Heterogeneous graph attention network based on meta-paths for lncrna–disease association prediction
Motivation: Discovering long noncoding RNA (lncRNA)–disease associations is a
fundamental and critical part in understanding disease etiology and pathogenesis. However …
fundamental and critical part in understanding disease etiology and pathogenesis. However …
Multi-view contrastive heterogeneous graph attention network for lncRNA–disease association prediction
Motivation: Exploring the potential long noncoding RNA (lncRNA)-disease associations
(LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the …
(LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the …
Specific topology and topological connection sensitivity enhanced graph learning for lncRNA–disease association prediction
Predicting disease-related candidate long noncoding RNAs (lncRNAs) is beneficial for
exploring disease pathogenesis due to the close relations between lncRNAs and the …
exploring disease pathogenesis due to the close relations between lncRNAs and the …
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 …
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 …
Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs
Abstract Motivation Long noncoding RNAs (lncRNAs) play an important role in the
occurrence and development of diseases. Predicting disease-related lncRNAs can help to …
occurrence and development of diseases. Predicting disease-related lncRNAs can help to …
Extra trees method for predicting LncRNA-disease association based on multi-layer graph embedding aggregation
QW Wu, RF Cao, JF Xia, JC Ni… - … /ACM transactions on …, 2021 - ieeexplore.ieee.org
Lots of experimental studies have revealed the significant associations between lncRNAs
and diseases. Identifying accurate associations will provide a new perspective for disease …
and diseases. Identifying accurate associations will provide a new perspective for disease …
DeepMNE: deep multi-network embedding for lncRNA-disease association prediction
Y Ma - IEEE Journal of Biomedical and Health Informatics, 2022 - ieeexplore.ieee.org
Long non-coding RNA (lncRNA) participates in various biological processes, hence its
mutations and disorders play an important role in the pathogenesis of multiple human …
mutations and disorders play an important role in the pathogenesis of multiple human …
Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA–disease association prediction
As the abnormalities of long non-coding RNAs (lncRNAs) are closely related to various
human diseases, identifying disease-related lncRNAs is important for understanding the …
human diseases, identifying disease-related lncRNAs is important for understanding the …
Graph triple-attention network for disease-related lncRNA prediction
Abnormal expressions of long non-coding RNAs (lncRNAs) are associated with various
human diseases. Identifying disease-related lncRNAs can help clarify complex disease …
human diseases. Identifying disease-related lncRNAs can help clarify complex disease …