Demo Data from EDBT 2026 / reviewers in the wild / expert
Kyle Kai-How Farh
dblp:196/4117
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 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 · 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 › genomics › genome-wide association study
causal variant prioritization |
0.5 | 1 | 2021 | PICS2: next-generation fine mapping via probabilistic identification of causal SNPs · Bioinform. 2021 |
Bioinformatics and computational biology › statistical genetics › fine-mapping
genetic fine-mapping |
0.5 | 1 | 2021 | PICS2: next-generation fine mapping via probabilistic identification of causal SNPs · Bioinform. 2021 |
Bioinformatics and computational biology › statistical genetics
post-GWAS analysis |
0.5 | 1 | 2021 | PICS2: next-generation fine mapping via probabilistic identification of causal SNPs · Bioinform. 2021 |
Methods — techniques the papers use, named apart from their topics
probabilistic inference · 0.5LD reference data · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | PICS2: next-generation fine mapping via probabilistic identification of causal SNPsabstractSUMMARY: The Probabilistic Identification of Causal SNPs (PICS) algorithm and web application was developed as a fine-mapping tool to determine the likelihood that each single nucleotide polymorphism (SNP) in LD with a reported index SNP is a true causal polymorphism. PICS is notable for its ability to identify candidate causal SNPs within a locus using only the index SNP, which are widely available from published GWAS, whereas other methods require full summary statistics or full genotype data. However, the original PICS web application operates on a single SNP at a time, with slow performance, severely limiting its usability. We have developed a next-generation PICS tool, PICS2, which enables performance of PICS analyses of large batches of index SNPs with much faster performance. Additional updates and extensions include use of LD reference data generated from 1000 Genomes phase 3; annotation of variant consequences; annotation of GTEx eQTL genes and downloadable PICS SNPs from GTEx eQTLs; the option of generating PICS probabilities from experimental summary statistics; and generation of PICS SNPs from all SNPs of the GWAS catalog, automatically updated weekly. These free and easy-to-use resources will enable efficient determination of candidate loci for biological studies to investigate the true causal variants underlying disease processes. AVAILABILITY AND IMPLEMENTATION: PICS2 is available at https://pics2.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Kimberly E. Taylor, K. Mark Ansel, Alexander Marson, Lindsey A. Criswell, Kyle Kai-How Farh |
Bioinform. | 5 |