ArticleBioMed research international2013
A novel method of predicting protein disordered regions based on sequence features.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences.Biology · 2022Article
- The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer.Frontiers in genetics · 2020Article
- The Methylation Pattern for Knee and Hip Osteoarthritis.Frontiers in cell and developmental biology · 2020Article
- Identification and Analysis of Glioblastoma Biomarkers Based on Single Cell Sequencing.Frontiers in bioengineering and biotechnology · 2020Article
- The early detection of asthma based on blood gene expression.Molecular biology reports · 2019Article
- The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing.Journal of Cancer · 2019Article
- The Gene Expression Biomarkers for Chronic Obstructive Pulmonary Disease and Interstitial Lung Disease.Frontiers in genetics · 2019Article
- Cross talk of chromosome instability, CpG island methylator phenotype and mismatch repair in colorectal cancer.Oncology letters · 2018Article
- Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer.OncoTargets and therapy · 2018Article
- Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy.OncoTargets and therapy · 2018Article
- Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods.Frontiers in genetics · 2018Article
- Genetic differences among ethnic groups.BMC genomics · 2015Article
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Authors and funding
7 authors.
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Abstract
With a large number of disordered proteins and their important functions discovered, it is highly desired to develop effective methods to computationally predict protein disordered regions. In this study, based on Random Forest (RF), Maximum Relevancy Minimum Redundancy (mRMR), and Incremental Feature Selection (IFS), we developed a new method to predict disordered regions in proteins. The mRMR criterion was used to rank the importance of all candidate features. Finally, top 128 features were selected from the ranked feature list to build the optimal model, including 92 Position Specific Scoring Matrix (PSSM) conservation score features and 36 secondary structure features. As a result, Matthews correlation coefficient (MCC) of 0.3895 was achieved on the training set by 10-fold cross-validation. On the basis of predicting results for each query sequence by using the method, we used the scanning and modification strategy to improve the performance. The accuracy (ACC) and MCC were increased by 4% and almost 0.2%, respectively, compared with other three popular predictors: DISOPRED, DISOclust, and OnD-CRF. The selected features may shed some light on the understanding of the formation mechanism of disordered structures, providing guidelines for experimental validation.
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