Autor: |
Teng-Ruei Chen, Sheng-Hung Juan, Yu-Wei Huang, Yen-Cheng Lin, Wei-Cheng Lo |
Jazyk: |
angličtina |
Rok vydání: |
2021 |
Předmět: |
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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 |
Externí odkaz: |
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