Author

Mayumi Kamada

Kyoto UniversityORCID 0000-0002-2555-7345Unclaimed 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:variant effect prediction· 2 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Approach:graph neural network· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Ryosuke KojimaiD· 2 shared Yasushi OkunoiD· 2 shared Atsuko Takagi· 1 shared Makoto HirataiD· 1 shared Masahiko NakatsuiiD· 1 shared Narumi Hatano· 1 shared Noriko TanabeiD· 1 shared Teruhiko YoshidaiD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 30 mutual co-author links

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2 shared papersBioinformatics

Ryosuke KojimaORCID

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2 papers

Recent·Most cited

Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network

Narumi Hatano, Mayumi Kamada, Ryosuke Kojima, Yasushi Okuno

This paper introduces Net-DMPred, a graph neural network-based method to predict cancer-specific driver missense mutations by incorporating molecular network structures.

BMC Bioinformatics · Oct 10, 2023Peer-reviewedBioinformatics

Network-based pathogenicity prediction for variants of uncertain significance

Mayumi Kamada, Atsuko Takagi, Ryosuke Kojima, Yoshihisa Tanaka, Masahiko Nakatsui, Noriko Tanabe, Makoto Hirata, Teruhiko Yoshida, Yasushi Okuno

This paper introduces PathoGN, a network-based method for predicting variant pathogenicity, which outperforms existing methods and identifies pathogenicity for 3,994 variants of uncertain significance.

bioRxiv · Jul 15, 2021 · PreprintGenomics