Results 1 to 10 of about 1,906,656 (324)

Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction

Frontiers in Bioengineering and Biotechnology, 2020
Due to the cost and complexity of biological experiments, many computational methods have been proposed to predict potential miRNA-disease associations by utilizing known miRNA-disease associations and other related information.
Rongxiang Zhu, Rongxiang Zhu, Chaojie Ji, Yingying Wang, Yingying Wang, Yunpeng Cai, Hongyan Wu   +6 more
doaj   +2 more sources

AEPMA: peptide-microbe association prediction based on autoevolutionary heterogeneous graph learning. [PDF]

Brief Bioinform
Hu Z, Pan L, Zhang D, Bin Y, Su Y.
europepmc   +3 more sources

CoupleMDA: Metapath-Induced Structural-Semantic Coupling Network for miRNA-Disease Association Prediction. [PDF]

Int J Mol Sci
Li Z, Chen G, Tan G, Chen CY.
europepmc   +3 more sources

Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction. [PDF]

Front Genet, 2023
Yao D, Zhang B, Li X, Zhan X, Zhan X, Zhang B.   +5 more
europepmc   +3 more sources

ICLRBBN: a tool for accurate prediction of potential lncRNA disease associations

Molecular Therapy: Nucleic Acids, 2021
Growing evidence has elucidated that long non-coding RNAs (lncRNAs) are involved in a variety of complex diseases in human bodies. In recent years, it has become a hot topic to develop effective computational models to identify potential lncRNA-disease ...
Yuqi Wang, Hao Li, Linai Kuang, Yihong Tan, Xueyong Li, Zhen Zhang, Lei Wang   +6 more
doaj   +1 more source

Observation of the long-period monotonic seismic waves of the November 11, 2018, Mayotte event by Iranian broadband seismic stations

Earth, Planets and Space, 2021
On November 11, 2018, an event generating long-lasting, monotonic long-period surface waves was observed by seismographs around the world. This event occurred at around 09:28 UTC east of the Mayotte Island, in the Indian Ocean off the coast of East ...
Hossein Sadeghi, Sadaomi Suzuki
doaj   +1 more source

Identifying lncRNA–disease association based on GAT multiple-operator aggregation and inductive matrix completion

Frontiers in Genetics, 2022
Computable models as a fundamental candidate for traditional biological experiments have been applied in inferring lncRNA–disease association (LDA) for many years, without time-consuming and laborious limitations. However, sparsity inherently existing in
Yi Zhang, Yi Zhang, Yu Wang, Yu Wang, Xin Li, Xin Li, Yarong Liu, Yarong Liu, Min Chen   +8 more
doaj   +1 more source

Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction

Frontiers in Genetics, 2021
Numerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for the ...
Jia Qu, Chun-Chun Wang, Shu-Bin Cai, Wen-Di Zhao, Xiao-Long Cheng, Zhong Ming   +5 more
doaj   +1 more source

AMCSMMA: Predicting Small Molecule–miRNA Potential Associations Based on Accurate Matrix Completion

Cells, 2023
Exploring potential associations between small molecule drugs (SMs) and microRNAs (miRNAs) is significant for drug development and disease treatment. Since biological experiments are expensive and time-consuming, we propose a computational model based on
Shudong Wang, Chuanru Ren, Yulin Zhang, Shanchen Pang, Sibo Qiao, Wenhao Wu, Boyang Lin   +6 more
doaj   +1 more source

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression

Frontiers in Bioengineering and Biotechnology, 2020
As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification ...
Li-Hong Peng, Li-Qian Zhou, Xing Chen, Xue Piao   +3 more
doaj   +1 more source