Selected article for: "cross validation and support vector machine"
Author: Amrita Banerjee; Dipannita Santra; Smarajit Maiti
Title: Energetics based epitope screening in SARS CoV-2 (COVID 19) spike glycoprotein by Immuno-informatic analysis aiming to a suitable vaccine development
  • Document date: 2020_4_5
    • ID: iy4knx7j_17
    Snippet: A tool which predicts Linear Antigenic Epitopes [28] . SVMTrip predicts the linier antigenic epitopes by feeding Support Vector Machine with the Tri-peptide similarity and Propensity scores of different pre-analyzed epitope data. Annotation of predicted epitopes was performed . CC-BY-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.or.....
    Document: A tool which predicts Linear Antigenic Epitopes [28] . SVMTrip predicts the linier antigenic epitopes by feeding Support Vector Machine with the Tri-peptide similarity and Propensity scores of different pre-analyzed epitope data. Annotation of predicted epitopes was performed . CC-BY-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.02.021725 doi: bioRxiv preprint through protein BLAST. SVMTrip have gained 80.1% sensitivity and 55.2% precision value with five fold cross-validation. For epitope prediction 20 amino acid lengths was selected.

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