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

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...

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Veröffentlicht in:PloS one 2021-07, Vol.16 (7), p.e0255076-e0255076
Hauptverfasser: Chen, Teng-Ruei, Juan, Sheng-Hung, Huang, Yu-Wei, Lin, Yen-Cheng, Lo, Wei-Cheng
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Sprache:eng
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Zusammenfassung: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
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0255076