Mikko J Sillanpaa
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Explore the profile of Mikko J Sillanpaa including associated specialties, affiliations and a list of published articles.
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85
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1840
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Recent Articles
1.
Arjas A, Leppala K, Sillanpaa M
Genetics
. 2025 Mar;
PMID: 40037594
Many quantitative traits can be measured from a single individual only once, making acquisition of longitudinal data impossible. In this paper we present GP-REBE (Gaussian Process Restricted Bayesian Estimation), a...
2.
Heiskala A, Tucker J, Choudhary P, Nedelec R, Ronkainen J, Sarala O, et al.
Int J Obes (Lond)
. 2025 Jan;
PMID: 39820013
Background/objectives: Children's biological age does not always correspond to their chronological age. In the case of BMI trajectories, this can appear as phase variation, which can be seen as shift,...
3.
Kuismin M, Sillanpaa M
Genetics
. 2024 Nov;
229(1):1-33.
PMID: 39535861
Gene co-expression networks typically comprise modules and their associated hub genes, which are regulating numerous downstream interactions within the network. Methods for hub screening, as well as data-driven estimation of...
4.
Ahlinder J, Hall D, Suontama M, Sillanpaa M
G3 (Bethesda)
. 2024 Oct;
PMID: 39429114
A cornerstone in breeding and population genetics is the genetic evaluation procedure, needed to make important decisions on population management. Multivariate mixed model analysis, in which many traits are considered...
5.
Kihlman R, Launonen I, Sillanpaa M, Waldmann P
G3 (Bethesda)
. 2024 Sep;
14(11).
PMID: 39250757
In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the...
6.
Fraimout A, Guillaume F, Li Z, Sillanpaa M, Rastas P, Merila J
Mol Ecol
. 2024 Feb;
33(6):e17299.
PMID: 38380534
Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most non-model...
7.
Sarala O, Pyhajarvi T, Sillanpaa M
Bioinformatics
. 2023 Nov;
39(11).
PMID: 37963057
Motivation: Due to advances in measuring technology, many new phenotype, gene expression, and other omics time-course datasets are now commonly available. Cluster analysis may provide useful information about the structure...
8.
Karhunen V, Launonen I, Jarvelin M, Sebert S, Sillanpaa M
Bioinformatics
. 2023 Jun;
39(7).
PMID: 37348543
Motivation: Genome-wide association studies (GWAS) have been successful in identifying genomic loci associated with complex traits. Genetic fine-mapping aims to detect independent causal variants from the GWAS-identified loci, adjusting for...
9.
Heino H, Rieppo L, MaNnisto T, Sillanpaa M, Mantynen V, Saarakkala S
Sci Rep
. 2022 Nov;
12(1):20358.
PMID: 36437268
Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with machine learning-based partial least squares discriminant analysis (PLS-DA) was applied to study if severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could...
10.
Fraimout A, Li Z, Sillanpaa M, Merila J
Proc Biol Sci
. 2022 May;
289(1975):20220352.
PMID: 35582807
Heritable variation in traits under natural selection is a prerequisite for evolutionary response. While it is recognized that trait heritability may vary spatially and temporally depending on which environmental conditions...