SBSM-Pro: support bio-sequence machine for proteins

Wang, Yizheng; Zhai, Yixiao; Ding, Yijie; Zou, Quan

doi:10.1007/s11432-024-4171-9

Sci. China Inf. Sci.

2024

DOI: 10.1007/s11432-024-4171-9

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SBSM-Pro: support bio-sequence machine for proteins

Yizheng Wang,

Yixiao Zhai,

Yijie Ding

et al.

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Cited by 36 publications

References 53 publications

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Identification of RNA‐dependent liquid‐liquid phase separation proteins using an artificial intelligence strategy

Ahmed,

Shahzadi,

Jin

et al. 2024

Proteomics

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RNA‐dependent liquid‐liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry‐based methods for identifying these proteins are time‐consuming and costly. Addressing this challenge, our study developed a robust computational model for their identification. We constructed a comprehensive dataset containing 137 RNA‐dependent and 606 non‐RNA‐dependent LLPS protein sequences, which were then encoded using amino acid composition, composition of K‐spaced amino acid pairs, Geary autocorrelation, and conjoined triad methods. Through a combination of correlation analysis, mutual information scoring, and incremental feature selection, we identified an optimal feature subset. This subset was used to train a random forest model, which achieved an accuracy of 90% when tested against an independent dataset. This study demonstrates the potential of computational methods as efficient alternatives for the identification of RNA‐dependent LLPS proteins. To enhance the accessibility of the model, a user‐centric web server has been established and can be accessed via the link: http://rpp.lin‐group.cn.

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Identification of RNA‐dependent liquid‐liquid phase separation proteins using an artificial intelligence strategy

Ahmed,

Shahzadi,

Jin

et al. 2024

Proteomics

View full text Add to dashboard Cite

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TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning

Yan,

Lv,

Shao

et al. 2024

Sci. China Inf. Sci.

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Computational approaches for circRNA-disease association prediction: a review

Niu,

Chen,

Wang

et al. 2024

Front. Comput. Sci.

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Circular RNA (circRNA) is a covalently closed RNA molecule formed by back splicing. The role of circRNAs in posttranscriptional gene regulation provides new insights into several types of cancer and neurological diseases. CircRNAs are associated with multiple diseases and are emerging biomarkers in cancer diagnosis and treatment. The associations prediction is one of the current research hotspots in the field of bioinformatics. Although research on circRNAs has made great progress, the traditional biological method of verifying circRNA-disease associations is still a great challenge because it is a difficult task and requires much time. Fortunately, advances in computational methods have made considerable progress in circRNA research. This review comprehensively discussed the functions and databases related to circRNA, and then focused on summarizing the calculation model of related predictions, detailed the mainstream algorithm into 4 categories, and analyzed the advantages and limitations of the 4 categories. This not only helps researchers to have overall understanding of circRNA, but also helps researchers have a detailed understanding of the past algorithms, guide new research directions and research purposes to solve the shortcomings of previous research.

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SBSM-Pro: support bio-sequence machine for proteins

Cited by 36 publications

References 53 publications

Identification of RNA‐dependent liquid‐liquid phase separation proteins using an artificial intelligence strategy

Identification of RNA‐dependent liquid‐liquid phase separation proteins using an artificial intelligence strategy

TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning

Computational approaches for circRNA-disease association prediction: a review

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