Cargando…
Biomedical Text Link Prediction for Drug Discovery: A Case Study with COVID-19
Link prediction in artificial intelligence is used to identify missing links or derive future relationships that can occur in complex networks. A link prediction model was developed using the complex heterogeneous biomedical knowledge graph, SemNet, to predict missing links in biomedical literature...
Autores principales: | McCoy, Kevin, Gudapati, Sateesh, He, Lawrence, Horlander, Elaina, Kartchner, David, Kulkarni, Soham, Mehra, Nidhi, Prakash, Jayant, Thenot, Helena, Vanga, Sri Vivek, Wagner, Abigail, White, Brandon, Mitchell, Cassie S. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230210/ https://www.ncbi.nlm.nih.gov/pubmed/34073456 http://dx.doi.org/10.3390/pharmaceutics13060794 |
Ejemplares similares
-
Meta-Analysis of Gastrointestinal Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia
por: Mohanavelu, Prahathishree, et al.
Publicado: (2021) -
Optimizations for Computing Relatedness in Biomedical Heterogeneous
Information Networks: SemNet 2.0
por: Kirkpatrick, Anna, et al.
Publicado: (2022) -
Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19
por: Kartchner, David, et al.
Publicado: (2023) -
Hematological Adverse Events with Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia: A Systematic Review with Meta-Analysis
por: Kronick, Olivia, et al.
Publicado: (2023) -
Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia
por: Mehra, Nidhi, et al.
Publicado: (2022)