Biomedical data and computational models for drug repositioning: a comprehensive review
Drug repositioning can drastically decrease the cost and duration taken by traditional drug
research and development while avoiding the occurrence of unforeseen adverse events …
research and development while avoiding the occurrence of unforeseen adverse events …
Recursive recurrent nets with attention modeling for ocr in the wild
CY Lee, S Osindero - … of the IEEE conference on computer …, 2016 - openaccess.thecvf.com
We present recursive recurrent neural networks with attention modeling (R2AM) for lexicon-
free optical character recognition in natural scene images. The primary advantages of the …
free optical character recognition in natural scene images. The primary advantages of the …
NCMCMDA: miRNA–disease association prediction through neighborhood constraint matrix completion
Emerging evidence shows that microRNAs (miRNAs) play a critical role in diverse
fundamental and important biological processes associated with human diseases. Inferring …
fundamental and important biological processes associated with human diseases. Inferring …
[HTML][HTML] Machine learning for drug repositioning: Recent advances and challenges
Because translating the growing body of knowledge about human disease into treatments
has been slower than expected, the application of machine learning techniques to drug …
has been slower than expected, the application of machine learning techniques to drug …
iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding
Computational drug repositioning and drug-target prediction have become essential tasks in
the early stage of drug discovery. In previous studies, these two tasks have often been …
the early stage of drug discovery. In previous studies, these two tasks have often been …
SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association
Accumulating studies have shown that microRNAs (miRNAs) could be used as targets of
small-molecule (SM) drugs to treat diseases. In recent years, researchers have proposed …
small-molecule (SM) drugs to treat diseases. In recent years, researchers have proposed …
A computational study of potential miRNA-disease association inference based on ensemble learning and kernel ridge regression
As increasing experimental studies have shown that microRNAs (miRNAs) are closely
related to multiple biological processes and the prevention, diagnosis and treatment of …
related to multiple biological processes and the prevention, diagnosis and treatment of …
Probabilistic and dynamic molecule-disease interaction modeling for drug discovery
Drug discovery aims at finding promising drug molecules for treating target diseases.
Existing computational drug discovery methods mainly depend on molecule databases …
Existing computational drug discovery methods mainly depend on molecule databases …
GK BertDTA: a graph representation learning and semantic embedding-based framework for drug-target affinity prediction
Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA),
indicating the binding capability between drugs and target proteins, is a crucial indicator for …
indicating the binding capability between drugs and target proteins, is a crucial indicator for …
Modeling relational drug-target-disease interactions via tensor factorization with multiple web sources
Modeling the behaviors of drug-target-disease interactions is crucial in the early stage of
drug discovery and holds great promise for precision medicine and personalized treatments …
drug discovery and holds great promise for precision medicine and personalized treatments …