A systematic review on the state-of-the-art strategies for protein representation

被引：5

作者：

Yue, Zi-Xuan ^{[1
]}

Yan, Tian-Ci ^{[1
]}

Xu, Hong-Quan ^{[1
]}

Liu, Yu-Hong ^{[1
]}

Hong, Yan-Feng ^{[1
]}

Chen, Gong-Xing ^{[1
]}

Xie, Tian ^{[1
]}

Tao, Lin ^{[1
]}

机构：

[1] Hangzhou Normal Univ, Sch Pharm, Key Lab Elemene Class Anticanc Chinese Med, Hangzhou 311121, Peoples R China

来源：

COMPUTERS IN BIOLOGY AND MEDICINE | 2023年 / 152卷

基金：

中国国家自然科学基金;

关键词：

Protein representation methods; Sequence-based descriptors; Structure-based descriptors; Machine learning; Drug research; PREDICTION; IDENTIFICATION; NORMALIZATION; SCALE; RNA;

D O I：

10.1016/j.compbiomed.2022.106440

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, pre-dictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. Ac-cording to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

引用

页数：10

共 107 条

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