Results 311 to 320 of about 1,906,656 (324)

Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction

Bioinform., 2020
MOTIVATION Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive ...
Jin Li, Sai Zhang, Tao Liu, Chen Ning, Zhuoxuan Zhang, Wei Zhou   +5 more
semanticscholar   +1 more source

Motif-Aware miRNA-Disease Association Prediction via Hierarchical Attention Network

IEEE journal of biomedical and health informatics
As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases.
Bowei Zhao, Yizhou He, Xiaorui Su, Yue Yang, Guo-Dong Li, Yu-an Huang, Peng-Wei Hu, Zhu-Hong You, Lun Hu   +8 more
semanticscholar   +1 more source

MDAPlatform: A Component-based Platform for Constructing and Assessing miRNA-disease Association Prediction Methods

Current Bioinformatics, 2021
Increasing evidence has indicated that miRNA-disease association prediction plays a critical role in the study of clinical drugs. Researchers have proposed many computational models for miRNA-disease prediction. However, there is no unified platform to
Zhang Yayan, Guihua Duan, C. Yan, Haolun Yi, Fang-Xiang Wu, Jianxin Wang   +5 more
semanticscholar   +1 more source

LDA-LNSUBRW: lncRNA-Disease Association Prediction Based on Linear Neighborhood Similarity and Unbalanced bi-Random Walk

IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2020
Increasing number of experiments show that lncRNAs are involved in many biological processes, and their mutations and disorders are associated with many diseases.
Guobo Xie, Jiawei Jiang, Yuping Sun
semanticscholar   +1 more source

Review of MiRNA-Disease Association prediction.

Current protein and peptide science, 2020
Accumulating evidence demonstrates that miRNAs serve as critical biomarkers in various complex human diseases. Thus, identifying potential miRNA-disease associations have become a hot research topic for providing better understanding of disease pathology,
Lei Jiang, Ji Zhu
semanticscholar   +1 more source

DBNLDA: Deep Belief Network based representation learning for lncRNA-disease association prediction

Applied intelligence (Boston), 2020
The advancements in the field of high throughput analysis show abnormal expression of long non-coding RNAs (lncRNAs) in many complex diseases. Accurately identifying the disease association of lncRNA is essential in understanding their role in disease ...
Manu Madhavan, G. Gopakumar
semanticscholar   +1 more source

Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA-disease association prediction

Briefings Bioinform., 2020
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 pathogenesis of complex diseases. Most of current data-driven methods for disease-
Nan Sheng, H. Cui, Tiangang Zhang, P. Xuan   +3 more
semanticscholar   +1 more source

End-to-end interpretable disease-gene association prediction

Briefings Bioinform., 2023
Yang Li, Zihou Guo, Ke-Qiang Wang, Xin Gao, Guohua Wang   +4 more
semanticscholar   +1 more source

MiRNA-disease association prediction based on meta-paths

Briefings Bioinform., 2022
Liang Yu, Yujia Zheng, Lin Gao
semanticscholar   +1 more source

Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease

Briefings Bioinform., 2021
Chun-Chun Wang, Chi-Chi Zhu, Xing Chen
semanticscholar   +1 more source