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Petra Stepanowsky

dblp:122/5559 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › transcriptomics › small RNA-seq analysis
microRNA sequencing analysis
0.212014
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
0.212014
MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure · Bioinform. 2014
GPUs and heterogeneous computing
GPU-accelerated bioinformatics
0.112014
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
YearPublicationVenuePosition
2014 MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure
abstract
SUMMARY: 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.4