Author

Antti Airola

University of TurkuORCID 0000-0002-1010-4386Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Research Focus:clustering· 1 Research Focus:dimensionality reduction· 1 Assay:electronic health records· 1 Organism:human· 1

Frequent Co-Authors

Ilkka SuuroneniD· 2 shared Tapio PahikkalaiD· 2 shared Aaron BarroniD· 1 shared Ashmeet JollyiD· 1 shared Aylin RosbergiD· 1 shared Bernard De Baets CiD· 1 shared Elena VartiaineniD· 1 shared Elmo P. PulliiD· 1 shared

Activity by Year

2018-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersNeuroscience

Ilkka SuuronenORCID

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3 papers

Recent·Most cited

NeurosciencebioRxivPreprintOct 26, 2025

skiftiTools: An R package for reading, writing, analysing, and visualising, tract-based spatial statistics (TBSS) derived diffusion MR images

Jetro J. Tuulari, Aaron Barron, Ashmeet Jolly…·Harri Merisaari

skiftiTools enables statistical analysis and visualization of TBSS-derived diffusion MRI data using R, supporting advanced modeling and data manipulation.

DOI 10.1101/2025.10.26.684698

imagingstatistical model

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NeuroscienceIEEE Journal of Biomedical and Health InformaticsPeer-reviewedJan 9, 2023

Resting state EEG as a biomarker of Parkinson's disease: Influence of measurement conditions

Henry Railo, Ilkka Suuronen, Valtteri Kaasinen…·Antti Airola

This study evaluates the use of resting state EEG for Parkinson's disease classification, finding that fewer electrodes and open-eye conditions improve accuracy.

64 citationstop 1%

humanelectronic health records

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Systems BiologyBriefings in BioinformaticsPeer-reviewedSep 10, 2018

Algebraic Shortcuts for Leave-One-Out Cross-Validation in Supervised Network Inference

Michiel Stock, Tapio Pahikkala, Antti Airola…·Bernard De Baets C

The paper introduces algebraic shortcuts for leave-one-out cross-validation to evaluate kernel-based network inference methods efficiently.

20 citations

statistical modelbenchmarking

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