Mikko J. Sillanpää

Professor of Statistics

University of Oulu

Finland

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Professor Mikko J. Sillanpää is a faculty member at the University of Oulu in Finland. His research focuses on statistical modeling in health science, particularly in the areas of diabetes risk prediction, childhood BMI trajectory analysis, and multi-outcome regression techniques. He has contributed to advancements in Bayesian statistics and genomic prediction, evidenced by his publications on novel regression methods and the utilization of graph neural networks.

Mikko J. Sillanpää

Research Interests

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Articles (26)

Robust multi-outcome regression with correlated covariate blocks using fused LAD-lasso

Adaptive and Self-Tuning SBL With Total Variation Priors for Block-Sparse Signal Recovery

Developing risk prediction models for type 2 diabetes and assessing the role of circulating metabolic biomarkers in five independent Finnish cohorts with over 22000 individuals

Timing based clustering of childhood BMI trajectories reveals differential maturational patterns; Study in the Northern Finland Birth Cohorts 1966 and 1986

Posterior estimation of longitudinal variance components from nonlongitudinal data using Bayesian Gaussian process model

Network hub gene detection using the entire solution path information

A Bayesian multi-state model with data augmentation for estimating population size and effect of inbreeding on survival

Hierarchical MTC User Activity Detection and Channel Estimation with Unknown Spatial Covariance

Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes

Editorial

Capacitated spatial clustering with multiple constraints and attributes

Evaluation of Sparse Proximal Multi-Task Learning for Genome-Wide Prediction

Principal component analysis revisited: fast multitrait genetic evaluations with smooth convergence

Reducing bias and mitigating the influence of excess of zeros in regression covariates with multi-outcome adaptive LAD-lasso

Genetic fine-mapping from summary data using a nonlocal prior improves the detection of multiple causal variants

BELMM: Bayesian model selection and random walk smoothing in time-series clustering

Sequential Model Correction for Nonlinear Inverse Problems

Gradient Boosted Regression Trees for Modelling Onset of Austenite Decomposition During Cooling of Steels

Neural Network Kalman Filtering for 3-D Object Tracking From Linear Array Ultrasound Data

Hierarchical deconvolution for incoherent scatter radar data

Age-dependent genetic architecture across ontogeny of body size in sticklebacks

Gap-com: general model selection criterion for sparse undirected gene networks with nontrivial community structure

Edge computing server placement with capacitated location allocation

Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways

Importance of correcting genomic relationships in single-locus QTL mapping model with an advanced backcross population

MCPeSe: Monte Carlo penalty selection for graphical lasso

Collaborators (10)

Jukka Riekki

Samu Mäntyniemi

Jyrki Mottonen

Tanja Pyhäjärvi

Bhaskar D. Rao

Patrik Waldmann

Simon Arridge

Lauri Lovén

I. I. Virtanen

Markku Juntti

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