Machine learning in rare disease
High-throughput profiling methods (such as genomics or imaging) have accelerated basic
research and made deep molecular characterization of patient samples routine. These …
research and made deep molecular characterization of patient samples routine. These …
Lead/drug discovery from natural resources
Z Xu, B Eichler, EA Klausner, J Duffy-Matzner, W Zheng - Molecules, 2022 - mdpi.com
Natural products and their derivatives have been shown to be effective drug candidates
against various diseases for many years. Over a long period of time, nature has produced an …
against various diseases for many years. Over a long period of time, nature has produced an …
KG-Predict: A knowledge graph computational framework for drug repurposing
The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data
has offered unprecedented opportunities for drug discovery including drug repurposing …
has offered unprecedented opportunities for drug discovery including drug repurposing …
Medical knowledge graph: Data sources, construction, reasoning, and applications
Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have
been in use in a variety of intelligent medical applications. Thus, understanding the research …
been in use in a variety of intelligent medical applications. Thus, understanding the research …
[HTML][HTML] BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis
Background: Knowledge is evolving over time, often as a result of new discoveries or
changes in the adopted methods of reasoning. Also, new facts or evidence may become …
changes in the adopted methods of reasoning. Also, new facts or evidence may become …
Drug–disease association prediction with literature based multi-feature fusion
H Kang, L Hou, Y Gu, X Lu, J Li, Q Li - Frontiers in Pharmacology, 2023 - frontiersin.org
Introduction: Exploring the potential efficacy of a drug is a valid approach for drug
development with shorter development times and lower costs. Recently, several …
development with shorter development times and lower costs. Recently, several …
MRNDR: multihead attention-based recommendation network for drug repurposing
X Feng, Z Ma, C Yu, R ** new drugs is extremely expensive, whereas
drug repurposing represents a promising approach to augment the efficiency of new drug …
drug repurposing represents a promising approach to augment the efficiency of new drug …
[HTML][HTML] IUPHAR review–Data-driven computational drug repurposing approaches for opioid use disorder
Abstract Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by
the misuse of opioid drugs, causing significant morbidity and mortality in the United States …
the misuse of opioid drugs, causing significant morbidity and mortality in the United States …
A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks
Identifying disease-gene associations is important for understanding molecule mechanisms
of diseases, finding diagnostic markers and therapeutic targets. Many computational …
of diseases, finding diagnostic markers and therapeutic targets. Many computational …
[HTML][HTML] A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects
X Liang, J Li, Y Fu, L Qu, Y Tan, P Zhang - Journal of Biomedical Informatics, 2022 - Elsevier
Drug side effects are closely related to the success and failure of drug development. Here
we present a novel machine learning method for side effect prediction. The proposed …
we present a novel machine learning method for side effect prediction. The proposed …