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An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis

Chuanxin Zou, Jiayu Gong, Honglin Li
2013 BMC Bioinformatics  
Conclusions: The good results suggest that it can efficiently develop an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict  ...  Results: In this work, the focus is how to transform these informative features into uniform numeric representation appropriately and improve the prediction accuracy of our SVM-based classifier for DNA-BPs  ...  We propose a novel method for predicting DNA-BPs using the SVM algorithm in conjunction with comprehensive feature analysis based on protein sequence.  ... 
doi:10.1186/1471-2105-14-90 pmid:23497329 pmcid:PMC3602657 fatcat:6spnbrgrnrc2vccm4evjie3pbm

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

Ruifeng Xu, Jiyun Zhou, Hongpeng Wang, Yulan He, Xiaolong Wang, Bin Liu
2015 BMC Systems Biology  
Conclusions:: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins  ...  However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions.  ...  For example, use the protein 1IGN chain B as a query sequence, you will see on your screen that the predictive result is "DNA-binding protein".  ... 
doi:10.1186/1752-0509-9-s1-s10 pmid:25708928 pmcid:PMC4331676 fatcat:x6dvikx52ndtloc563osetzgtm

Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions

Nitin Bhardwaj, Hui Lu
2007 FEBS Letters  
In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction  ...  We use a support vector machines (SVM)based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues.  ...  A few computational protocols have been developed for automated identification of DNA-binding residues based on the features derived from sequence and structure collectively and those from sequence alone  ... 
doi:10.1016/j.febslet.2007.01.086 pmid:17316627 pmcid:PMC1993824 fatcat:vn2t24mulrepfifzkf6g3dfdqu

Prediction of DNA-binding residues from sequence

Yanay Ofran, Venkatesh Mysore, Burkhard Rost
2007 Computer applications in the biosciences : CABIOS  
Motivation: Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown.  ...  If the 3D structure of a protein is known, it is often possible to predict DNA-binding sites in silico. However, for most proteins, such knowledge is not available.  ...  We thank Jinfeng Liu, Andrew Kernytsky and Michael Honig (Columbia University) for help with computers and databases.  ... 
doi:10.1093/bioinformatics/btm174 pmid:17646316 fatcat:ehc3xdoiq5gjtfpg2lm77nb6qq

DNABind: A hybrid algorithm for structure-based prediction of DNA-binding residues by combining machine learning- and template-based approaches

Rong Liu, Jianjun Hu
2013 Proteins: Structure, Function, and Bioinformatics  
Xiong et al. 9 integrated SVM with four features including sequence profile, solvent accessibility, packing density, and pK a value to recognize DNA-binding residues.  ...  to conduct structural and functional analysis on the interfaces between proteins and DNA with computational methods.  ...  of each residue in a protein sequence. 12 Machine learning-based prediction protocol Here a machine learning-based algorithm was proposed for the prediction of DNA-binding residues, where the Support  ... 
doi:10.1002/prot.24330 pmid:23737141 fatcat:znqcqtyoz5f6flhqhnr5h6fvhq

Bridging protein local structures and protein functions

Zhi-Ping Liu, Ling-Yun Wu, Yong Wang, Xiang-Sun Zhang, Luonan Chen
2008 Amino Acids  
of sequence and structure data.  ...  One of the major goals of molecular and evolutionary biology is to understand the functions of proteins by extracting functional information from protein sequences, structures and interactions.  ...  The authors are grateful to the anonymous referees as well as editors for comments and for helping to improve the earlier version.  ... 
doi:10.1007/s00726-008-0088-8 pmid:18421562 fatcat:micrifjcrfetnafnm4fx45ouom

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

Xin Ma, Jing Guo, Xiao Sun, Bin Liu
2016 PLoS ONE  
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.  ...  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 authors would also like to thank the two anonymous reviewers for their constructive comments, which were very helpful for strengthening the presentation of this study.  ... 
doi:10.1371/journal.pone.0167345 pmid:27907159 pmcid:PMC5132331 fatcat:3oahir4dgvcz3bwvkvlyurwlnu

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

Shandar Ahmad, Philip Prathipati, Lokesh P Tripathi, Yi-An Chen, Ajay Arya, Yoichi Murakami, Kenji Mizuguchi
2017 Nucleic Acids Research  
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  ...  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  ...  ., contributed through critical discussions and helped in improving the manuscript.  ... 
doi:10.1093/nar/gkx1166 pmid:29186632 pmcid:PMC5758906 fatcat:uudle3my7rg7hneb34lmq6s4be

Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features

Sunil Kumar, Philipp Bucher
2016 BMC Bioinformatics  
DNA sequence intrinsic features such as predicted binding affinity are often not very effective in predicting in vivo site occupancy and in any case could not explain cell-type specific binding events.  ...  In this work, we use machine-learning methods to build predictive models and assess the relative importance of different sequence-intrinsic and chromatin features in the TF-to-target-site recruitment process  ...  Declarations Publication charges for this article have been funded by the Swiss Federal Institute of Technology Lausanne (EPFL).  ... 
doi:10.1186/s12859-015-0846-z pmid:26818008 pmcid:PMC4895346 fatcat:wgalgvjcgbharggldcj34remga

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

Balachandran Manavalan, Tae Hwan Shin, Gwang Lee
2017 OncoTarget  
In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal  ...  Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms.  ...  The copyright holder for this preprint (which was .  ... 
doi:10.18632/oncotarget.23099 pmid:29416743 pmcid:PMC5788611 fatcat:3eadyivd2fgltpynb7ohpxn2eu

Sequence-Based Prediction of RNA-Binding Residues in Proteins [chapter]

Rasna R. Walia, Yasser EL-Manzalawy, Vasant G. Honavar, Drena Dobbs
2016 Msphere  
Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described.  ...  We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner. 206 16  ...  RRW is currently supported by an appointment to the ARS-USDA Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between  ... 
doi:10.1007/978-1-4939-6406-2_15 pmid:27787829 pmcid:PMC5796408 fatcat:552w4ny4rvcntj3kv55phjpfii

Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection

Xin Ma, Jing Guo, Xiao Sun
2015 BioMed Research International  
High prediction accuracy and successful prediction performance suggested that our method can be a useful approach to identify RNA-binding proteins from sequence information.  ...  In this study, a highly accurate method was developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR) method, followed  ...  proposed an SVM-based predictor using a conjoint triad feature, which extracts information directly from the amino acids sequences of proteins [10] .  ... 
doi:10.1155/2015/425810 pmid:26543860 pmcid:PMC4620426 fatcat:jcp5vobu7jb77ista7dazoqtpi

Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers

Mehdi Poursheikhali Asghari, Parviz Abdolmaleki
2019 Avicenna journal of medical biotechnology  
In the current study, nine machine-learning algorithms were used to predict RNA- and DNA-binding proteins and also to discriminate between RNA-binding proteins and DNA-binding proteins.  ...  Our findings show that the prediction of nucleic acid-binding function based on these simple electrostatic features can be improved by applied classifiers.  ...  Acknowledgement We would like to thank Dr Ebrahim Barzegari Asadabadi for his useful comments.  ... 
pmid:30800250 pmcid:PMC6359699 fatcat:eaq32ciwabbpniw2czndsm4ghu

Computational challenges in modeling gene regulatory events

Abhijeet Pataskar, Vijay K. Tiwari
2016 Transcription  
This article gives an exemplified account of the current computational challenges in molecular biology.  ...  Cellular transcriptional programs driven by genetic and epigenetic mechanisms could be better understood by integrating "omics" data and subsequently modeling the gene-regulatory events.  ...  For the flanking regions of these motifs, information is extracted from DNA structure (DNAshape), sequence features, and chromatin state dynamics to build the classifying model (i.e., SVM, RBF, or Regression  ... 
doi:10.1080/21541264.2016.1204491 pmid:27390891 pmcid:PMC5066510 fatcat:x6eatpawrvg2dnr4dhivj4yqty

A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers

Frances L. Heredia, Abiel Roche-Lima, Elsie I. Parés-Matos, Eleonora Alfinito
2021 PLoS Computational Biology  
of DNA-binding sequences.  ...  Usually, 10 to 20 steps are required for SELEX to be completed. Throughout this process it is necessary to discriminate between true DNA aptamers and unspecified DNA-binding sequences.  ...  Vázquez-Quiñones, professor of the School of Sciences and Technology of the Universidad Metropolitana-Ana G. Méndez, for his comments and suggestions during the writing of this manuscript.  ... 
doi:10.1371/journal.pcbi.1009247 pmid:34343165 pmcid:PMC8362955 fatcat:cmnednyusvcxjivnxsg5ar5gmy
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