基本信息
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Career Trajectory
Bio
Kevin Karplus taught at Cornell University for four years before joining the UCSC faculty. At UCSC, his research interests changed from VLSI CAD tools to bioinformatics. Work as part of the UCSC bioinformatics group includes protein-structure prediction, evaluating methods for estimating distributions of amino acids given just a few instances from the distribution, and design of a SIMD parallel computer that is particularly good for sequence alignment and linear HMM training ( Kestrel ).
Since 1993 he has worked in bioinformatics---analyzing the vast databases created by high-throughput biological experimentation, such as the human genome project. He has used several techniques from machine learning and Bayesian statistics, including hidden Markov models (HMMs), mixtures of Dirichlet priors, and neural networks.
Most recently, he has concentrated on automatic techniques for finding remotely related proteins using sequence information to build HMMs. These techniques have been applied to protein structure prediction, and he lead teams participating in the Critical Assessment of Structure Prediction experiments ( CASP2 1996, CASP3 1998, and CASP4 2000). The group did quite well in these blind prediction experiments in all three attempts (in the top 6 groups worldwide for fold recognition), and hope to continue to do well this year.
Since 1993 he has worked in bioinformatics---analyzing the vast databases created by high-throughput biological experimentation, such as the human genome project. He has used several techniques from machine learning and Bayesian statistics, including hidden Markov models (HMMs), mixtures of Dirichlet priors, and neural networks.
Most recently, he has concentrated on automatic techniques for finding remotely related proteins using sequence information to build HMMs. These techniques have been applied to protein structure prediction, and he lead teams participating in the Critical Assessment of Structure Prediction experiments ( CASP2 1996, CASP3 1998, and CASP4 2000). The group did quite well in these blind prediction experiments in all three attempts (in the top 6 groups worldwide for fold recognition), and hope to continue to do well this year.
Research Interests
Papers共 114 篇Author StatisticsCo-AuthorSimilar Experts
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mBiono. 6 (2023): e0210523-e0210523
mag(2015)
BIOINFORMATICSno. 12 (2015): 1897-1903
Voronoi Diagrams in Science and Engineeringpp.117-122E, (2011)
Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature (2011)
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