Demo Data from EDBT 2026 / reviewers in the wild / expert
Veronna Marie
dblp:249/1468
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0002-5010-5076ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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 › molecular evolution
coevolution analysis |
0.4 | 1 | 2019 | Gag-protease coevolution shapes the outcome of lopinavir-inclusive treatment regimens in chronically infected HIV-1 subtype C patients · Bioinform. 2019 |
Bioinformatics and computational biology › clinical bioinformatics
HIV drug resistance |
0.4 | 1 | 2019 | Gag-protease coevolution shapes the outcome of lopinavir-inclusive treatment regimens in chronically infected HIV-1 subtype C patients · Bioinform. 2019 |
Bioinformatics and computational biology › molecular evolution
viral evolution |
0.4 | 1 | 2019 | Gag-protease coevolution shapes the outcome of lopinavir-inclusive treatment regimens in chronically infected HIV-1 subtype C patients · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
phylogenetic analysis · 0.4bayesian network learning · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Gag-protease coevolution shapes the outcome of lopinavir-inclusive treatment regimens in chronically infected HIV-1 subtype C patientsabstractMOTIVATION: Commonly, protease inhibitor failure is characterized by the development of multiple protease resistance mutations (PRMs). While the impact of PRMs on therapy failure are understood, the introduction of Gag mutations with protease remains largely unclear. RESULTS: Here, we utilized phylogenetic analyses and Bayesian network learning as tools to understand Gag-protease coevolution and elucidate the pathways leading to Lopinavir failure in HIV-1 subtype C infected patients. Our analyses indicate that while PRMs coevolve in response to drug selection pressure within protease, the Gag mutations added to the existing network while specifically interacting with known Lopinavir failure PRMs. Additionally, the selection of mutations at specific positions in Gag-protease suggests that these coevolving mutational changes occurs to maintain structural integrity during Gag cleavage. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Veronna Marie, Michelle Gordon |
Bioinform. | 1 |