Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Junru Jin"'
Autor:
Yu Wang, Chao Pang, Yuzhe Wang, Junru Jin, Jingjie Zhang, Xiangxiang Zeng, Ran Su, Quan Zou, Leyi Wei
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-15 (2023)
Abstract Automating retrosynthesis with artificial intelligence expedites organic chemistry research in digital laboratories. However, most existing deep-learning approaches are hard to explain, like a “black box” with few insights. Here, we prop
Externí odkaz:
https://doaj.org/article/c63228f9cbbb4198a7eb92a6b99699c1
Autor:
Junru Jin, Yingying Yu, Ruheng Wang, Xin Zeng, Chao Pang, Yi Jiang, Zhongshen Li, Yutong Dai, Ran Su, Quan Zou, Kenta Nakai, Leyi Wei
Publikováno v:
Genome Biology, Vol 23, Iss 1, Pp 1-23 (2022)
Abstract In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperfo
Externí odkaz:
https://doaj.org/article/2f80a30227c3499ab9491653447fcbb1
Autor:
Yi Jiang, Ruheng Wang, Jiuxin Feng, Junru Jin, Sirui Liang, Zhongshen Li, Yingying Yu, Anjun Ma, Ran Su, Quan Zou, Qin Ma, Leyi Wei
Publikováno v:
Advanced Science, Vol 10, Iss 11, Pp n/a-n/a (2023)
Abstract Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep hypergraph learning framework for the prediction of peptid
Externí odkaz:
https://doaj.org/article/588f2336ca0b4bc2a753111de279c44d
Autor:
Ruheng Wang, Yi Jiang, Junru Jin, Chenglin Yin, Haoqing Yu, Fengsheng Wang, Jiuxin Feng, Ran Su, Kenta Nakai, Quan Zou, Leyi Wei
Publikováno v:
Nucleic Acids Research. 51:3017-3029
Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5991b5f87e0addab62b648e507e3bee
https://doi.org/10.1093/nar/gkad055
https://doi.org/10.1093/nar/gkad055
BackgroundDiabetes is a chronic metabolic disorder that has been a major cause of blindness, kidney failure, heart attacks, stroke, and lower limb amputation across the world. To alleviate the impact of diabetes, researchers have developed the next g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fdc94eafcb87be5ca95b4a19a3b10b0a
https://doi.org/10.1101/2023.05.22.541389
https://doi.org/10.1101/2023.05.22.541389
Publikováno v:
Bioinformatics. 39
MotivationPlant Small Secreted Peptides (SSPs) play an important role in plant growth, development, and plant–microbe interactions. Therefore, the identification of SSPs is essential for revealing the functional mechanisms. Over the last few decade
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e24f95b7b56111e60288a25ee498b460
https://doi.org/10.1093/bioinformatics/btad108
https://doi.org/10.1093/bioinformatics/btad108
Autor:
Xin Zhang, Lesong Wei, Xiucai Ye, Kai Zhang, Saisai Teng, Zhongshen Li, Junru Jin, Min Jae Kim, Tetsuya Sakurai, Lizhen Cui, Balachandran Manavalan, Leyi Wei
Publikováno v:
Briefings in Bioinformatics. 24
Background Cell-penetrating peptides (CPPs) have received considerable attention as a means of transporting pharmacologically active molecules into living cells without damaging the cell membrane, and thus hold great promise as future therapeutics. R
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24106078f6a738b0382a644a88217006
https://doi.org/10.1093/bib/bbac545
https://doi.org/10.1093/bib/bbac545
Publikováno v:
Bioinformatics. 37:4603-4610
Motivation DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, identification of DNA methylation sites is critical for better understanding and revealing their functional mec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b6f047062595a5598949c09b7fc51f4
https://doi.org/10.1093/bioinformatics/btab677
https://doi.org/10.1093/bioinformatics/btab677
Leveraging artificial intelligence for automatic retrosynthesis speeds up organic pathway planning in digital laboratories. However, existing deep learning approaches are unexplainable, like "black box" with few insights, notably limiting their appli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7cb1e91722eb4af2a435b6678e7b7c94
http://arxiv.org/abs/2210.02630
http://arxiv.org/abs/2210.02630
Autor:
Ruheng Wang, Yi Jiang, Junru Jin, Chenglin Yin, Haoqing Yu, Fengsheng Wang, Jiuxin Feng, Ran Su, Kenta Nakai, Quan Zou, Leyi Wei
Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc73b936463117e48303f831d6696c19
https://doi.org/10.1101/2022.09.29.509859
https://doi.org/10.1101/2022.09.29.509859