Demo Data from VLDB 2026 / reviewers in the wild / expert
Inna Golovchikova
dblp:98/94
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
—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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
cancer genomics |
0.1 | 1 | 2008 | Successful anti-cancer drug targets able to pass FDA review demonstrate the identifiable signature distinct from the signatures of random genes and initially proposed targets · Bioinform. 2008 |
Bioinformatics and computational biology
drug discovery |
0.1 | 1 | 2008 | Successful anti-cancer drug targets able to pass FDA review demonstrate the identifiable signature distinct from the signatures of random genes and initially proposed targets · Bioinform. 2008 |
Bioinformatics and computational biology › drug discovery › target identification
drug target prioritization |
0.1 | 1 | 2008 | Successful anti-cancer drug targets able to pass FDA review demonstrate the identifiable signature distinct from the signatures of random genes and initially proposed targets · Bioinform. 2008 |
Methods — techniques the papers use, named apart from their topics
linear classifier · 0.1enrichment scoring · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Successful anti-cancer drug targets able to pass FDA review demonstrate the identifiable signature distinct from the signatures of random genes and initially proposed targetsabstractMOTIVATION: New efforts to guide and prioritize the selection of cancer drug targets are urgently needed, as is evident by the slow development of novel anti-cancer agents and the narrow therapeutic index of existing drugs. Given these limitations, the current study was conducted to explore the classification features defining the therapeutic success that can result from targeting a particular gene. RESULTS: Classification was based on extracting features specific to known successful anti-cancer targets and combining them in a linear classifier, resulting in calculation of an enrichment score for each gene. Extended description, the search tool used in this study, enriched existing drug target candidates by up to 10-fold at an approximately 50% recall rate, covering approximately 24 000 genes or approximately 80% of genome. More importantly, the target category with high attrition rate was classified from target category with low attrition rate, allowing to refine the drug development portfolios. Biological relevance of the parameters comprising the enrichment score was explored. Enrichment in cancer-specific effects was independently demonstrated by literature analysis. Imposing these enrichment scores on existing structural, pathway and phenotype-based procedures for prospective target selection may enhance the efficiency and accuracy of target identification and accelerate drug design. AVAILABILITY: The software used in this work is available upon request. Anatoly L. Mayburd, Inna Golovchikova, James L. Mulshine |
Bioinform. | 2 |