Sj#olander, K., Karplus, K., Brown, M. P., Hughey, R., Krogh, A., Mian, I. S., & Haussler, D. #1996#. Dirichlet mixtures: A method for improving detection of weak but signi#cant protein sequence homology. CABIOS, 12 #4#, 327#345. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Classifying G-Protein Coupled Receptors With Support Vector.. - Karchin (2000) (Correct)
....and components have been estimated by studying large databases of protein sequences and observing which amino acid distributions are likely to occur at a given position in a protein. The University of California, Santa Cruz Computational Biology group maintains a library of these mixtures [52]. The Fisher score vector for sequence X given model M , has l components per match state in M , and each component represents a subclass of amino acids. In my experiments, a mixture of estimated frequencies for nine subclasses of amino acids, uprior.9comp written by Kevin Karplus, was used to ....
K. Sjolander, K. Karplus, M. P. Brown, R. Hughey, A. Krogh, I. S. Mian, and D. Haus- 94 sler. Dirichlet Mixtures: A Method for Improving Detection of Weak but Signicant Protein Sequence Homology. CABIOS, 12(4):327-345, August 1996.
Hidden Markov Models for Detecting Remote Protein Homologies - Karplus, Barrett, Hughey (1999) (29 citations) Self-citation (Karplus Hughey) (Correct)
No context found.
Sj#olander, K., Karplus, K., Brown, M. P., Hughey, R., Krogh, A., Mian, I. S., & Haussler, D. #1996#. Dirichlet mixtures: A method for improving detection of weak but signi#cant protein sequence homology. CABIOS, 12 #4#, 327#345.
Unknown - Self-citation (Karplus Haussler) (Correct)
No context found.
K. Sjolander, K. Karplus, M. P. Brown, R. Hughey, A. Krogh, I. S. Mian, and D. Haussler. Dirichlet mixtures: A method for improving detection of weak but signi cant protein sequence homology. 12(4):327-345, August 1996.
Building and using an HMM framework for finding protein.. - Cline, Barrett, Karplus Self-citation (Sj Karplus Hughey Krogh Haussler) (Correct)
No context found.
K. Sjolander, K. Karplus, M. P. Brown, R. Hughey, A. Krogh, I. S. Mian, and D. Haussler. Dirichlet mixtures: A method for improving detection of weak but signi cant protein sequence homology. CABIOS, 12(4):327-345, August 1996.
Predicting Protein Structure using only Sequence.. - Karplus, Barrett.. (1999) (6 citations) Self-citation (Karplus Hughey) (Correct)
....proteins in database search [6] Our hmm fold recognition method di ers from protein threading methods [10, 19, 14, 15] in that pairwise interactions are not modeled or used. Instead, we employ Bayesian methods [3, 2, 17] to incorporate prior information in the form of Dirichlet mixture densities [20] over positionspeci c amino acid distributions. The components of the mixture re ect di erent patterns of sequence conservation and can be combined with data from aligned homologs to form data dependent estimates of amino acid probabilities. In the CASP3 experiments, we used the recently developed ....
....with the original hmm. This novel null model cancels out arti cially strong scores due to length and composition biases and more subtle sources such as conserved rare residues and long helices. 3. Re estimate the hmm with these sequences, using sequence weighting and Dirichlet mixture priors [20]. 4. Re align the training set using the re trained hmm. This multiple alignment is used an input to the rst step in the next iteration. During each round of iteration, the score threshold in step 2 is made less stringent in order to capture less similar sequences that are still, we hope, ....
K. Sjolander, K. Karplus, M. P. Brown, R. Hughey, A. Krogh, I. S. Mian, and D. Haussler. Dirichlet mixtures: A method for improving detection of weak but signi cant protein sequence homology. CABIOS, 12(4):327{ 345, Aug. 1996.
Investigation Of Non-Pairwise Protein Structure Score Functions.. - Barrett (2001) Self-citation (Karplus Haussler) (Correct)
....alignment. This is problematic because the observed amino acid frequencies most likely represent a poor estimate of the true amino acid probability distribution. Mixtures of Dirichlet densities represent a method to infer better estimates of amino acid probabilities from few observations [88]. In the context of protein sequence analysis, the densities represent prior probabilities over amino acid distributions. They are intended to be combined with observed amino acid counts in a column, c, of a multiple sequence alignment to arrive at a posterior probability distribution over the ....
K. Sjolander, K. Karplus, M. P. Brown, R. Hughey, A. Krogh, I. S. Mian, and D. Haussler. Dirichlet mixtures: A method for improving detection of weak but signicant protein sequence homology. 12(4):327-345, August 1996.
BiTAM: Bilingual Topic AdMixture Models for Word Alignment - Bing Zhao And (Correct)
No context found.
K. Sj olander, K. Karplus, M. Brown, R. Hughey, A. Krogh, I.S. Mian, and D. Haussler. 1996. Dirichlet mixtures: A method for improving detection of weak but significant protein sequence homology. Computer Applications in the Biosciences, 12.
Profile Hidden Markov Dirichlet-Multinomial Models for Motif.. - Xing (Correct)
No context found.
K. Sjolander, K. Karplus, M. Brown, R. Hughey, A. Krogh, I.S. Mian, and D. Haussler. Dirichlet mixtures: A method for improving detection of weak but signi cant protein sequence homology. Computer Applications in the Biosciences, 12, 1996.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC