FastTENET: an accelerated TENET algorithm based on manycore computing in Python
Rakbin Sung, Hyeonkyu Kim, Junil Kim, Daewon Lee, Can Alkan · 2024
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Description
SummaryTENET 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.
Catalog record
| CREATOR | Rakbin Sung, Hyeonkyu Kim, Junil Kim, Daewon Lee, Can Alkan |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 16 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |