Author

Ilia O. Korvigo

Moscow Institute of Physics and TechnologyORCID 0000-0001-9049-1111Unclaimed 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· 2 Research Focus:benchmarking· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Mikhail Y. SkobloviD· 2 shared Anatolii ZaikovskiiiD· 1 shared Andrey Afanasyev· 1 shared Maksim M. KholmatoviD· 1 shared Nikolai RomashchenkoiD· 1 shared

Activity by Year

2018-2018 · peak 2/yr

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5 top collaborators · 6 mutual co-author links

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2 shared papersBioinformatics

Mikhail Y. SkoblovORCID

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2 papers

Recent·Most cited

BioinformaticsJournal of CheminformaticsPeer-reviewedMay 23, 2018

Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules.

Ilia O. Korvigo, Maksim M. Kholmatov, Anatolii Zaikovskii, Mikhail Y. Skoblov

The paper develops a deep CNN-RNN model for chemical named entity recognition without hand-crafted rules, achieving near human-level performance on the CHEMDNER corpus.

53 citations

deep learningbenchmarking

𝕏 PostBluesky

BioinformaticsPLoS ONEPeer-reviewedMar 14, 2018

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.

17 citations

whole-exome sequencingdeep learning

𝕏 PostBluesky