Demo Data from VLDB 2026 / reviewers in the wild / expert
Zalman Vaksman
dblp:120/0281
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0001-5121-8657ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 1 since 2021
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 · 67% Medical and health informatics · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics
clinical genomics |
0.8 | 1 | 2024 | AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification · Bioinform. 2024 |
Bioinformatics and computational biology › genome annotation
genomic variant annotation |
0.8 | 1 | 2024 | AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification · Bioinform. 2024 |
Bioinformatics and computational biology › statistical genetics › variant effect prediction
variant pathogenicity prediction |
0.8 | 1 | 2024 | AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification · Bioinform. 2024 |
Methods — techniques the papers use, named apart from their topics
intervar · 0.8clinvar integration · 0.8ACMG-AMP criteria · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classificationabstractSUMMARY: With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics-Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 strength adjustments, removal of PP5/BP6). This tool facilitates large-scale, clinically focused classification of germline sequence variants in a research setting. AVAILABILITY AND IMPLEMENTATION: AutoGVP is an open source dockerized workflow implemented in R and freely available on GitHub at https://github.com/diskin-lab-chop/AutoGVP. Jung Kim, Ammar S. Naqvi, Ryan J. Corbett, Rebecca S. Kaufman, Zalman Vaksman, Miguel A. Brown, Daniel P. Miller, Saksham Phul, Zhuangzhuang Geng, Phillip B. Storm, Adam C. Resnick, Douglas R. Stewart, Jo Lynne Rokita, Sharon J. Diskin |
Bioinform. | 5 |
| 2012 | The Mechanistic Basis of Myxococcus xanthus Rippling Behavior and Its Physiological Role during PredationabstractMyxococcus xanthus cells self-organize into periodic bands of traveling waves, termed ripples, during multicellular fruiting body development and predation on other bacteria. To investigate the mechanistic basis of rippling behavior and its physiological role during predation by this Gram-negative soil bacterium, we have used an approach that combines mathematical modeling with experimental observations. Specifically, we developed an agent-based model (ABM) to simulate rippling behavior that employs a new signaling mechanism to trigger cellular reversals. The ABM has demonstrated that three ingredients are sufficient to generate rippling behavior: (i) side-to-side signaling between two cells that causes one of the cells to reverse, (ii) a minimal refractory time period after each reversal during which cells cannot reverse again, and (iii) physical interactions that cause the cells to locally align. To explain why rippling behavior appears as a consequence of the presence of prey, we postulate that prey-associated macromolecules indirectly induce ripples by stimulating side-to-side contact-mediated signaling. In parallel to the simulations, M. xanthus predatory rippling behavior was experimentally observed and analyzed using time-lapse microscopy. A formalized relationship between the wavelength, reversal time, and cell velocity has been predicted by the simulations and confirmed by the experimental data. Furthermore, the results suggest that the physiological role of rippling behavior during M. xanthus predation is to increase the rate of spreading over prey cells due to increased side-to-side contact-mediated signaling and to allow predatory cells to remain on the prey longer as a result of more periodic cell motility. Zalman Vaksman, Douglas B. Litwin, Peng Shi 0004, Heidi B. Kaplan, Oleg A. Igoshin |
PLoS Comput. Biol. | 2 |