DCTGM: A Novel Dual-channel Transformer Graph Model for miRNA-disease Association Prediction

Chen X, Yan CC, Luo C. Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity. Sci Rep. 2015;5:11338.

Ponting CP, Oliver PL, Reik W. Evolution and functions of long noncoding RNAs. Cell. 2009;136:629–41.

Szymański M, Barciszewski J. Noncoding RNAs in human diseases. Springer, Berlin Heidelberg. 2008;12:861–74.

Ambros V. The functions of animal microRNAs. Nature. 2004;431:350–5.

Afshar S, Warden E, Manochehri H, Saidijam M. Application of artificial neural network in miRNA biomarker selection and precise diagnosis of colorectal cancer. Iran Biomed J. 2018;23(3):175–83.

Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97.

Cheng AM, Byrom MW, Shelton J. Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res. 2005;33:1290–7.

Cui Q, Yu Z, Purisima EO. Principles of microRNA regulation of a human cellular signaling network. Mol Syst Biol. 2006;2:46.

Esquela-Kerscher A, Slack FJ. Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer. 2006;6:259–69.

Calin G, Croce C. MicroRNA signatures in human cancers. Nat Rev Cancer. 2006;6:857–66.

Lu M, Zhang Q, Min D. An analysis of human microRNA and disease associations. PLoS ONE. 2008;3:e3420.

Miska EA. How microRNAs control cell division, differentiation and death. Curr Opin Genet Dev. 2005;15:563–8.

Jiang Q, Hao Y, Wang G. Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Syst Biol. 2010;4:S2.

Shi H, Xu J, Zhang G. Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC Syst Biol. 2013;7:101.

Chen X, Yan CC, Zhang X. RBMMMDA: predicting multiple types of disease-microRNA associations. Sci Rep. 2015;5:13877.

Luo J, Xiao Q, Liang C. Predicting microRNA-disease associations using Kronecker regularized least squares based on heterogeneous omics data. IEEE Access. 2017;5:2503–13.

Xuan P, Sun H, Wang X. Inferring the disease-associated miRNAs based on network representation learning and convolutional neural networks. Int J Mol Sci. 2019;20:3648.

Peng J, Hui W, Li Q. A learning-based framework for miRNA-disease association identification using neural networks. Bioinformatics. 2019;35:4364–71.

Pang S, Zhuang Y, Wang X, et al. EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network. BMC Med Inform Decis Mak. 2021;(1).

Bhaskar H, Al-Mualla M. Spontaneous Vs. posed facial expression analysis using deformable feature models and aggregated classifiers[C]. Proceedings of the International Conference on Information FUSION. 2013.

Tanveer M, Rashid AH, Ganaie MA. Classification of Alzheimer’s disease using ensemble of deep neural networks trained through transfer learning. IEEE J Biomed Health Inform. 2022;26:1453–63.

Mt A, Mag A, Pns B. Ensemble of classification models with weighted functional link network. Appl Soft Comput. 2021;107.

Gautam C, Mishra PK. Minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data. Neural Netw. 2020;123:191–216.

Gautam C, Tiwari A, Tanveer M. Graph-embedded multi-layer kernel extreme learning machine for one-class classification or (graph-embedded multi-layer kernel ridge regression for one-class classification). 2019.

Casalino G, Castellano G, Consiglio A, et al. MicroRNA expression classification for pediatric multiple sclerosis identification. J Ambient Intell Humaniz Comput. 2021:1–10.

Cui Q. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics. 2010;26:1644–50.

Ping X, Ke H, Guo M. Correction: Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLOS ONE 2013;8:e70204

Lipscomb CE. Medical subject headings (MeSH). Bull Med Libr Assoc. 2000;88:265–6.

Twan, Laarhoven V , Sander B , et al. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics. 2011;27:3036–43.

Li Y, Qiu C, Tu J, Geng B. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. 2014;42:D1070–4.

Hamilton W L , Ying R , Leskovec J . Inductive Representation Learning on Large Graphs. 2017.

Vaswani A , Shazeer N , Parmar N , et al. Attention Is All You Need. arXiv, 2017.

Zhu HY, Huang ZA, Zhu ZX. PBMDA: a novel and effective path-based computational model for miRNA-disease association prediction. PLoS Comput Biol. 2017;13:e1005455.

Qu Y, Zhang H, Lyu C. LLCMDA: a novel method for predicting miRNA gene and disease relationship based on locality-constrained linear coding. Front Genet 2018;9.

Zhang L, Liu B, Li Z. Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model. BMC Bioinformatics. 2020;21:470.

Zhou S, Wang S, Wu Q. Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression. Comput Biol Chem. 2020;85: 107200.

Zhen Y, Fei R, Liu C. dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genomics. 2010;11:1–8.

Galderisi U, Cipollaro M, Giordano A. Stem cells and brain cancer. Cell Death Differ. 2006;13:5.

Li Z, Li J, Nie R. A graph auto-encoder model for miRNA-disease associations prediction. Brief Bioinform. 2021;22:bbaa240.

Chow WH, Dong LM, Devesa SS. Epidemiology and risk factors for kidney cancer. Nat Rev Urol. 2010;7:245–57.

Goh JN, Loo SY, Datta A. microRNAs in breast cancer: regulatory roles governing the hallmarks of cancer. Biol Rev. 2015;91:409–28.