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 …
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 …
Application of machine learning in microbiology
Microorganisms are ubiquitous and closely related to people's daily lives. Since they were
first discovered in the 19th century, researchers have shown great interest in …
first discovered in the 19th century, researchers have shown great interest in …
A graph auto-encoder model for miRNA-disease associations prediction
Emerging evidence indicates that the abnormal expression of miRNAs involves in the
evolution and progression of various human complex diseases. Identifying disease-related …
evolution and progression of various human complex diseases. Identifying disease-related …
AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA–disease associations identification
Q Ning, Y Zhao, J Gao, C Chen, X Li… - Briefings in …, 2023 - academic.oup.com
In recent years, many experiments have proved that microRNAs (miRNAs) play a variety of
important regulatory roles in cells, and their abnormal expression can lead to the emergence …
important regulatory roles in cells, and their abnormal expression can lead to the emergence …
GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest
QW Wu, JF Xia, JC Ni, CH Zheng - Briefings in bioinformatics, 2021 - academic.oup.com
Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new
biomarkers for prevention, diagnosis and treatment of complex human diseases. In this …
biomarkers for prevention, diagnosis and treatment of complex human diseases. In this …
MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph
Accurate identification of the miRNA-disease associations (MDAs) helps to understand the
etiology and mechanisms of various diseases. However, the experimental methods are …
etiology and mechanisms of various diseases. However, the experimental methods are …
SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost
Background Identifying miRNA and disease associations helps us understand disease
mechanisms of action from the molecular level. However, it is usually blind, time-consuming …
mechanisms of action from the molecular level. However, it is usually blind, time-consuming …
Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression
S Zhou, S Wang, Q Wu, R Azim, W Li - Computational biology and …, 2020 - Elsevier
MicroRNAs (miRNAs) have been proved to play an indispensable role in many fundamental
biological processes, and the dysregulation of miRNAs is closely correlated with human …
biological processes, and the dysregulation of miRNAs is closely correlated with human …
Predicting miRNA-disease associations using an ensemble learning framework with resampling method
Q Dai, Z Wang, Z Liu, X Duan, J Song… - Briefings in …, 2022 - academic.oup.com
Motivation: Accumulating evidences have indicated that microRNA (miRNA) plays a crucial
role in the pathogenesis and progression of various complex diseases. Inferring disease …
role in the pathogenesis and progression of various complex diseases. Inferring disease …