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William J. Bruno

dblp:71/1294 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › phylogenetics › phylogenetic inference › statistical alignment
bayesian sequence alignment
0.012001
Evolutionary HMMs: a Bayesian approach to multiple alignment · Bioinform. 2001
Bioinformatics and computational biology
multiple sequence alignment
0.012001
Evolutionary HMMs: a Bayesian approach to multiple alignment · Bioinform. 2001
Bioinformatics and computational biology › phylogenetics
phylogenetic inference
0.012001
Evolutionary HMMs: a Bayesian approach to multiple alignment · Bioinform. 2001
Bioinformatics and computational biology
sequence alignment
0.012001
Evolutionary HMMs: a Bayesian approach to multiple alignment · Bioinform. 2001
Bioinformatics and computational biology › phylogenetics › phylogenetic inference
statistical alignment
0.012001
Evolutionary HMMs: a Bayesian approach to multiple alignment · Bioinform. 2001
Bioinformatics and computational biology › gene regulation › regulatory element discovery
regulatory element prediction
0.012000
Finding Regulatory Elements Using Joint Likelihoods for Sequence and Expression Profile Data · ISMB 2000

Methods — techniques the papers use, named apart from their topics

markov chain monte carlo · 0.0hidden markov model · 0.0bayesian inference · 0.0joint likelihood · 0.0
YearPublicationVenuePosition
2001 Evolutionary HMMs: a Bayesian approach to multiple alignment
abstract
MOTIVATION: We review proposed syntheses of probabilistic sequence alignment, profiling and phylogeny. We develop a multiple alignment algorithm for Bayesian inference in the links model proposed by Thorne et al. (1991, J. Mol. Evol., 33, 114-124). The algorithm, described in detail in Section 3, samples from and/or maximizes the posterior distribution over multiple alignments for any number of DNA or protein sequences, conditioned on a phylogenetic tree. The individual sampling and maximization steps of the algorithm require no more computational resources than pairwise alignment. METHODS: We present a software implementation (Handel) of our algorithm and report test results on (i) simulated data sets and (ii) the structurally informed protein alignments of BAliBASE (Thompson et al., 1999, Nucleic Acids Res., 27, 2682-2690). RESULTS: We find that the mean sum-of-pairs score (a measure of residue-pair correspondence) for the BAliBASE alignments is only 13% lower for Handelthan for CLUSTALW(Thompson et al., 1994, Nucleic Acids Res., 22, 4673-4680), despite the relative simplicity of the links model (CLUSTALW uses affine gap scores and increased penalties for indels in hydrophobic regions). With reference to these benchmarks, we discuss potential improvements to the links model and implications for Bayesian multiple alignment and phylogenetic profiling. AVAILABILITY: The source code to Handelis freely distributed on the Internet at http://www.biowiki.org/Handel under the terms of the GNU Public License (GPL, 2000, http://www.fsf.org./copyleft/gpl.html).
Ian Holmes, William J. Bruno
Bioinform.2
2000 Finding Regulatory Elements Using Joint Likelihoods for Sequence and Expression Profile Data
Ian Holmes, William J. Bruno
ISMB2
1998 Non-adaptive Group Testing in the Presence of Errors
Emanuel Knill, William J. Bruno, David C. Torney
Discret. Appl. Math.2