Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Jiuxin Feng"'
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
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
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
Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, we propose PHAT, a deep graph learning framework for the prediction of peptide secondary str
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f38cdc7e68a9a81b584b0c1b1bea28c0
https://doi.org/10.1101/2022.06.09.495580
https://doi.org/10.1101/2022.06.09.495580