Network representation learning: A survey
With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …
increasingly popular to capture complex relationships across various disciplines, such as …
Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models
Since the problem proposed in late 2000s, microRNA–disease association (MDA)
predictions have been implemented based on the data fusion paradigm. Integrating diverse …
predictions have been implemented based on the data fusion paradigm. Integrating diverse …
A survey on network embedding
Network embedding assigns nodes in a network to low-dimensional representations and
effectively preserves the network structure. Recently, a significant amount of progresses …
effectively preserves the network structure. Recently, a significant amount of progresses …
deepDR: a network-based deep learning approach to in silico drug repositioning
Motivation Traditional drug discovery and development are often time-consuming and high
risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high …
risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high …
Predicting miRNA–disease association based on inductive matrix completion
X Chen, L Wang, J Qu, NN Guan, JQ Li - Bioinformatics, 2018 - academic.oup.com
Motivation It has been shown that microRNAs (miRNAs) play key roles in variety of
biological processes associated with human diseases. In Consideration of the cost and …
biological processes associated with human diseases. In Consideration of the cost and …
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
The emergence of large-scale genomic, chemical and pharmacological data provides new
opportunities for drug discovery and repositioning. In this work, we develop a computational …
opportunities for drug discovery and repositioning. In this work, we develop a computational …
Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction
Motivation Predicting the association between microRNAs (miRNAs) and diseases plays an
import role in identifying human disease-related miRNAs. As identification of miRNA …
import role in identifying human disease-related miRNAs. As identification of miRNA …
[PDF][PDF] Network representation learning with rich text information.
Abstract Representation learning has shown its effectiveness in many tasks such as image
classification and text mining. Network representation learning aims at learning distributed …
classification and text mining. Network representation learning aims at learning distributed …
Target identification among known drugs by deep learning from heterogeneous networks
Without foreknowledge of the complete drug target information, development of promising
and affordable approaches for effective treatment of human diseases is challenging. Here …
and affordable approaches for effective treatment of human diseases is challenging. Here …
Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models
Currently, there exist no generally accepted strategies of evaluating computational models
for microRNA-disease associations (MDAs). Though K-fold cross validations and case …
for microRNA-disease associations (MDAs). Though K-fold cross validations and case …