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A comprehensive review and comparison of existing computational methods for protein function prediction.
Lin, Baohui; Luo, Xiaoling; Liu, Yumeng; Jin, Xiaopeng.
Afiliación
  • Lin B; College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong 518118, China.
  • Luo X; Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Shenzhen, Guangdong, China.
  • Liu Y; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518061, China.
  • Jin X; College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong 518118, China.
Brief Bioinform ; 25(4)2024 May 23.
Article en En | MEDLINE | ID: mdl-39003530
ABSTRACT
Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Proteínas / Biología Computacional Límite: Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Proteínas / Biología Computacional Límite: Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China