ArticleAmino acids2010
DomSVR: domain boundary prediction with support vector regression from sequence information alone.
Article in Amino acids, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps.BMC bioinformatics · 2022Article
- Res-Dom: predicting protein domain boundary from sequence using deep residual network and Bi-LSTM.Bioinformatics advances · 2022Article
- Protein domain identification methods and online resources.Computational and structural biotechnology journal · 2021Review
- Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm.International journal of molecular sciences · 2020Article
- DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction.BioMed research international · 2017Article
- LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone.BMC bioinformatics · 2014Article
- Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features.BMC bioinformatics · 2013Article
- DomHR: accurately identifying domain boundaries in proteins using a hinge region strategy.PloS one · 2013Article
- Article
- The MULTICOM toolbox for protein structure prediction.BMC bioinformatics · 2012Article
- TANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences.PloS one · 2012Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Protein domains are structural and fundamental functional units of proteins. The information of protein domain boundaries is helpful in understanding the evolution, structures and functions of proteins, and also plays an important role in protein classification. In this paper, we propose a support vector regression-based method to address the problem of protein domain boundary identification based on novel input profiles extracted from AAindex database. As a result, our method achieves an average sensitivity of approximately 36.5% and an average specificity of approximately 81% for multi-domain protein chains, which is overall better than the performance of published approaches to identify domain boundary. As our method used sequence information alone, our method is simpler and faster.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.