Author

Sakiya Kawada

Tohoku UniversityORCID 0000-0002-8345-6219Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Research Focus:protein design· 1 Assay:protein sequence· 1 Research Focus:protein structure prediction· 1 Assay:proteomics· 1

Frequent Co-Authors

Mitsuo UmetsuiD· 2 shared Tomoyuki ItoiD· 2 shared Yoichi KurumidaiD· 2 shared Yutaka SaitōiD· 2 shared Hikaru NakazawaiD· 1 shared Nur Syatila Ab GhaniiD· 1 shared Takuma Matsushita· 1 shared Tomohiro NoguchiiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Mitsuo UmetsuORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintAug 7, 2026

Evaluating Lightweight and Full Fine-Tuning Strategies Against Classical Machine Learning for Protein Function Prediction

Nur Syatila Ab Ghani, Takuma Matsushita, Tomohiro Noguchi…·Yutaka Saitō

This study compares different fine-tuning strategies and classical machine learning approaches for protein function prediction using benchmark datasets.

DOI 10.64898/2026.08.02.737389

protein sequencemachine learning (classical)

BioengineeringbioRxivPreprintJun 1, 2026

Exploring diverse routes to high-affinity-antibody variable domains through deep-sequencing-informed machine learning

Sakiya Kawada, Tomoyuki Ito, Hikaru Nakazawa…·Mitsuo Umetsu

This study uses deep sequencing and machine learning to optimize antibody discovery by analyzing training data and search space design for high-affinity VHHs.

DOI 10.64898/2026.05.28.728451

proteomicsdeep learning