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Hyeonkyu Kim

dblp:254/3946 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—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%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 50% GPUs and heterogeneous computing · 50%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › biological network › network biology › network inference
gene regulatory network inference
0.812024
FastTENET: an accelerated TENET algorithm based on manycore computing in Python · Bioinform. 2024
GPUs and heterogeneous computing
GPU computing
0.212024
FastTENET: an accelerated TENET algorithm based on manycore computing in Python · Bioinform. 2024
Parallel and multicore computing › many-core systems
many-core computing
0.212024
FastTENET: an accelerated TENET algorithm based on manycore computing in Python · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

transfer entropy · 1.5array computing · 1.5
YearPublicationVenuePosition
2024 FastTENET: an accelerated TENET algorithm based on manycore computing in Python
abstract
SUMMARY: TENET reconstructs gene regulatory networks from single-cell RNA sequencing (scRNAseq) data using the transfer entropy (TE), and works successfully on a variety of scRNAseq data. However, TENET is limited by its long computation time for large datasets. To address this limitation, we propose FastTENET, an array-computing version of TENET algorithm optimized for acceleration on manycore processors such as GPUs. FastTENET counts the unique patterns of joint events to compute the TE based on array computing. Compared to TENET, FastTENET achieves up to 973× performance improvement. AVAILABILITY AND IMPLEMENTATION: FastTENET is available on GitHub at https://github.com/cxinsys/fasttenet.
Rakbin Sung, Hyeonkyu Kim, Junil Kim, Dae-Won Lee
Bioinform.2