id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-344691-vmc0rrrk	Srinivasan, K.N.	Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens	2004-08-04		.txt	text/plain	3654	184	52	Results: Our predictions against experimental data from four melanoma-related proteins showed that MULTIPRED ANN and HMM models could predict T-cell epitopes with high accuracy. A number of predictive methods for MHC classes I and II binding peptides are available, including those based on binding motifs (Rammensee et al., 1995) , quantitative matrices (Parker et al., 1994) , artificial neural networks (ANNs) , hidden Markov models (HMMs) (Mamitsuka, 1998) , multivariate statistical approaches (Guan et al., 2003) , support vector machines (Zhao et al., 2003) and decision trees (Savoie et al., 1999) . In our prediction of promiscuous class I T-cell epitopes, we made predictions of T-cell epitope hot spots in nucleocapsid protein of the severe acute respiratory syndrome coronavirus (SARS-CoV). MULTIPRED, a computational system developed for human leukocyte antigen (HLA) classes I-A2 and I-A3 binding, predicts individual 9-mer T-cell epitopes and also promiscuous class I regions as immunological hot spots, based on HMM and ANN models (Zhang et al., 2003) .	./cache/cord-344691-vmc0rrrk.txt	./txt/cord-344691-vmc0rrrk.txt