Richard H. Lathrop
Richard H. Lathrop
Professor of Computer Science, University of California Irvine
Verified email at
Cited by
Cited by
Solving the multiple instance problem with axis-parallel rectangles
TG Dietterich, RH Lathrop, T Lozano-Pérez
Artificial intelligence 89 (1-2), 31-71, 1997
The protein threading problem with sequence amino acid interaction preferences is NP-complete
RH Lathrop
Protein Engineering, Design and Selection 7 (9), 1059-1068, 1994
Global optimum protein threading with gapped alignment and empirical pair score functions
RH Lathrop, TF Smith
Journal of molecular biology 255 (4), 641-665, 1996
Computational identification of a transiently open L1/S3 pocket for reactivation of mutant p53
CD Wassman, R Baronio, Ö Demir, BD Wallentine, CK Chen, LV Hall, ...
Nature communications 4 (1), 1407, 2013
Parallelism in manipulator dynamics
R Lathrop
Proceedings. 1985 IEEE International Conference on Robotics and Automation 2 …, 1985
A statistical phylogeography of influenza A H5N1
RG Wallace, HM HoDac, RH Lathrop, WM Fitch
Proceedings of the National Academy of Sciences 104 (11), 4473-4478, 2007
Proceedings: Second international conference on intelligent systems for molecular biology
R Altman, D Brutlag, P Karp, R Lathrop, D Searls
Stanford Univ., CA (United States), 1994
Consensus topography in the ATP binding site of the simian virus 40 and polyomavirus large tumor antigens.
MK Bradley, TF Smith, RH Lathrop, DM Livingston, TA Webster
Proceedings of the National Academy of Sciences 84 (12), 4026-4030, 1987
Compass: A shape-based machine learning tool for drug design
AN Jain, TG Dietterich, RH Lathrop, D Chapman, RE Critchlow, BE Bauer, ...
Journal of Computer-Aided Molecular Design 8, 635-652, 1994
Ariadne: pattern-directed inference and hierarchical abstraction in protein structure recognition
RH Lathrop, TA Webster, TF Smith
Communications of the ACM 30 (11), 909-921, 1987
Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning
SA Danziger, R Baronio, L Ho, L Hall, K Salmon, GW Hatfield, P Kaiser, ...
PLoS computational biology 5 (9), e1000498, 2009
DNA sequence and structure: direct and indirect recognition in protein-DNA binding
NR Steffen, SD Murphy, L Tolleri, GW Hatfield, RH Lathrop
Bioinformatics 18 (suppl_1), S22-S30, 2002
Information‐theoretic dissection of pairwise contact potentials
MS Cline, K Karplus, RH Lathrop, TF Smith, RG Rogers Jr, D Haussler
Proteins: Structure, Function, and Bioinformatics 49 (1), 7-14, 2002
Current limitations to protein threading approaches
TF Smith, L LO CONTE, J BIENKOWSKA, C Gaitatzes, RG ROGERS Jr, ...
Journal of Computational Biology 4 (3), 217-225, 1997
Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants
SA Danziger, J Zeng, Y Wang, RK Brachmann, RH Lathrop
Bioinformatics 23 (13), i104-i114, 2007
Functional census of mutation sequence spaces: the example of p53 cancer rescue mutants
SA Danziger, SJ Swamidass, J Zeng, LR Dearth, Q Lu, JH Chen, J Cheng, ...
IEEE/ACM transactions on computational biology and bioinformatics 3 (2), 114-125, 2006
Predicting protein structure with probabilistic models
CM Stultz, R Nambudripad, RH Lathrop, JV White
Advances in molecular and cell biology 22, 447-506, 1997
HB tag modules for PCR‐based gene tagging and tandem affinity purification in Saccharomyces cerevisiae
C Tagwerker, H Zhang, X Wang, LSZ Larsen, RH Lathrop, GW Hatfield, ...
Yeast 23 (8), 623-632, 2006
Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology
ELL Sonnhammer, G Von Heijne, A Krogh, J Glasgow, T Littlejohn, ...
AAAI Press, 1998
Ensemble-based computational approach discriminates functional activity of p53 cancer and rescue mutants
Ö Demir, R Baronio, F Salehi, CD Wassman, L Hall, GW Hatfield, ...
PLoS computational biology 7 (10), e1002238, 2011
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