Phil Jeffrey

dblp:162/5711 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · unresolved

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

Applied, interdisciplinary, general and emerging computing · 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
1 paper
Bioinformatics and computational biology · 77% Medical and health informatics · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
comparative genomics
0.212015
ADME SARfari: comparative genomics of drug metabolizing systems · Bioinform. 2015
Medical and health informatics
pharmacokinetics
0.112015
ADME SARfari: comparative genomics of drug metabolizing systems · Bioinform. 2015

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

predictive modeling · 0.2data integration · 0.2
YearPublicationVenuePosition
2015 ADME SARfari: comparative genomics of drug metabolizing systems
abstract
MOTIVATION: ADME SARfari is a freely available web resource that enables comparative analyses of drug-disposition genes. It does so by integrating a number of publicly available data sources, which have subsequently been used to build data mining services, predictive tools and visualizations for drug metabolism researchers. The data include the interactions of small molecules with ADME (absorption, distribution, metabolism and excretion) proteins responsible for the metabolism and transport of molecules; available pharmacokinetic (PK) data; protein sequences of ADME-related molecular targets for pre-clinical model species and human; alignments of the orthologues including information on known SNPs (Single Nucleotide Polymorphism) and information on the tissue distribution of these proteins. In addition, in silico models have been developed, which enable users to predict which ADME relevant protein targets a novel compound is likely to interact with.
Mark Davies, Nathan Dedman, Anne Hersey, George Papadatos, Matthew D. Hall, Lourdes Cucurull-Sanchez, Phil Jeffrey, Samiul Hasan, Peter J. Eddershaw, John P. Overington
Bioinform.7