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Edward A. Sausville

dblp:69/6683 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none

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 · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
cancer genomics
0.012003
Linking the growth inhibition response from the National Cancer Institute's anticancer screen to gene expression levels and other molecular target data · Bioinform. 2003
Bioinformatics and computational biology › drug discovery › drug response prediction
drug sensitivity prediction
0.012003
Linking the growth inhibition response from the National Cancer Institute's anticancer screen to gene expression levels and other molecular target data · Bioinform. 2003
Bioinformatics and computational biology › gene expression analysis › gene co-expression analysis
gene expression correlation
0.012003
Linking the growth inhibition response from the National Cancer Institute's anticancer screen to gene expression levels and other molecular target data · Bioinform. 2003

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

data mining · 0.0correlation analysis · 0.0
YearPublicationVenuePosition
2003 Linking the growth inhibition response from the National Cancer Institute's anticancer screen to gene expression levels and other molecular target data
abstract
MOTIVATION: Data mining tools are proposed to establish mechanistic connections between chemotypes and specific cellular functions. Drawing on a previous study that classified the cellular response patterns of growth inhibition measurements log( GI(50)) from the National Cancer Institute's (NCI's) anticancer screen, we have examined additional data for mRNA expression, sets of known molecular targets and mutational status against these same tumor cell lines to relate chemosensitivity more precisely to biochemical pathways. RESULTS: Our analysis finds that gene expression levels do not, in general, correlate with log(GI(50)) measurements, instead they reflect a generic toxic condition. Within the remaining set of non-generic conditions, examples were found where a correlation suggesting a biochemical basis for cellular cytotoxicity could be supported. These included reconfirmation of previously observed associations between mutant and wild-type status of p53, and chemosensitivity to alkylating agents, while extending these results to reveal associations with gamma-induced expressions of MDM2, WAF1 and GADD45, signals that were not apparent in measurements of basal mRNA expression levels for any of these genes. Additional examinations revealed that mRNA expression levels directly correlated with paclitaxel chemosensitivity to mitosis, while also identifying additional chemotypes as P-glycoprotein substrates. Our analysis revealed well-known direct associations between p16 mutant status and chemotypes implicated in cell cycle control, and extended these results to include expression levels for three additional tyrosine kinase proteins (TEK, transgelin and hCdc4). Links were also found that suggested associations between chemosensitivity and the endocrine, paracrine ligand-receptor loops, via expression of the adrenergic receptor, calcium second messenger pathways via expression levels of carbonic anhydrase and cellular communication pathways via fibrillin.
Anders Wallqvist, Alfred A. Rabow, Robert H. Shoemaker, Edward A. Sausville, David G. Covell
Bioinform.4