Demo Data from EDBT 2026 / reviewers in the wild / expert
Akash Kumar 0013
dblp:332/0531
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
0000-0001-9442-3597ORCID · verified
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% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › genomics › high-throughput sequencing
targeted resequencing |
0.2 | 1 | 2014 | MIPgen: optimized modeling and design of molecular inversion probes for targeted resequencing · Bioinform. 2014 |
Data mining › predictive modeling
classification |
0.1 | 1 | 2014 | MIPgen: optimized modeling and design of molecular inversion probes for targeted resequencing · Bioinform. 2014 |
Methods — techniques the papers use, named apart from their topics
support vector machine · 0.4logistic regression · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | MIPgen: optimized modeling and design of molecular inversion probes for targeted resequencingabstractUNLABELLED: Molecular inversion probes (MIPs) enable cost-effective multiplex targeted gene resequencing in large cohorts. However, the design of individual MIPs is a critical parameter governing the performance of this technology with respect to capture uniformity and specificity. MIPgen is a user-friendly package that simplifies the process of designing custom MIP assays to arbitrary targets. New logistic and SVM-derived models enable in silico predictions of assay success, and assay redesign exhibits improved coverage uniformity relative to previous methods, which in turn improves the utility of MIPs for cost-effective targeted sequencing for candidate gene validation and for diagnostic sequencing in a clinical setting. AVAILABILITY AND IMPLEMENTATION: MIPgen is implemented in C++. Source code and accompanying Python scripts are available at http://shendurelab.github.io/MIPGEN/. Evan A. Boyle, Brian J. O'Roak, Beth K. Martin, Akash Kumar 0013, Jay Shendure |
Bioinform. | 4 |