[HTML][HTML] Data resources and computational methods for lncRNA-disease association prediction
N Sheng, L Huang, Y Lu, H Wang, L Yang… - Computers in Biology …, 2023 - Elsevier
Increasing interest has been attracted in deciphering the potential disease pathogenesis
through lncRNA-disease association (LDA) prediction, regarding to the diverse functional …
through lncRNA-disease association (LDA) prediction, regarding to the diverse functional …
A survey of deep learning for detecting miRNA-disease associations: databases, computational methods, challenges, and future directions
N Sheng, X **e, Y Wang, L Huang… - IEEE/ACM …, 2024 - ieeexplore.ieee.org
MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role
in the occurrence and development of various diseases. Identifying the potential miRNA …
in the occurrence and development of various diseases. Identifying the potential miRNA …
GANLDA: graph attention network for lncRNA-disease associations prediction
Increasing studies have indicated that long non-coding RNAs (lncRNAs) play important
roles in many physiological and pathological pathways. Identifying lncRNA-disease …
roles in many physiological and pathological pathways. Identifying lncRNA-disease …
GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest
QW Wu, JF **a, 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 …
[HTML][HTML] A novel battery abnormality detection method using interpretable Autoencoder
X Zhang, P Liu, N Lin, Z Zhang, Z Wang - Applied Energy, 2023 - Elsevier
The abnormality detection of lithium-ion battery pack is crucial to ensure the safety of electric
vehicles (EVs). However, the dynamic and complex operating conditions of EVs making it …
vehicles (EVs). However, the dynamic and complex operating conditions of EVs making it …
Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases
Motivation Identifying the relationships among long non-coding RNAs (lncRNAs),
microRNAs (miRNAs) and diseases is highly valuable for diagnosing, preventing, treating …
microRNAs (miRNAs) and diseases is highly valuable for diagnosing, preventing, treating …
LDAformer: predicting lncRNA-disease associations based on topological feature extraction and Transformer encoder
Y Zhou, X Wang, L Yao, M Zhu - Briefings in Bioinformatics, 2022 - academic.oup.com
The identification of long noncoding RNA (lncRNA)-disease associations is of great value for
disease diagnosis and treatment, and it is now commonly used to predict potential lncRNA …
disease diagnosis and treatment, and it is now commonly used to predict potential lncRNA …
A random forest based computational model for predicting novel lncRNA-disease associations
D Yao, X Zhan, X Zhan, CK Kwoh, P Li, J Wang - BMC bioinformatics, 2020 - Springer
Background Accumulated evidence shows that the abnormal regulation of long non-coding
RNA (lncRNA) is associated with various human diseases. Accurately identifying disease …
RNA (lncRNA) is associated with various human diseases. Accurately identifying disease …
gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network
L Wang, C Zhong - BMC bioinformatics, 2022 - Springer
Abstract Background Long non-coding RNAs (lncRNAs) are related to human diseases by
regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute …
regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute …
Multi-channel graph attention autoencoders for disease-related lncRNAs prediction
Motivation Predicting disease-related long non-coding RNAs (lncRNAs) can be used as the
biomarkers for disease diagnosis and treatment. The development of effective computational …
biomarkers for disease diagnosis and treatment. The development of effective computational …