EDBT 2026 Demo / reviewers in the wild / expert
Alistair J. Cochran
dblp:61/10200
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
—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 › copy number analysis
copy number variation detection |
0.1 | 1 | 2011 | Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV · Bioinform. 2011 |
Bioinformatics and computational biology › genomics › structural variation
exome sequencing CNV detection |
0.1 | 1 | 2011 | Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV · Bioinform. 2011 |
Bioinformatics and computational biology › cancer genomics › chromosomal aberration detection
loss of heterozygosity detection |
0.1 | 1 | 2011 | Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV · Bioinform. 2011 |
Methods — techniques the papers use, named apart from their topics
statistical modeling · 0.1depth-of-coverage analysis · 0.1b-allele frequency analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNVabstractMOTIVATION: The ability to detect copy-number variation (CNV) and loss of heterozygosity (LOH) from exome sequencing data extends the utility of this powerful approach that has mainly been used for point or small insertion/deletion detection. RESULTS: We present ExomeCNV, a statistical method to detect CNV and LOH using depth-of-coverage and B-allele frequencies, from mapped short sequence reads, and we assess both the method's power and the effects of confounding variables. We apply our method to a cancer exome resequencing dataset. As expected, accuracy and resolution are dependent on depth-of-coverage and capture probe design. AVAILABILITY: CRAN package 'ExomeCNV'. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Jarupon Fah Sathirapongsasuti, Hane Lee, Basil A. J. Horst, Georg Brunner, Alistair J. Cochran, Scott Binder, John Quackenbush, Stanley F. Nelson |
Bioinform. | 5 |