CACO: A Core-Attachment Method With Cross-Species Functional Ortholog Information to Detect Human Protein Complexes

Wang, Wenkang; Meng, Xiangmao; Xiang, Ju; Shuai, Yunyan; Bedru, Hayat Dino; Li, Min

doi:10.1109/jbhi.2023.3289490

Cited by 2 publications

References 64 publications

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A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches

Wang,

Shuai,

Yang

et al. 2024

Briefings in Bioinformatics

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Proteins play an important role in life activities and are the basic units for performing functions. Accurately annotating functions to proteins is crucial for understanding the intricate mechanisms of life and developing effective treatments for complex diseases. Traditional biological experiments struggle to keep pace with the growing number of known proteins. With the development of high-throughput sequencing technology, a wide variety of biological data provides the possibility to accurately predict protein functions by computational methods. Consequently, many computational methods have been proposed. Due to the diversity of application scenarios, it is necessary to conduct a comprehensive evaluation of these computational methods to determine the suitability of each algorithm for specific cases. In this study, we present a comprehensive benchmark, BeProf, to process data and evaluate representative computational methods. We first collect the latest datasets and analyze the data characteristics. Then, we investigate and summarize 17 state-of-the-art computational methods. Finally, we propose a novel comprehensive evaluation metric, design eight application scenarios and evaluate the performance of existing methods on these scenarios. Based on the evaluation, we provide practical recommendations for different scenarios, enabling users to select the most suitable method for their specific needs. All of these servers can be obtained from https://csuligroup.com/BEPROF and https://github.com/CSUBioGroup/BEPROF.

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A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches

Wang,

Shuai,

Yang

et al. 2024

Briefings in Bioinformatics

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Protein function prediction using graph neural network with multi-type biological knowledge

Shuai,

Wang,

et al. 2023

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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CACO: A Core-Attachment Method With Cross-Species Functional Ortholog Information to Detect Human Protein Complexes

Cited by 2 publications

References 64 publications

A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches

A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches

Protein function prediction using graph neural network with multi-type biological knowledge

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