Author

Mikhail Lebedev

ORCID 0000-0002-3568-6511Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Assay:whole-genome sequencing· 2 Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Dmitry S. KolobkoviD· 2 shared Egor KosaretskiyiD· 2 shared Ruslan VakhitoviD· 2 shared Александр Владимирович МедведевiD· 2 shared Alexander Tischenko· 1 shared Andrew Ponomarev· 1 shared Dmitriy Osipenko· 1 shared Hui WangiD· 1 shared

Activity by Year

2022-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Dmitry S. KolobkovORCID

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2 papers

Recent·Most cited

Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project

Dmitry S. Kolobkov, Satyarth Mishra Sharma, Александр Владимирович Медведев, Mikhail Lebedev, Egor Kosaretskiy, Ruslan Vakhitov

This study evaluates federated learning's effectiveness on genomic data using UK Biobank and 1000 Genomes Project datasets, showing performance close to centralized models despite data heterogeneity.

Frontiers in Big Data · Feb 29, 2024Peer-reviewedGenetic & Genomic Medicine

GRAPE: Genomic Relatedness Detection Pipeline

Александр Владимирович Медведев, Mikhail Lebedev, Andrew Ponomarev, Mikhail Kosaretskiy, Dmitriy Osipenko, Alexander Tischenko, Egor Kosaretskiy, Hui Wang, Dmitry S. Kolobkov, Vitalina Chamberlain-Evans, Ruslan Vakhitov, Pavel Nikonorov

GRAPE is a genomic relatedness detection pipeline that accurately classifies kinship degrees using preprocessing, IBD detection, and relationship estimation.

F1000Research · May 30, 2022Peer-reviewedBioinformatics