A comprehensive survey on computational methods of non-coding RNA and disease association prediction
The studies on relationships between non-coding RNAs and diseases are widely carried out
in recent years. A large number of experimental methods and technologies of producing …
in recent years. A large number of experimental methods and technologies of producing …
Biomedical data, computational methods and tools for evaluating disease–disease associations
In recent decades, exploring potential relationships between diseases has been an active
research field. With the rapid accumulation of disease-related biomedical data, a lot of …
research field. With the rapid accumulation of disease-related biomedical data, a lot of …
iCircDA-MF: identification of circRNA-disease associations based on matrix factorization
H Wei, B Liu - Briefings in bioinformatics, 2020 - academic.oup.com
Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-
loop structure, which play critical roles in various biological processes. Identifying …
loop structure, which play critical roles in various biological processes. Identifying …
[HTML][HTML] Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records
Abstract Machine learning has become ubiquitous and a key technology on mining
electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised …
electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised …
Enhancing the prediction of disease–gene associations with multimodal deep learning
Motivation Computationally predicting disease genes helps scientists optimize the in-depth
experimental validation and accelerates the identification of real disease-associated genes …
experimental validation and accelerates the identification of real disease-associated genes …
Multitask joint strategies of self-supervised representation learning on biomedical networks for drug discovery
Self-supervised representation learning (SSL) on biomedical networks provides new
opportunities for drug discovery; however, effectively combining multiple SSL models is still …
opportunities for drug discovery; however, effectively combining multiple SSL models is still …
Drug repositioning with GraphSAGE and clustering constraints based on drug and disease networks
The understanding of therapeutic properties is important in drug repositioning and drug
discovery. However, chemical or clinical trials are expensive and inefficient to characterize …
discovery. However, chemical or clinical trials are expensive and inefficient to characterize …
[HTML][HTML] Automatic ICD code assignment of Chinese clinical notes based on multilayer attention BiRNN
Abstract International Classification of Diseases (ICD) code is an important label of
electronic health record. The automatic ICD code assignment based on the narrative of …
electronic health record. The automatic ICD code assignment based on the narrative of …
Computational drug repositioning with random walk on a heterogeneous network
Drug repositioning is an efficient and promising strategy to identify new indications for
existing drugs, which can improve the productivity of traditional drug discovery and …
existing drugs, which can improve the productivity of traditional drug discovery and …
Evaluating disease similarity based on gene network reconstruction and representation
Motivation Quantifying the associations between diseases is of great significance in
increasing our understanding of disease biology, improving disease diagnosis, re …
increasing our understanding of disease biology, improving disease diagnosis, re …