Demo Data from VLDB 2026 / reviewers in the wild / expert
Hyeonkyu Kim
dblp:254/3946
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › biological network › network biology › network inference
gene regulatory network inference |
0.8 | 1 | 2024 | FastTENET: an accelerated TENET algorithm based on manycore computing in Python · Bioinform. 2024 |
GPUs and heterogeneous computing
GPU computing |
0.2 | 1 | 2024 | FastTENET: an accelerated TENET algorithm based on manycore computing in Python · Bioinform. 2024 |
Parallel and multicore computing › many-core systems
many-core computing |
0.2 | 1 | 2024 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | FastTENET: an accelerated TENET algorithm based on manycore computing in PythonabstractSUMMARY: 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 |