A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.

Autor: Teng-Ruei Chen, Sheng-Hung Juan, Yu-Wei Huang, Yen-Cheng Lin, Wei-Cheng Lo
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: PLoS ONE, Vol 16, Iss 7, p e0255076 (2021)
Druh dokumentu: article
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0255076
Popis: Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing
Databáze: Directory of Open Access Journals
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