Demo Data from VLDB 2026 / reviewers in the wild / expert
Shabir A Madhi
dblp:254/3569
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-7629-0636ORCID · reported
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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
genomics |
0.4 | 1 | 2019 | HLA*LA - HLA typing from linearly projected graph alignments · Bioinform. 2019 |
Bioinformatics and computational biology › sequence alignment
graph-based alignment |
0.4 | 1 | 2019 | HLA*LA - HLA typing from linearly projected graph alignments · Bioinform. 2019 |
Bioinformatics and computational biology › immunoinformatics
HLA typing |
0.4 | 1 | 2019 | HLA*LA - HLA typing from linearly projected graph alignments · Bioinform. 2019 |
Bioinformatics and computational biology
sequence analysis |
0.1 | 1 | 2019 | HLA*LA - HLA typing from linearly projected graph alignments · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
linear alignment projection · 0.4graph alignment · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | HLA*LA - HLA typing from linearly projected graph alignmentsabstractSUMMARY: HLA*LA implements a new graph alignment model for human leukocyte antigen (HLA) type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data) and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample. AVAILABILITY AND IMPLEMENTATION: HLA*LA is implemented in C++ and Perl and freely available as a bioconda package or from https://github.com/DiltheyLab/HLA-LA (GPL v3). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Alexander T. Dilthey, Alexander J. Mentzer, Raphael Carapito, Clare Cutland, Nezih Cereb, Shabir A Madhi, Arang Rhie, Sergey Koren, Seiamak Bahram, Gil McVean, Adam M. Phillippy |
Bioinform. | 6 |