Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information

Ma, Xin; Wu, Jiansheng; Xue, Xiaoyun

doi:10.1155/2013/524502

Cited by 12 publications

(4 citation statements)

References 35 publications

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“…The support vector machine (SVM) based methods to predict the DNA binding sites have been proposed in papers [ 18 , 25 – 29 ] by using the different sequential and structural features. In paper [ 25 ] surface and overall composition, overall charge, and positive potential patches on the protein surface have been used.…”

Section: An Overview Of Computational Intelligence Techniques In Pmentioning

confidence: 99%

“…In paper [ 28 ], normalized PSSM score, normalized solvent accessible surface area, and protein backbone structure have been used. In paper [ 29 ], PSSM, amino acid composition, hydrophobicity, polarity, polarizability, secondary structure, solvent accessibility, normalized Vander Waals volume, and binding and nonbinding propensity have been used. In paper [ 30 ] the authors have proposed a combination of SVM and ANN based method to predict the DNA binding sites with PSSM and structural features such as secondary structure, solvent accessibility, and globularity.…”

Section: An Overview Of Computational Intelligence Techniques In Pmentioning

confidence: 99%

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A Survey of Computational Intelligence Techniques in Protein Function Prediction

Tiwari

Srivastava

2014

International Journal of Proteomics

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During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction.

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Section: An Overview Of Computational Intelligence Techniques In Pmentioning

confidence: 99%

Section: An Overview Of Computational Intelligence Techniques In Pmentioning

confidence: 99%

A Survey of Computational Intelligence Techniques in Protein Function Prediction

Tiwari

Srivastava

2014

International Journal of Proteomics

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“…While a number of methods have been proposed for predicting DBPs and DBS separately ( 15 , 16 , 20 , 21 , 23 – 38 ), to the best of our knowledge, no study has been conducted to develop a prediction system that employs DBS as an engine for the DBP prediction, combined with the amino acid compositional biases of the full length proteins, and to evaluate it comprehensively on an entire genome.…”

Section: Introductionmentioning

confidence: 99%

Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism

Ahmad

Prathipati

Tripathi

et al. 2017

Nucleic Acids Research

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DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.

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“…Machine learning algorithms were employed to construct models to predict DNA-binding proteins and produced effective performances [4–9,11–19]. Interestingly, the support vector machine (SVM) algorithm has been used frequently to predict DNA-binding proteins [4–6,8,12–16]. Cai and Lin first applied the SVM algorithm for DNA-binding protein prediction using a protein’s amino acid composition and a limited range of correlations of hydrophobicity and solvent-accessible surface areas as input features [4].…”

Section: Introductionmentioning

confidence: 99%

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues

Guo

Sun

2016

PLoS ONE

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DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

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Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information

Cited by 12 publications

References 35 publications

A Survey of Computational Intelligence Techniques in Protein Function Prediction

A Survey of Computational Intelligence Techniques in Protein Function Prediction

Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues

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