Demo Data from EDBT 2026 / reviewers in the wild / expert
Alex Ferbrache
dblp:151/5706
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—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% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
GPUs and heterogeneous computing · 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 › transcriptomics › small RNA-seq analysis
microRNA sequencing analysis |
0.2 | 1 | 2014 | MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure · Bioinform. 2014 |
Bioinformatics and computational biology › gene regulation
microRNA target prediction |
0.2 | 1 | 2014 | MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure · Bioinform. 2014 |
GPUs and heterogeneous computing
GPU-accelerated bioinformatics |
0.1 | 1 | 2014 | MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure · Bioinform. 2014 |
Methods — techniques the papers use, named apart from their topics
parallel computing · 0.4CUDA · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructureabstractSUMMARY: MAGI is a web service for fast MicroRNA-Seq data analysis in a graphics processing unit (GPU) infrastructure. Using just a browser, users have access to results as web reports in just a few hours->600% end-to-end performance improvement over state of the art. MAGI's salient features are (i) transfer of large input files in native FASTA with Qualities (FASTQ) format through drag-and-drop operations, (ii) rapid prediction of microRNA target genes leveraging parallel computing with GPU devices, (iii) all-in-one analytics with novel feature extraction, statistical test for differential expression and diagnostic plot generation for quality control and (iv) interactive visualization and exploration of results in web reports that are readily available for publication. AVAILABILITY AND IMPLEMENTATION: MAGI relies on the Node.js JavaScript framework, along with NVIDIA CUDA C, PHP: Hypertext Preprocessor (PHP), Perl and R. It is freely available at http://magi.ucsd.edu. Jihoon Kim 0001, Eric Levy, Alex Ferbrache, Petra Stepanowsky, Claudiu Farcas, Shuang Wang 0002, Stefan Brunner, Tyler Bath, Yuan Wu 0003, Lucila Ohno-Machado |
Bioinform. | 3 |