NetGO 3.0: A Recent Protein Function Prediction Tool Based on Protein Language Model.

Shaojun Wang, Hancheng Liu, Ronghui You, Yunjia Liu, Yi Xiong, Shanfeng Zhu

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Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Shaojun WangInstitute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.

Hancheng LiuInstitute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.

Ronghui YouSchool of Statistics and Data Science, Nankai University, Tianjin, China.

Yunjia LiuMailman School of Public Health, Columbia University, New York, United States of America.

Yi XiongSchool of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Shanfeng ZhuInstitute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China. zhusf@fudan.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the field of bioinformatics, automated function prediction (AFP) for proteins is a significant issue. We propose a computational framework based on a protein language model to provide accurate functional predictions for proteins. By integrating multiple component methods, our approach effectively improves prediction performance. Additionally, we have developed a user-friendly online platform that allows users to obtain prediction results simply by submitting protein sequences, freely available at https://dmiip.sjtu.edu.cn/ng3.0/?returning=true . We provide a detailed guide on how to use the web server and correctly interpret the prediction results. Finally, through a practical example, we demonstrate the superior performance of NetGO 3.0 in predicting protein functions, further showcasing the potential of this framework for protein functional annotation.

Indexed as

Computational BiologyProteinsSoftwareAlgorithmsDatabases, ProteinProteinsGene ontologyNetGO 3.0Protein function predictionProtein language models

Identifiers

PMID40728611

DOI10.1016/j.jgg.2023.09.002

What OpenQuestion holds

Textmetadata

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.