{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T01:34:23Z","timestamp":1769909663661,"version":"3.49.0"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chung-Ang University Graduate Research Scholarship in 2024","award":["KSC-2021-CRE-0371"],"award-info":[{"award-number":["KSC-2021-CRE-0371"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea government","award":["RS-2024\u201300342721"],"award-info":[{"award-number":["RS-2024\u201300342721"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>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\u00d7 performance improvement.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>FastTENET is available on GitHub at https:\/\/github.com\/cxinsys\/fasttenet.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae699","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T16:30:51Z","timestamp":1732206651000},"source":"Crossref","is-referenced-by-count":2,"title":["FastTENET: an accelerated TENET algorithm based on manycore computing in Python"],"prefix":"10.1093","volume":"40","author":[{"given":"Rakbin","family":"Sung","sequence":"first","affiliation":[{"name":"Department of Applied Art and Technology, College of Art and Technology, Chung-Ang University , Anseong 17546,","place":["Republic of 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