Author

Nur Syatila Ab Ghani

Kitasato UniversityORCID 0000-0003-0718-6904Unclaimed 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 Assay:protein sequence· 2 Research Focus:variant effect prediction· 2 Research Focus:benchmarking· 1 Approach:machine learning (classical)· 1 Research Focus:protein design· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

Yoichi KurumidaiD· 2 shared Yutaka SaitōiD· 2 shared Kaito KobayashiiD· 1 shared Mitsuo UmetsuiD· 1 shared Sakiya KawadaiD· 1 shared Shinji IidaiD· 1 shared Takuma Matsushita· 1 shared Teppei DeguchiiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

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2 shared papersBioinformatics

Yoichi KurumidaORCID

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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)

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BioinformaticsBriefings in BioinformaticsPeer-reviewedAug 31, 2025

Data-efficient protein mutational effect prediction with weak supervision by molecular simulation and protein language models

Teppei Deguchi, Nur Syatila Ab Ghani, Yoichi Kurumida…·Yutaka Saitō

The paper presents a method that improves protein mutational effect prediction with limited experimental data by combining molecular simulation and protein language models.

2 citations

protein sequencedeep learning

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