P. Marttinen

发表

Single-Cell Genomics Reveals Hundreds of Coexisting Subpopulations in Wild Prochlorococcus

N. Kashtan, R. Stocker, Huiming Ding, 2014, Science.

Errors-in-Variables Modeling of Personalized Treatment-Response Trajectories

Pekka Marttinen, Guangyi Zhang, Reza Ashrafi, 2019, IEEE Journal of Biomedical and Health Informatics.

Informative Bayesian Neural Network Priors for Weak Signals

A. Havulinna, P. Marttinen, S. Kaski, 2021, Bayesian Analysis.

Learning Pairwise Interactions with Bayesian Neural Networks

Samuel Kaski, P. Marttinen, Tianyu Cui, 2019 .

Robust universal scoring function for threshold free gene set analysis : Supplementary text

L. Holm, P. Törönen, P. Marttinen, 2009 .

Comprehensive Identification of Single Nucleotide Polymorphisms Associated with Beta-lactam Resistance within Pneumococcal Mosaic Genes

W. Hanage, J. Parkhill, P. Marttinen, 2014, PLoS genetics.

Reconstructing population histories from single nucleotide polymorphism data.

J. Corander, P. Marttinen, J. Sirén, 2011, Molecular biology and evolution.

Detection of recombination events in bacterial genomes from large population samples

J. Corander, W. Hanage, P. Marttinen, 2011, Nucleic acids research.

Efficient inference of recent and ancestral recombination within bacterial populations

Cheryl P. Andam, J. Corander, W. Hanage, 2017, bioRxiv.

Parallel Gaussian process surrogate method to accelerate likelihood-free inference pdf

Aki Vehtari, Michael U. Gutmann, Pekka Marttinen, 2019, ArXiv.

Recombination produces coherent bacterial species clusters in both core and accessory genomes

M. Gutmann, J. Corander, W. Hanage, 2015, Microbial genomics.

Bayesian identification of admixture events using multilocus molecular markers

J. Corander, P. Marttinen, 2006, Molecular ecology.

Dense genomic sampling identifies highways of pneumococcal recombination

Jukka Corander, David Goldblatt, Paul Turner, 2014, Nature Genetics.

Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes

Jukka Corander, Antti Honkela, Niko Välimäki, 2016, Nature Communications.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Matti Pirinen, Jukka Corander, Krister Wennerberg, 2016, Nature Communications.

Modelling G×E with historical weather information improves genomic prediction in new environments

Samuel Kaski, Hiroshi Mamitsuka, P. Marttinen, 2017, bioRxiv.

The impact of host metapopulation structure on the population genetics of colonizing bacteria

M. Gutmann, J. Corander, W. Hanage, 2015, bioRxiv.

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

Aki Vehtari, Pekka Marttinen, Marko Järvenpää, 2019, UAI.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

Matti Pirinen, Juho Rousu, Anna Cichonska, 2015, bioRxiv.

Computational modelling of self-reported dietary carbohydrate intake on glucose concentrations in patients undergoing Roux-en-Y gastric bypass versus one-anastomosis gastric bypass

P. Marttinen, K. Pietiläinen, Reza A. Ashrafi, 2021, Annals of medicine.

Multiple Output Regression with Latent Noise

Matti Pirinen, Samuel Kaski, Jussi Gillberg, 2014, J. Mach. Learn. Res..

Multiple Output Regression with Latent Noise Gillberg , Jussi 2016-0101

M. Pirinen, P. Marttinen, J. Gillberg, 2022 .

Introduction Jon Williamson Draft of August 5 , 2005 Combining Probability and Logic

J. Corander, P. Marttinen, Jon Williamson, 2005 .

Gaussian process modelling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria

Aki Vehtari, Marko Jarvenpaa, Pekka Marttinen, 2016, The Annals of Applied Statistics.

Sequence element enrichment analysis to determine the 1 genetic basis of bacterial phenotypes 2 3

J. Corander, A. Honkela, J. Parkhill, 2016 .

Genomes of a major nosocomial pathogen Enterococcus faecium are shaped by adaptive evolution of the chromosome and plasmidome

Samuel Kaski, J. Corander, P. Marttinen, 2019, bioRxiv.

Population analysis of Legionella pneumophila reveals a basis for resistance to complement-mediated killing

J. Corander, P. Marttinen, Bryan A. Wee, 2020, Nature Communications.

Look beyond labels: Incorporating functional summary information in Bayesian neural networks

P. Marttinen, Markus Heinonen, Tianyu Cui, 2022, ArXiv.

International genomic definition of pneumococcal lineages, to contextualise disease, antibiotic resistance and vaccine impact

Jukka Corander, Andrew J. Page, Pekka Marttinen, 2019, EBioMedicine.

Modelling methicillin-resistant Staphylococcus aureus decolonization: interactions between body sites and the impact of site-specific clearance

P. Marttinen, Y. Grad, L. Miller, 2022, Journal of the Royal Society Interface.

Analysis of recent and ancestral recombination reveals high-resolution population structure in Streptococcus pneumoniae

Cheryl P. Andam, J. Corander, W. Hanage, 2016 .

Multiple output regression with latent noise. Journal of Machine Learning

M. Pirinen, K. Borgwardt, Samuel Kaski, 2016 .

Joint Non-parametric Point Process model for Treatments and Outcomes: Counterfactual Time-series Prediction Under Policy Interventions

S. T. John, P. Marttinen, K. Pietiläinen, 2022, ArXiv.

Multitask Balanced and Recalibrated Network for Medical Code Prediction

E. Cambria, P. Marttinen, Wei Sun, 2021, ACM Trans. Intell. Syst. Technol..

Deformation equivariant cross-modality image synthesis with paired non-aligned training data

L. Latonen, P. Ruusuvuori, P. Marttinen, 2022, ArXiv.

HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes

Samuel Kaski, C. Lippert, P. Marttinen, 2022, bioRxiv.

HAPNEST: An efficient tool for generating large-scale genetics datasets from limited training data

Alicia R. Martin, Samuel Kaski, C. Lippert, 2022 .

Finite Adaptation and Multistep Moves in the Metropolis-Hastings Algorithm for Variable Selection in Genome-Wide Association Analysis

Aki Vehtari, P. Marttinen, Tomi Peltola, 2012, PloS one.

Interactive user intent modeling for eliciting priors of a normal linear model

Samuel Kaski, Giulio Jacucci, Tomi Peltola, 2016 .

A Unified Review of Deep Learning for Automated Medical Coding

P. Marttinen, Wei Sun, Honghan Wu, 2022, ArXiv.

Gene–gene interaction detection with deep learning

Samuel Kaski, A. Havulinna, P. Marttinen, 2021, bioRxiv.

Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function

Liisa Holm, Petri Törönen, Pekka Marttinen, 2009, BMC Bioinformatics.

Modelling G×E with historical weather information improves genomic prediction in new environments

Samuel Kaski, Jussi Gillberg, Hiroshi Mamitsuka, 2017, bioRxiv.

Plasmids Shaped the Recent Emergence of the Major Nosocomial Pathogen Enterococcus faecium

Samuel Kaski, J. Corander, P. Marttinen, 2020, mBio.

Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation

Aki Vehtari, Michael U. Gutmann, Marko Jarvenpaa, 2017, Bayesian Analysis.

Substitutions of short heterologous DNA segments of intragenomic or extragenomic origins produce clustered genomic polymorphisms

Cheryl P. Andam, W. Hanage, P. Marttinen, 2016, Proceedings of the National Academy of Sciences.

Phylogeographic variation in recombination rates within a global clone of methicillin-resistant Staphylococcus aureus

J. Corander, W. Hanage, J. Parkhill, 2012, Genome Biology.

A non-adaptive demographic mechanism for genome expansion in Streptomyces

P. Marttinen, D. Buckley, M. Järvenpää, 2021, bioRxiv.

Genetics and population analysis metaCCA : summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

M. Pirinen, T. Lehtimäki, V. Salomaa, 2016 .

Genome-wide association studies with high-dimensional phenotypes

Jukka Corander, Samuel Kaski, Jussi Gillberg, 2012, Statistical applications in genetics and molecular biology.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

M. Pirinen, T. Lehtimäki, V. Salomaa, 2015, bioRxiv.

Plant Sterol Feeding Induces Tumor Formation and Alters Sterol Metabolism in the Intestine of Apc MinMice

P. Marttinen, V. Piironen, M. Niku, 2014, Nutrition and cancer.

Efficient Bayesian approach for multilocus association mapping including gene-gene interactions

Jukka Corander, Pekka Marttinen, J. Corander, 2010, BMC Bioinformatics.

biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements

Matti Pirinen, Pekka Marttinen, Manuel A. Rivas, 2016, bioRxiv.

Crop loss identification at field parcel scale using satellite remote sensing and machine learning

T. Palosuo, Hiroshi Mamitsuka, P. Marttinen, 2021, bioRxiv.

Bayesian clustering and feature selection for cancer tissue samples

Jukka Corander, Pekka Marttinen, Samuel Myllykangas, 2009, BMC Bioinformatics.

Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations

Jukka Corander, Jing Tang, Jukka Sirén, 2008, BMC Bioinformatics.

A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

Philip S. Yu, Erik Cambria, Shirui Pan, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Ethical layering in AI-driven polygenic risk scores—New complexities, new challenges

S. McLennan, P. Marttinen, A. Buyx, 2023, Frontiers in Genetics.

COVIDNet: An Automatic Architecture for COVID-19 Detection With Deep Learning From Chest X-Ray Images

Neeraj Kumar, P. Marttinen, P. Tiwari, 2022, IEEE Internet of Things Journal.

Efficient Inference of Recent and Ancestral Recombination within Bacterial Populations

Jukka Corander, Pekka Marttinen, Nicholas J. Croucher, 2016, bioRxiv.

Speciation trajectories in recombining bacterial species

Pekka Marttinen, William P. Hanage, W. Hanage, 2016, bioRxiv.

Cryptic ecology among host generalist Campylobacter jejuni in domestic animals

Jukka Corander, Pekka Marttinen, Julian Parkhill, 2014, Molecular ecology.

Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression

Matti Pirinen, Samuel Kaski, Jussi Gillberg, 2014, Bioinform..

Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study

Pekka Marttinen, Shaoxiong Ji, Matti Holtta, 2021, Comput. Biol. Medicine.

BAPS 2: enhanced possibilities for the analysis of genetic population structure

Jukka Corander, Pekka Marttinen, Mikko J. Sillanpää, 2004, Bioinform..

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Amir K. Foroushani, Yuanfang Guan, Jaewoo Kang, 2019, Nature Communications.

Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection

P. Marttinen, Shaoxiong Ji, Ya Gao, 2023, ArXiv.

Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning

P. Marttinen, Shaoxiong Ji, 2021, EACL.

Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study

Jussi Gillberg, Pekka Marttinen, Esa Hämäläinen, 2017, PloS one.

Learning Global Pairwise Interactions with Bayesian Neural Networks

Samuel Kaski, Pekka Marttinen, Tianyu Cui, 2019, ECAI.

Dynamics and impact of homologous recombination on the evolution of Legionella pneumophila

C. Buchrieser, J. Parkhill, P. Marttinen, 2017, PLoS genetics.

Kernel method based on non-linear coherent states in quantum feature space pdf

Massimo Melucci, Shahram Dehdashti, Peter Bruza, 2020, Journal of Physics A: Mathematical and Theoretical.

Bacmeta: simulator for genomic evolution in bacterial metapopulations

Jukka Corander, Pekka Marttinen, Aleksi Sipola, 2018, Bioinform..

Bacmeta: simulation for genomic evolution in bacterial metapopulations

J. Corander, P. Marttinen, Aleksi Sipola, 2017, bioRxiv.

Baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss in individuals with obesity

A. Valsesia, A. Astrup, P. Marttinen, 2023, PeerJ.

A Unified Review of Deep Learning for Automated Medical Coding

P. Marttinen, Wei Sun, Honghan Wu, 2022, ACM Computing Surveys.

Modeling MRSA decolonization: Interactions between body sites and the impact of site-specific clearance

P. Marttinen, Y. Grad, L. Miller, 2020 .

The impact of site-specific clearance on methicillin-resistant staphylococcus aureus decolonization

P. Marttinen, Y. Grad, L. Miller, 2020, medRxiv.

Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text pdf

Erik Cambria, Pekka Marttinen, Shaoxiong Ji, 2020, CLINICALNLP.

Deep Learning for Depression Recognition with Audiovisual Cues: A Review

Lang He, Songtao Ding, Mingyue Niu, 2021, Inf. Fusion.

Informative Bayesian Neural Network Priors for Weak Signals

Samuel Kaski, A. Havulinna, P. Marttinen, 2020, Bayesian Analysis.

Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes

S. T. John, P. Marttinen, A. Juuti, 2022, 2209.04142.

Bayesian Model Learning Based on Predictive Entropy

Jukka Corander, Pekka Marttinen, J. Corander, 2006, J. Log. Lang. Inf..

Bayesian Model Learning Based on Predictive

J. Corander, P. Marttinen, 2006 .

Parallel Gaussian Process Surrogate Bayesian Inference with Noisy Likelihood Evaluations

Aki Vehtari, Marko Jarvenpaa, Pekka Marttinen, 2019, Bayesian Analysis.

Bayesian modeling of the impact of antibiotic resistance on the efficiency of MRSA decolonization

Mohamad R. Abdul Sater, P. Marttinen, Y. Grad, 2023, bioRxiv.

Quantifying Movement Behavior of Chronic Low Back Pain Patients in Virtual Reality

P. Marttinen, P. Liljeberg, T. Huttunen, 2023, ACM Transactions on Computing for Healthcare.

Bayesian modeling of recombination events in bacterial populations

Jukka Corander, Adam Baldwin, Pekka Marttinen, 2008, BMC Bioinformatics.

Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge

Samuel Kaski, Chen He, Giulio Jacucci, 2017, Bioinform..

Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets

Samuel Kaski, Giulio Jacucci, Tomi Peltola, 2016, IUI.

A Bayesian method for identification of stock mixtures from molecular marker data

Pekka Marttinen, Samu Mäntyniemi, Jukka Corrander, 2006 .

A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models

P. Marttinen, Severi Rissanen, 2021, NeurIPS.

Identifiable causal inference with noisy treatment and no side information

P. Marttinen, Antti Pöllänen, 2023, ArXiv.

A Critical Look At The Identifiability of Causal Effects with Deep Latent Variable Models pdf

Pekka Marttinen, Severi Rissanen, P. Marttinen, 2021, ArXiv.

Multitask Recalibrated Aggregation Network for Medical Code Prediction

Erik Cambria, Pekka Marttinen, Shaoxiong Ji, 2021, ECML/PKDD.

Bayesian learning of graphical vector autoregressions with unequal lag-lengths

Jukka Corander, Pekka Marttinen, J. Corander, 2009, Machine Learning.

Medical Code Assignment with Gated Convolution and Note-Code Interaction

Shirui Pan, P. Marttinen, Shaoxiong Ji, 2020, FINDINGS.

Bayesian search of functionally divergent protein subgroups and their function specific residues

L. Holm, J. Corander, P. Törönen, 2006, Bioinform..

Bayesian Clustering of Fuzzy Feature Vectors Using a Quasi-Likelihood Approach

Bernard De Baets, Jukka Corander, Jing Tang, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Recovering Pairwise Interactions Using Neural Networks

Samuel Kaski, P. Marttinen, Tianyu Cui, 2019, ArXiv.

A Bayesian model of acquisition and clearance of bacterial colonization

Pekka Marttinen, Yonatan H. Grad, Paul C. Blainey, 2018, ArXiv.

A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation

Pekka Marttinen, Marko Järvenpää, Mohamad R. Abdul Sater, 2018, bioRxiv.

Improving Neural Networks for Genotype-Phenotype Prediction Using Published Summary Statistics

Khaoula El Mekkaoui, Samuel Kaski, A. Havulinna, 2021, bioRxiv.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Suleiman A. Khan, Michael A. Goren, Donna E. Dillenberger, 2016, Nature Communications.

Gaussian process modelling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria

Aki Vehtari, P. Marttinen, Michael U Gutmann, 2016, The Annals of Applied Statistics.

Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach

P. Marttinen, Markus Heinonen, Tianyu Cui, 2022, AISTATS.

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

Aki Vehtari, P. Marttinen, M. Järvenpää, 2019, UAI.

Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data

Aki Vehtari, Tomi Peltola, Markus Perola, 2012, PloS one.

Unsupervised feature selection based on variance-covariance subspace distance

P. Marttinen, P. Tiwari, S. Vahdati, 2023, Neural Networks.

Deconfounded Representation Similarity for Comparison of Neural Networks

Samuel Kaski, P. Marttinen, Tianyu Cui, 2022, NeurIPS.

A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease

Amit Kumar Jaiswal, P. Marttinen, V. K. Rathi, 2023, ArXiv.

Deformation equivariant cross-modality image synthesis with paired non-aligned training data

P. Ruusuvuori, P. Marttinen, Joel Honkamaa, 2022, Medical Image Anal..

Bioinformatics Original Paper Bayesian Search of Functionally Divergent Protein Subgroups and Their Function Specific Residues

P. Marttinen, Petri Tö Rö Nen, J. Corander, 2022 .

Substitutions of short heterologous DNA segments of intragenomic or extragenomic origins produce clustered genomic polymorphisms

Cheryl P. Andam, W. Hanage, P. Marttinen, 2016, Proceedings of the National Academy of Sciences.

Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation

Aki Vehtari, P. Marttinen, Michael U Gutmann, 2017, Bayesian Analysis.

New features in a nutshell

J. Corander, P. Marttinen, Jingwan Tang, 2009 .

Causal Modeling of Policy Interventions From Treatment-Outcome Sequences

S. T. John, P. Marttinen, A. Juuti, 2023, ICML.