Honghuang Lin to Algorithms
This is a "connection" page, showing publications Honghuang Lin has written about Algorithms.
Connection Strength
0.459
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Lin HH, Han LY, Zhang HL, Zheng CJ, Xie B, Cao ZW, Chen YZ. Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach. BMC Bioinformatics. 2006 Dec 18; 7 Suppl 5:S13.
Score: 0.185
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Lin HH, Zhang GL, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics. 2008 Dec 12; 9 Suppl 12:S22.
Score: 0.053
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Ong SA, Lin HH, Chen YZ, Li ZR, Cao Z. Efficacy of different protein descriptors in predicting protein functional families. BMC Bioinformatics. 2007 Aug 17; 8:300.
Score: 0.049
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Lin HH, Han LY, Zhang HL, Zheng CJ, Xie B, Chen YZ. Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity. J Lipid Res. 2006 Apr; 47(4):824-31.
Score: 0.044
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Posner DC, Lin H, Meigs JB, Kolaczyk ED, Dupuis J. Convex combination sequence kernel association test for rare-variant studies. Genet Epidemiol. 2020 06; 44(4):352-367.
Score: 0.029
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Naj AC, Lin H, Vardarajan BN, White S, Lancour D, Ma Y, Schmidt M, Sun F, Butkiewicz M, Bush WS, Kunkle BW, Malamon J, Amin N, Choi SH, Hamilton-Nelson KL, van der Lee SJ, Gupta N, Koboldt DC, Saad M, Wang B, Nato AQ, Sohi HK, Kuzma A, Wang LS, Cupples LA, van Duijn C, Seshadri S, Schellenberg GD, Boerwinkle E, Bis JC, Dupuis J, Salerno WJ, Wijsman EM, Martin ER, DeStefano AL. Quality control and integration of genotypes from two calling pipelines for whole genome sequence data in the Alzheimer's disease sequencing project. Genomics. 2019 07; 111(4):808-818.
Score: 0.026
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Zhang GL, Lin HH, Keskin DB, Reinherz EL, Brusic V. Dana-Farber repository for machine learning in immunology. J Immunol Methods. 2011 Nov 30; 374(1-2):18-25.
Score: 0.016
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Zhu F, Han LY, Chen X, Lin HH, Ong S, Xie B, Zhang HL, Chen YZ. Homology-free prediction of functional class of proteins and peptides by support vector machines. Curr Protein Pept Sci. 2008 Feb; 9(1):70-95.
Score: 0.013
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Han LY, Zheng CJ, Xie B, Jia J, Ma XH, Zhu F, Lin HH, Chen X, Chen YZ. Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. Drug Discov Today. 2007 Apr; 12(7-8):304-13.
Score: 0.012
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Cui J, Han LY, Lin HH, Tang ZQ, Jiang L, Cao ZW, Chen YZ. MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties. Immunogenetics. 2006 Aug; 58(8):607-13.
Score: 0.011
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Han L, Cui J, Lin H, Ji Z, Cao Z, Li Y, Chen Y. Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity. Proteomics. 2006 Jul; 6(14):4023-37.
Score: 0.011
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Li ZR, Lin HH, Han LY, Jiang L, Chen X, Chen YZ. PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res. 2006 Jul 01; 34(Web Server issue):W32-7.
Score: 0.011