Author

Farrokh Mehryary

University of TurkuORCID 0000-0002-5555-2828Unclaimed 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:database / resource· 2 Assay:protein sequence· 2 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

Katerina C. NastouiD· 2 shared Lars Juhl JenseniD· 2 shared Sampo PyysaloiD· 2 shared Tomoko OhtaiD· 2 shared Filip GinteriD· 1 shared Hans MoeniD· 1 shared Jari BjörneiD· 1 shared Jouni LuomaiD· 1 shared

Activity by Year

2020-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBioinformatics

Katerina C. NastouORCID

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3 papers

Recent·Most cited

STRING-ing together protein complexes: corpus and methods for extracting physical protein interactions from the biomedical literature

Farrokh Mehryary, Katerina C. Nastou, Tomoko Ohta, Lars Juhl Jensen, Sampo Pyysalo

The paper introduces a manually annotated corpus and a relation extraction model to identify physical protein interactions from biomedical literature, integrating results into the STRING database.

Bioinformatics · Sep 1, 2024Peer-reviewedBioinformatics

RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature

Katerina C. Nastou, Farrokh Mehryary, Tomoko Ohta, Jouni Luoma, Sampo Pyysalo, Lars Juhl Jensen

RegulaTome provides a comprehensive corpus of biomedical relations, enabling improved relation extraction with a Transformer model achieving 66.6% F1-score.

Database · Jan 1, 2024Peer-reviewedBioinformatics

Neural network and random forest models in protein function prediction

Kai Hakala, Suwisa Kaewphan, Jari Björne, Farrokh Mehryary, Hans Moen, Martti E. E. Tolvanen, Tapio Salakoski, Filip Ginter

The paper develops an ensemble system combining random forest and neural network models to predict protein functions using sequence and taxonomic features, showing competitive performance in CAFA3 evaluations.

IEEE Transactions on Computational Biology and Bioinformatics · Dec 11, 2020Peer-reviewedBioinformatics