Author

Nikolai Romashchenko

All-Russian Research Institute of Agricultural MicrobiologyORCID 0000-0002-2264-5219Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1 Organism:virus· 1 Assay:whole-exome sequencing· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Andrey Afanasyev· 1 shared Benjamin LinardiD· 1 shared Éric RivalsiD· 1 shared Fabio PardiiD· 1 shared Guillaume E. ScholziD· 1 shared Ilia O. KorvigoiD· 1 shared Mikhail Y. SkobloviD· 1 shared

Activity by Year

2018-2020 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 9 mutual co-author links

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1 shared paperBioinformatics

Andrey Afanasyev

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2 papers

Recent·Most cited

Rapid screening and detection of inter-type viral recombinants using phylo-k-mers

Guillaume E. Scholz, Benjamin Linard, Nikolai Romashchenko, Éric Rivals, Fabio Pardi

SHERPAS rapidly detects viral recombinants by analyzing phylogenetically-informed k-mers, outperforming existing methods in speed with minimal accuracy loss.

Bioinformatics · Nov 27, 2020Peer-reviewedBioinformatics

Generalising Better: Applying Deep Learning To Integrate Deleteriousness Prediction Scores For Whole-Exome SNV Studies

Ilia O. Korvigo, Andrey Afanasyev, Nikolai Romashchenko, Mikhail Y. Skoblov

This paper explores using deep learning to improve the integration of deleteriousness prediction scores for whole-exome SNV studies, showing that simple neural networks can enhance prediction accuracy and coverage.

PLoS ONE · Mar 14, 2018Peer-reviewedBioinformatics