Author

Selena Setsu

Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan;ORCID 0009-0007-4261-5264Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Selena Setsu1 paper · Keio University

Biology Disciplines

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Biophysics· 1 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:proteomics· 2 Research Focus:sequence-to-function modeling· 2 Research Focus:cell type annotation· 1 Research Focus:drug response / drug discovery· 1 Organism:E. coli· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Emi Mishiro-SatoiD· 2 shared Kotaro TsuboyamaiD· 2 shared Akihide YoshimiiD· 1 shared Akinobu MatsumotoiD· 1 shared Andy Y. W. LamiD· 1 shared Atsushi HatanoiD· 1 shared Ayako SakamotoiD· 1 shared Chisa ShiraishiiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersMolecular Biology

Emi Mishiro-SatoORCID

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2 papers

Recent·Most cited

Molecular BiologybioRxivPreprintNov 5, 2026

Physiological mapping and prediction reveal stress-responsive translation initiation landscapes

Kazuya Ichihara, Taichi Shiraishi, Haruhiko Morita…·Akinobu Matsumoto

This study introduces TISCA2 and IncepTIS to map and predict stress-responsive translation initiation sites, revealing tissue-specific patterns and enabling engineered mRNA 5' leaders.

DOI 10.64898/2026.11.03.763266

humanproteomics

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BiophysicsbioRxivPreprintNov 4, 2026

Massively parallel quantification and prediction of monomeric protein yield in Escherichia coli

Selena Setsu, Kaori Kiyokawa, Andy Y. W. Lam…·Kotaro Tsuboyama

The paper develops a scalable assay and a protein language model to predict monomeric yield of proteins in E. coli, showing it can be learned and designed.

DOI 10.64898/2026.11.02.763507

E. coliproteomics

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