ArticleBriefings in bioinformatics2020
Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning.
Article in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
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- Recall DNA methylation levels at low coverage sites using a CNN model in WGBS.PLoS computational biology · 2023Article
- Improving language model of human genome for DNA-protein binding prediction based on task-specific pre-training.Interdisciplinary sciences, computational life sciences · 2023Article
- M6AREG: m6A-centered regulation of disease development and drug response.Nucleic acids research · 2023Article
- DrugMAP: molecular atlas and pharma-information of all drugs.Nucleic acids research · 2023Article
- DRESIS: the first comprehensive landscape of drug resistance information.Nucleic acids research · 2023Article
- Bioinformatics characterization of BcsA-like orphan proteins suggest they form a novel family of pseudomonad cyclic-β-glucan synthases.PloS one · 2023Article
- An informatic workflow for the enhanced annotation of excretory/secretory proteins ofComputational and structural biotechnology journal · 2023Article
- A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs.Nucleic acids research · 2022Article
- Understanding the mutational frequency in SARS-CoV-2 proteome using structural features.Computers in biology and medicine · 2022Article
- Integrated COVID-19 Predictor: Differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data.Computers in biology and medicine · 2022Article
- RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins.Briefings in bioinformatics · 2022Article
- Functional annotation of creeping bentgrass protein sequences based on convolutional neural network.BMC plant biology · 2022Article
- RNA-RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection.Briefings in bioinformatics · 2022Article
- VARIDT 2.0: structural variability of drug transporter.Nucleic acids research · 2022Article
- Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents.Nucleic acids research · 2022Article
- DeepLRR: An Online Webserver for Leucine-Rich-Repeat Containing Protein Characterization Based on Deep Learning.Plants (Basel, Switzerland) · 2022Article
- LncRNA functional annotation with improved false discovery rate achieved by disease associations.Computational and structural biotechnology journal · 2022Article
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8 authors.
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No grant is acknowledged in the PubMed record.
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.
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