ArticleInternational journal of molecular sciences2018
Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 39 citations in OpenAlex.
- AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding.Genome biology · 2024Article
- Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning.Briefings in bioinformatics · 2020Article
- A novel riboswitch classification based on imbalanced sequences achieved by machine learning.PLoS computational biology · 2020Article
- Photosynthetic protein classification using genome neighborhood-based machine learning feature.Scientific reports · 2020Article
- iProEP: A Computational Predictor for Predicting Promoter.Molecular therapy. Nucleic acids · 2019Article
- Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility.CNS neuroscience & therapeutics · 2019Article
- Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools.International journal of molecular sciences · 2019Article
- Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics.Frontiers in pharmacology · 2019Article
- Identification of novel immune-relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis.CNS neuroscience & therapeutics · 2018Article
- Prediction of Signal Peptides in Proteins from Malaria Parasites.International journal of molecular sciences · 2018Article
- Prediction of GluN2B-CTMolecules (Basel, Switzerland) · 2018Article
- Special Protein Molecules Computational Identification.International journal of molecular sciences · 2018Article
- Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target.Frontiers in pharmacology · 2018Article
- Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification.Frontiers in pharmacology · 2018Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The function of a protein is of great interest in the cutting-edge research of biological mechanisms, disease development and drug/target discovery. Besides experimental explorations, a variety of computational methods have been designed to predict protein function. Among these in silico methods, the prediction of BLAST is based on protein sequence similarity, while that of machine learning is also based on the sequence, but without the consideration of their similarity. This unique characteristic of machine learning makes it a good complement to BLAST and many other approaches in predicting the function of remotely relevant proteins and the homologous proteins of distinct function. However, the identification accuracies of these in silico methods and their false discovery rate have not yet been assessed so far, which greatly limits the usage of these algorithms. Herein, a comprehensive comparison of the performances among four popular prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these methods was systematically assessed by four standard statistical indexes based on the independent test datasets of 93 functional protein families defined by UniProtKB keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model organisms (
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