Abstract:
:Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.
journal_name
Brief Bioinformjournal_title
Briefings in bioinformaticsauthors
Hong J,Luo Y,Zhang Y,Ying J,Xue W,Xie T,Tao L,Zhu Fdoi
10.1093/bib/bbz081subject
Has Abstractpub_date
2020-07-15 00:00:00pages
1437-1447issue
4eissn
1467-5463issn
1477-4054pii
5542883journal_volume
21pub_type
杂志文章abstract:MOTIVATION:Over the past decade, the field of next-generation sequencing (NGS) has seen dramatic advances in methods and a decrease in costs. Consequently, a large expansion of data has been generated by NGS, most of which have originated from RNA-sequencing (RNA-seq) experiments. Because mitochondrial genes are expres...
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更新日期:2010-01-01 00:00:00
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