Feature selection using a one dimensional naïve Bayes’ classifier increases the accuracy of support vector machine classification of CDR3 repertoires | Zendy

Mattia Cinelli | Zendy; Yuxin Sun | Zendy; Katharine Best | Zendy; James Heather | Zendy; Shlomit Reich-Zeliger | Zendy; Eric Shifrut | Zendy; Nir Friedman | Zendy; John ShaweTaylor | Zendy; Benny Chain | Zendy

Open Access

Feature selection using a one dimensional naïve Bayes’ classifier increases the accuracy of support vector machine classification of CDR3 repertoires

Author(s) -

Mattia Cinelli,

Yuxin Sun,

Katharine Best,

James Heather,

Shlomit Reich-Zeliger,

Eric Shifrut,

Nir Friedman,

John ShaweTaylor,

Benny Chain

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw771

Subject(s) - support vector machine , computer science , naive bayes classifier , feature selection , computational biology , artificial intelligence , classifier (uml) , biology

Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund's adjuvant (CFA) or CFA alone.

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