ArticleNucleic acids research2010
Boosting the prediction and understanding of DNA-binding domains from sequence.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Benchmarking recent computational tools for DNA-binding protein identification.Briefings in bioinformatics · 2024Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features.Applied bionics and biomechanics · 2022Article
- PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method.BioMed research international · 2020Article
- HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2020Article
- Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins.Frontiers in genetics · 2019Article
- iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features.Scientific reports · 2017Article
- HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.BioMed research international · 2017Article
- Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm.BMC bioinformatics · 2016Article
- DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.Nucleic acids research · 2015Article
- Network-based prediction and knowledge mining of disease genes.BMC medical genomics · 2015Article
- nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.BMC bioinformatics · 2014Article
- Article
- Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.PloS one · 2014Article
- Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.PloS one · 2014Article
- A structure-based protocol for learning the family-specific mechanisms of membrane-binding domains.Bioinformatics (Oxford, England) · 2012Article
- Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters.Nucleic acids research · 2012Article
- Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence.Nucleic acids research · 2011Article
- iDNA-Prot: identification of DNA binding proteins using random forest with grey model.PloS one · 2011Article
- An improved machine learning protocol for the identification of correct Sequest search results.BMC bioinformatics · 2010Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
DNA-binding proteins perform vital functions related to transcription, repair and replication. We have developed a new sequence-based machine learning protocol to identify DNA-binding proteins. We compare our method with an extensive benchmark of previously published structure-based machine learning methods as well as a standard sequence alignment technique, BLAST. Furthermore, we elucidate important feature interactions found in a learned model and analyze how specific rules capture general mechanisms that extend across DNA-binding motifs. This analysis is carried out using the malibu machine learning workbench available at http://proteomics.bioengr.uic.edu/malibu and the corresponding data sets and features are available at http://proteomics.bioengr.uic.edu/dna.
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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.