Demo Data from VLDB 2026 / reviewers in the wild / expert
Spencer V. Muse
dblp:54/983
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2010
0000-0003-0104-0728ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 3
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 3 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
molecular evolution |
0.1 | 1 | 2005 | HyPhy: hypothesis testing using phylogenies · Bioinform. 2005 |
Bioinformatics and computational biology › phylogenetics › statistical phylogenetics
phylogenetic hypothesis testing |
0.1 | 1 | 2005 | HyPhy: hypothesis testing using phylogenies · Bioinform. 2005 |
Bioinformatics and computational biology › molecular evolution
sequence evolution |
0.0 | 1 | 2005 | HyPhy: hypothesis testing using phylogenies · Bioinform. 2005 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 0.1maximum likelihood · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | CodonTest: Modeling Amino Acid Substitution Preferences in Coding SequencesabstractCodon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes. Wayne Delport, Konrad Scheffler, Gordon Botha, Michael B. Gravenor, Spencer V. Muse, Sergei L. Kosakovsky Pond |
PLoS Comput. Biol. | 5 |
| 2005 | PowerMarker: an integrated analysis environment for genetic marker analysisabstractSUMMARY: PowerMarker delivers a data-driven, integrated analysis environment (IAE) for genetic data. The IAE integrates data management, analysis and visualization in a user-friendly graphical user interface. It accelerates the analysis lifecycle and enables users to maintain data integrity throughout the process. An ever-growing list of more than 50 different statistical analyses for genetic markers has been implemented in PowerMarker. AVAILABILITY: www.powermarker.net Kejun Liu, Spencer V. Muse |
Bioinform. | 2 |
| 2005 | HyPhy: hypothesis testing using phylogeniesabstractUNLABELLED: The HyPhypackage is designed to provide a flexible and unified platform for carrying out likelihood-based analyses on multiple alignments of molecular sequence data, with the emphasis on studies of rates and patterns of sequence evolution. AVAILABILITY: http://www.hyphy.org CONTACT: [email protected] SUPPLEMENTARY INFORMATION: HyPhydocumentation and tutorials are available at http://www.hyphy.org. Sergei L. Kosakovsky Pond, Simon D. W. Frost, Spencer V. Muse |
Bioinform. | 3 |