publication . Article . Other literature type . 2003

Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations

Peder Worning; Søren Brunak; Søren Buus; Sanne Lise Lauemøller; Morten Nielsen; Kasper Lamberth; Ole Lund; Claus Lundegaard;
Open Access
  • Published: 01 May 2003 Journal: Protein Science, volume 12, pages 1,007-1,017 (issn: 0961-8368, eissn: 1469-896X, Copyright policy)
  • Publisher: Wiley
Abstract
In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex displ...
Subjects
free text keywords: Biochemistry, Molecular Biology, Article, Bioinformatics, Correlation, Pattern recognition, Artificial neural network, Markov chain, Epitope, Mutual information, BLOSUM, Hidden Markov model, Encoding (memory), Artificial intelligence, business.industry, business, Biology
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