- Hidden Markov Models That Use Predicted Local Structure for Fold Recognition: Alphabets of Backbone Geometry
- Evaluation of Local Structure Alphabets Based on Residue Burial
- Origami with strings: protein folding by computer
- References 19 SF86 Kenneth J. Supowit and Steven J. Friedman. A new method for verifying sequential
- Better than Chance: the importance of null models
- Model Quality Assessment using Distance Constraints from Alignments Martin Paluszewski
- SAM-T04: what's new in protein-structure prediction for CASP6 Kevin Karplus
- Predicting protein structure using hidden Markov models Kevin Karplusy
- Sequence Comparisons Using Multiple Sequences Detect Three Times as Many Remote Homologues as
- Combining local-structure, fold-recognition, and new-fold methods for protein structure prediction
- Logic Minimization using Two-column Rectangle Replacement*
- Regularizers for Estimating Distributions of
- Xtmap: Generate-and-Test Mapper for Table-Lookup Gate Arrays Kevin Karplus*
- References 21 GJ79 Michael R. Garey and David S. Johnson. Computers and Intractability, A Guide to the
- Unifying secondary-structure, fold-recognition, and new-fold
- Session F2B BIOINFORMATICS
- Predicting Protein Structure using only Sequence Information This is a preprint of an article accepted for publication in Proteins: Structure, Function, and Genetics copyright 1999.
- HMMs and secondary structure (OSU) Using Hidden Markov Models to Recognize Protein Folds
- Predicting protein structure using hidden Markov models
- Using the ucsc-report LaTEX style le
- http://www.jstor.org Digital Synthesis of Plucked-String and Drum Timbres
- Fig. 1. The STR alphabet subdivides the letter E from DSSP into P,A,M for strands in the middle of a (parallel, anti-parallel, or mixed) sheet, and Q,Z for
- SAM-T04: What Is New in ProteinStructure Prediction Kevin Karplus,* Sol Katzman, George Shackleford, Martina Koeva, Jenny Draper, Bret Barnes, Marcia Soriano,
- Estimating statistical significance with reverse-sequence null models
- A protocol for evaluating local structure alphabets
- Information-Theoretic Dissection of Pairwise Contact Melissa S. Cline,1* Kevin Karplus,2
- A COMPILER-DRIVEN SUPERCOMPUTER KEVIN KARPLUS AND ALEXANDRU NICOLAU'
- EFFICIENT HARDWARE FOR MULTI-WAY JUMPS AND PREFeTCHES Kevin Karplus and Alexandru Nicolau
- PREDICTIONS FROM AUTOMATIC SERVERS CAFASP-1: Critical Assessment of Fully Automated
- Bioinformatics Methods http://users.soe.ucsc.edu/~karplus/papers/
- Mutation Research 485 (2001) 2336 Nucleotide excision repair "a legacy of creativity"
- Better than Chance: the importance of null models
- Protein Packing Quality Using Delaunay Complexes Rasmus Fonseca
- SAM-T08, HMM-based Protein Structure Prediction Kevin Karplus
- Journal of VLSI Signal Processing 19, 115126 (1998) c 1998 Kluwer Academic Publishers. Manufactured in The Netherlands.
- Applying Undertaker to Quality Assessment John G. Archie, Martin Paluszewski, and Kevin Karplus
- Amap: a Technology Mapper for Selector-based Field-Programmable Gate Arrays
- FoldRecognitionusingHiddenMarkov ModelsandSecondaryStructure
- Statistical Significance for Reverse-sequence MM2001 Estimating Statistical Significance for Reverse-sequence Null Models
- Scoring Hidden Markov Models Christian Barrett Richard Hughey Kevin Karplus
- 898 IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN. VOL. 9. NO. 8. AUGUST 1990 H. Fujiwara. T. Shimono. "On the acceleration of test generation al-
- Xmap: a Technology Mapper for Table-lookup Field-Programmable Gate Arrays
- Is there any point to human prediction? Kevin Karplus
- Karplus lab: protein structure prediction and design
- Better than Chance: the importance of null models
- Karplus lab: protein structure prediction and design
- Contact Prediction using Mutual Information and Neural Nets George Shackelford
- original les optimized le xcmap xmap xtmap cpu xcmap xmap xtmap cpu
- A PROOF OF THE ISOMORPHISM OF wxyz-TRANSFORMALS AND 2 x 2 INTEGER MATRICES
- FINDING MINIMAL PERFECT HASH FUNCTIONS Gary Haggard
- Methods of translating NMR proton distances into their corresponding heavy atom distances
- Protein folding: not just another optimization
- The Delta RuleDevelopmentSystemfor Speech Synthesis from Text
- Tech Writing Quick Review BME/CMPE/EE 123A
- Using Markov Models and Hidden Markov Models to Find Repetitive
- BIOINFORMATICS Vol. 17 no. 0 2001
- Oxford University Press 2009 1 Pokefind: a novel topological filter for use with protein structure
- Computers Chem. Vol. 20. No. 1. m. S23. 1996 Copyright 0 1996 El&& S&&e Ltd
- Bioinformatics Methods Kevin Karplus
- The UCSC Kestrel Parallel Processor Andrea Di Blas, Member, IEEE, David M. Dahle, Mark Diekhans, Leslie Grate, Jeffrey Hirschberg,
- Kestrel: A Programmable Array for Sequence Analysis Jeffrey D. Hirschberg Richard Hughey Kevin Karplus
- Dirichlet Mixtures: A Method for Improved Detection of Weak but Signi cant Protein Sequence Homology
- HMMs and secondary structure Fold Recognition using Hidden Markov Models and Secondary
- Origami with strings: protein folding by computer
- Origami with strings: predicting how proteins fold
- Kestrel: Design of an %bit SIMD parallel processor David M.Dahle Jeffrey D. Hirschherg Kevin Karplus Hansjorg Keller
- References 17 Xmap including merge Amap XAmap subsets
- Discrete Applied Mathematics 33 (1991) 109428 North-Holland
- Better than Chance: the importance of null models
- On alignment shift and its measures Melissa Cline
- Origami with strings: protein folding by computer
