VLDB 2026 Research / reviewers in the wild / expert
Armand Halbert
dblp:150/8973
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1Applied, 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.
| Software engineering, system software, and programming languages
1 paper |
Program analysis · 36% Software maintenance and evolution · 36% Debugging and program repair · 28% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis › specification mining
behavioral model inference |
0.2 | 1 | 2014 | Behavioral resource-aware model inference · ASE 2014 |
Debugging and program repair
fault localization |
0.2 | 1 | 2014 | Behavioral resource-aware model inference · ASE 2014 |
Software maintenance and evolution
program comprehension |
0.2 | 1 | 2014 | Behavioral resource-aware model inference · ASE 2014 |
Program analysis
dynamic analysis |
0.1 | 1 | 2014 | Behavioral resource-aware model inference · ASE 2014 |
Software maintenance and evolution
log analysis |
0.1 | 1 | 2014 | Behavioral resource-aware model inference · ASE 2014 |
Methods — techniques the papers use, named apart from their topics
log analysis · 0.2finite-state machine inference · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Most of the tight positional conservation of transcription factor binding sites near the transcription start site reflects their co-localization within regulatory modulesabstractBACKGROUND: Transcription factors (TFs) form complexes that bind regulatory modules (RMs) within DNA, to control specific sets of genes. Some transcription factor binding sites (TFBSs) near the transcription start site (TSS) display tight positional preferences relative to the TSS. Furthermore, near the TSS, RMs can co-localize TFBSs with each other and the TSS. The proportion of TFBS positional preferences due to TFBS co-localization within RMs is unknown, however. ChIP experiments confirm co-localization of some TFBSs genome-wide, including near the TSS, but they typically examine only a few TFs at a time, using non-physiological conditions that can vary from lab to lab. In contrast, sequence analysis can examine many TFs uniformly and methodically, broadly surveying the co-localization of TFBSs with tight positional preferences relative to the TSS. RESULTS: Our statistics found 43 significant sets of human motifs in the JASPAR TF Database with positional preferences relative to the TSS, with 38 preferences tight (±5 bp). Each set of motifs corresponded to a gene group of 135 to 3304 genes, with 42/43 (98%) gene groups independently validated by DAVID, a gene ontology database, with FDR < 0.05. Motifs corresponding to two TFBSs in a RM should co-occur more than by chance alone, enriching the intersection of the gene groups corresponding to the two TFs. Thus, a gene-group intersection systematically enriched beyond chance alone provides evidence that the two TFs participate in an RM. Of the 903 = 43*42/2 intersections of the 43 significant gene groups, we found 768/903 (85%) pairs of gene groups with significantly enriched intersections, with 564/768 (73%) intersections independently validated by DAVID with FDR < 0.05. A user-friendly web site at http://go.usa.gov/3kjsH permits biologists to explore the interaction network of our TFBSs to identify candidate subunit RMs. CONCLUSIONS: Gene duplication and convergent evolution within a genome provide obvious biological mechanisms for replicating an RM near the TSS that binds a particular TF subunit. Of all intersections of our 43 significant gene groups, 85% were significantly enriched, with 73% of the significant enrichments independently validated by gene ontology. The co-localization of TFBSs within RMs therefore likely explains much of the tight TFBS positional preferences near the TSS. Natalia Acevedo-Luna, Leonardo Mariño-Ramírez, Armand Halbert, Ulla Hansen, David Landsman, John L. Spouge |
BMC Bioinform. | 3 |
| 2014 | Behavioral resource-aware model inferenceabstractSoftware bugs often arise because of differences between what developers think their system does and what the system actually does. These differences frustrate debugging and comprehension efforts. We describe Perfume, an automated approach for inferring behavioral, resource-aware models of software systems from logs of their executions. These finite state machine models ease understanding of system behavior and resource use. Tony Ohmann, Michael Herzberg, Sebastian Fiss, Armand Halbert, Marc Palyart, Ivan Beschastnikh, Yuriy Brun |
ASE | 4 |