Machine learning competition in immunology - Prediction of HLA class I binding peptides

Journal of Immunological Methods

Volumn 374, Issue 1-2, 2011, Pages 1-4

  Zhang, Guang Lan ^a   Ansari, Hifzur Rahman ^b   Bradley, Phil ^c   Cawley, Gavin C ^d   Hertz, Tomer ^e   Hu, Xihao ^f   Jojic, Nebojsa ^g   Kim, Yohan ^h   Kohlbacher, Oliver ⁱ   Lund, Ole ⁱ   Lundegaard, Claus ⁱ   Magaret, Craig A ^c   Nielsen, Morten ⁱ   Papadopoulos, Harris ^j   Raghava, G P S ^b   Tal, Vider Shalit ^k   Xue, Li C ^l   Yanover, Chen ^c   Zhu, Shanfeng ^e   Rock, Michael T ^m   Crowe, James E ^m   Panayiotou, Christos ⁿ   Polycarpou, Marios M ⁿ   Duch, Włodzisław ^o,p   Brusic, Vladimir ^a   more..

^a DANA FARBER CANCER INSTITUTE   (United States)

^b INSTITUTE OF MICROBIAL TECHNOLOGY   (India)

^c Fred Hutchinson Cancer Research Center   (United States)

^d UNIVERSITY OF EAST ANGLIA   (United Kingdom)

^e FUDAN UNIVERSITY   (China)

^f MICROSOFT RESEARCH   (United States)

^g LA JOLLA INSTITUTE FOR ALLERGY AND IMMUNOLOGY   (United States)

^h UNIVERSITY OF TÜBINGEN   (Germany)

ⁱ TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^j FREDERICK UNIVERSITY   (Cyprus)

^k BAR ILAN UNIVERSITY   (Israel)

^l IOWA STATE UNIVERSITY   (United States)

^m VANDERBILT UNIVERSITY   (United States)

ⁿ UNIVERSITY OF CYPRUS   (Cyprus)

^o NICOLAUS COPERNICUS UNIVERSITY   (Poland)

^p NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

more..

Author keywords

Computational models; HLA; Human leukocyte antigen; Machine learning; Peptide binding; Predictions

Indexed keywords

BINDING PROTEIN; HLA A1 ANTIGEN; HLA ANTIGEN CLASS 1;

ALGORITHM; AUTOIMMUNITY; BINDING AFFINITY; EDITORIAL; HIGH THROUGHPUT SCREENING; HLA MATCHING; IMMUNOAFFINITY CHROMATOGRAPHY; MACHINE LEARNING; MAJOR HISTOCOMPATIBILITY COMPLEX; MASS SPECTROMETRY; PRIORITY JOURNAL; PROTEIN ANALYSIS; QUALITY CONTROL; T LYMPHOCYTE;

ALGORITHMS; ALLERGY AND IMMUNOLOGY; ARTIFICIAL INTELLIGENCE; HISTOCOMPATIBILITY ANTIGENS CLASS I; HUMANS; PEPTIDES; PROTEIN BINDING;

EID: 81255157774     PISSN: 00221759     EISSN: 18727905     Source Type: Journal    
DOI: 10.1016/j.jim.2011.09.010     Document Type: Editorial

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