ArticleBioinformation2013
SVM based model generation for binding site prediction on helix turn helix motif type of transcription factors in eukaryotes.
Article in Bioinformation, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins.Advances in bioinformatics · 2016Article
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4 authors.
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Abstract
Support vector machine is a class of machine learning algorithms which uses a set of related supervised learning methods for classification and regression. Nowadays this method is vividly applied to many detection problems related with secondary structure, tumor cell and binding residue prediction. In this work, support vector machines (SVMs) have been trained on 90 sequences of transcription factors with HTH motif. Four sequence features were used as attribute for the prediction of interaction site in HTH motif. A web page was also developed so that user can easily enter the protein sequence and receive the output as interaction site predicted or not predicted. The generated model shows a very high amount of accuracy, sensitivity and specificity which proves to be a good model for the selected case.
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