Searching for B Cell Epitope Prediction Using Support Vector Machine Tool information? Find all needed info by using official links provided below.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5227168/
Dec 29, 2016 · B-cell epitope prediction using support vector machine tool (BEST) is sequence-based tool designed for prediction of both linear and conformational epitopes from full antigen sequence. Prediction is based on averaging of selected scores (sequence conservation, similarity to experimentally validated B-cell epitopes, predicted secondary structure ...Cited by: 63
https://omictools.com/best-2-tool
Predicts epitopes from full protein chains using an approach based on averaging selected scores generated from 20-mers by a support vector machine (SVM)-based predictor. BEST uses a sliding window to represent the input antigen chain as a set of 20-mers. It then combines predictions from the SVM using a custom-designed scheme that outputs the propensity of each amino acid (AA) to form of a B ...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384636/
Our BEST (B-cell Epitope prediction using Support vector machine Tool) method predicts epitopes from antigen sequences, in contrast to some method that predict only from short sequence fragments, using a new architecture based on averaging selected scores generated from sliding 20-mers by a Support Vector Machine (SVM).Cited by: 65
http://biomine.cs.vcu.edu/datasets/BEST/
BEST: B-cell Epitope prediction using Support vector machine Tool This web page provides datasets and prediction model associated with: Gao J, Faraggi E, Zhou Y, Ruan J, Kurgan L. BEST: improved prediction of B-cell epitopes from antigen sequences.PLoS One. 2012; 7(6):e40104
https://pdfs.semanticscholar.org/105c/9bc428d82f3e02747ca8698c70f43a12eca1.pdf
research. We overview the available approaches for the prediction of B-cell epitopes and propose a novel and accurate sequence-based solution. Our BEST (B-cell Epitope prediction using Support vector machine Tool) method predicts epitopes from antigen sequences, in contrast to some method that predict only from short sequence fragments, using a new
https://www.hindawi.com/journals/bmri/2011/432830/
Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification.Cited by: 73
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