Results 1 to 10 of about 1,960,778 (332)

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction [PDF]

Frontiers in Genetics, 2018
In recent years, microRNAs (miRNAs) have been confirmed to be involved in many important biological processes and associated with various kinds of human complex diseases.
Xing Chen, Jia Qu, Jun Yin
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

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

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

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

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

MambaCAttnGCN+: a comprehensive framework integrating MambaTextCNN, cross-attention and graph convolution network for piRNA-disease association prediction. [PDF]

Sci Rep
Yao D, Li X, Zhan X, Zhang B, Zhang J.
europepmc   +2 more sources

Genomic prediction and quantitative trait locus discovery in a cassava training population constructed from multiple breeding stages [PDF]

, 2020
Open Access Article; Published online: 11 Dec 2019Assembly of a training population (TP) is an important component of effective genomic selection‐based breeding programs.
Egesi, C., Jannink, Jean-Luc, Kawuki, R., Kulembeka, H., Lozano, R., Mrema, E., Mtunda, K., Ozimati, A., Rabbi, Ismail Y., Salum, K., Somo, M., Wolfe, M.D.   +11 more
core   +1 more source

A new integrated framework for the identification of potential virus–drug associations

Frontiers in Microbiology, 2023
IntroductionWith the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease.
Jia Qu, Zihao Song, Xiaolong Cheng, Zhibin Jiang, Jie Zhou   +4 more
doaj   +1 more source