Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Tianfan Fu"'
Publikováno v:
Health Data Science, Vol 4 (2024)
Background: Clinical trial is a crucial step in the development of a new therapy (e.g., medication) and is remarkably expensive and time-consuming. Forecasting the approval of clinical trials accurately would enable us to circumvent trials destined t
Externí odkaz:
https://doaj.org/article/ed4686231a1e46e6ab114d90262d2449
Publikováno v:
IEEE transactions on knowledge and data engineering. 34(11)
The goal of molecular optimization is to generate molecules similar to a target molecule but with better chemical properties. Deep generative models have shown great success in molecule optimization. However, due to the iterative local generation pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0825de9734277d66a47f7efc0d0c2e89
https://pubmed.ncbi.nlm.nih.gov/36590707
https://pubmed.ncbi.nlm.nih.gov/36590707
Autor:
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik
Publikováno v:
Nat Chem Biol
Artificial intelligence (AI) is poised to advance therapeutic science. Therapeutics Data Commons is an initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery, establishing the foundation of which AI meth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08a5f42ddeaa51cdb10b04af8a835bd7
https://europepmc.org/articles/PMC9529840/
https://europepmc.org/articles/PMC9529840/
Autor:
Tianfan Fu, Jimeng Sun
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c1bcd357cd239ea93f2a7953012cfc58
https://doi.org/10.1145/3534678.3539284
https://doi.org/10.1145/3534678.3539284
Autor:
Tianfan Fu, Jimeng Sun
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08239f94c8e051d1b5e82d3a9ec31ff4
https://doi.org/10.1145/3534678.3539285
https://doi.org/10.1145/3534678.3539285
Publikováno v:
KDD
Drug discovery aims at finding promising drug molecules for treating target diseases. Existing computational drug discovery methods mainly depend on molecule databases, ignoring valuable data collected from clinical trials. In this work, we propose P
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b892be5f51aa2fd09691a537d4dfd06f
https://doi.org/10.1145/3447548.3467286
https://doi.org/10.1145/3447548.3467286
Publikováno v:
BCB
The molecular optimization task is to generate molecules that are similar to a target molecule but with better chemical properties. Deep Generative Models (DGMs) have shown initial success in automatic molecule optimization. However, the training of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22225225b2745dc0ff8adebd93925842
https://doi.org/10.1145/3459930.3469530
https://doi.org/10.1145/3459930.3469530
Publikováno v:
Bioinformatics
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists ent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82f1e4918d17fae80afc8b391cdb49e2
https://doi.org/10.1093/bioinformatics/btaa1005
https://doi.org/10.1093/bioinformatics/btaa1005
Publikováno v:
IJCAI (U S)
IJCAI
IJCAI
Predictive phenotyping is about accurately predicting what phenotypes will occur in the next clinical visit based on longitudinal Electronic Health Record (EHR) data. Several deep learning (DL) models have demonstrated great performance in predictive
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b48fc65feb84e8c13b7dea1c22560c1
https://pubmed.ncbi.nlm.nih.gov/33767572
https://pubmed.ncbi.nlm.nih.gov/33767572
Publikováno v:
BIBM
Automatic molecule optimization aims at generating a new molecule Y with more desirable properties based on an input molecule X. There are two learning strategies for molecule optimization: 1) Maximum Likelihood Estimation (MLE) methods that take a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ced86fa57fb6d52da88200ea98d4ce74
https://doi.org/10.1109/bibm49941.2020.9313409
https://doi.org/10.1109/bibm49941.2020.9313409