Journal ArticleDOI
Protein Folds Prediction with Hierarchical Structured SVM
Dapeng Li,Ying Ju,Quan Zou +2 more
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This article is published in Current Proteomics.The article was published on 2016-05-31. It has received 112 citations till now. The article focuses on the topics: Structured support vector machine.read more
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Sequence clustering in bioinformatics: an empirical study.
TL;DR: This review selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets to assist bioinformatics users in employing suitable clustering tool effectively to analyze big sequencing data.
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iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators.
TL;DR: A new predictor based on support vector machine to identify transcription terminators based on pseudo k-tuple nucleotide composition (PseKNC) that could become a powerful tool for bacterial terminator recognition.
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Machine Learning for Drug-Target Interaction Prediction
TL;DR: A hierarchical classification scheme is adopted and several representative methods of each category of drug-target interaction prediction are introduced, especially the recent state-of-the-art methods.
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Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species.
TL;DR: This study proposes a machine learning based predictor, namely 4mcPred‐SVM, for the genome‐wide detection of DNA 4mC sites, and presents a new feature representation algorithm that sufficiently exploits sequence‐based information.
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iRNA-2OM: A Sequence-Based Predictor for Identifying 2'-O-Methylation Sites in Homo sapiens.
TL;DR: This study proposed a support vector machine-based model to predict 2'-O-methylation sites in H. sapiens, and the RNA sequences were encoded with the optimal features obtained from feature selection.