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FastTENET: an accelerated TENET algorithm based on manycore computing in Python
- Sung, Rakbin;
- Kim, Hyeonkyu;
- Kim, Junil;
- Lee, Daewon
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2초록
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 973x performance improvement. Availability and implementation: FastTENET is available on GitHub at https://github.com/cxinsys/fasttenet. [GRAPHICS] .
- 제목
- FastTENET: an accelerated TENET algorithm based on manycore computing in Python
- 저자
- Sung, Rakbin; Kim, Hyeonkyu; Kim, Junil; Lee, Daewon
- 발행일
- 2024-12
- 유형
- Article
- 저널명
- Bioinformatics
- 권
- 40
- 호
- 12
- 언어
- ENG
- 출판사
- OXFORD UNIV PRESS
- 발행국가
- 영국
- ISSN
- E 1367-4811
P 1367-4803