell population asynchrony in gene expression time-series.
Signal Process., 2003

On Learning Gene Regulatory Networks Under the Boolean Network Model.
Mach. Learn., 2003

Detecting Periodicity in Nonideal Datasets.
Proceedings of the Third SIAM International Conference on Data Mining, 2003


  Loading...