References

Alanis-Lobato, G., C.V. Cannistraci and T. Ravasi, 2014. Exploring the genetics underlying autoimmune diseases with network analysis and link prediction. Proceedings of the 2nd Middle East Conference on Biomedical Engineering, February 17-20, 2014, IEEE, Doha, Qatar, ISBN:978-1-4799-4799-7, pp: 167-170.

Alshalalfa, M. and R. Alhajj, 2013. Using context-specific effect of miRNAs to identify functional associations between miRNAs and gene signatures. BMC. Bioinf., 14: 1-13.
PubMed  |  Direct Link  |  

Chen, L., B. Liu and C. Yan, 2018. DPFMDA: Distributed and privatized framework for miRNA-disease association prediction. Pattern Recognit. Lett., 109: 4-11.
CrossRef  |  Direct Link  |  

Chen, X., C.C. Yan, X. Zhang and Z.H. You, 2016. Long non-coding RNAs and complex diseases: From experimental results to computational models. Briefings Bioinf., 18: 558-576.
CrossRef  |  PubMed  |  Direct Link  |  

Chen, X., C.C. Yan, X. Zhang, Z.H. You and L. Deng et al., 2016. WBSMDA: Within and between score for MiRNA-disease association prediction. Sci. Rep., 6: 1-9.
CrossRef  |  PubMed  |  Direct Link  |  

Chen, X., Y.W. Niu, G.H. Wang and G.Y. Yan, 2017. Hamda: Hybrid approach for MiRNA-disease association prediction. J. Biomed. Inf., 76: 50-58.
CrossRef  |  PubMed  |  Direct Link  |  

Cheng, S., M. Guo, C. Wang, X. Liu and Y. Liu et al., 2015. MiRTDL: A deep learning approach for miRNA target prediction. IEEE/ACM. Trans. Comput. Boil. Bioinf., 13: 1161-1169.
CrossRef  |  PubMed  |  Direct Link  |  

Conze, P.H., V. Noblet, F. Rousseau, F. Heitz and R. Memeo et al., 2016. Random forests on hierarchical multi-scale supervoxels for liver tumor segmentation in dynamic contrast-enhanced CT scans. Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), April 13-16, 2016, IEEE, Prague, Czech Republic, ISBN:978-1-4799-2349-6, pp: 416-419.

Fan, H., F. Zhang, Y. Xu, X. Huang and G. Sun et al., 2010. An association study of DRD2 gene polymorphisms with schizophrenia in a Chinese Han population. Neurosci. Lett., 477: 53-56.
CrossRef  |  PubMed  |  Direct Link  |  

Hoe, H.S. and G.W. Rebeck, 2008. Functional interactions of APP with the apoE receptor family. J. Neurochem., 106: 2263-2271.
CrossRef  |  PubMed  |  Direct Link  |  

Hu, W., 2015. High accuracy gene signature for chemosensitivity prediction in Breast Cancer. Tsinghua Sci. Technol., 20: 530-536.
CrossRef  |  Direct Link  |  

Kukreti, R., S. Tripathi, P. Bhatnagar, S. Gupta and C. Chauhan et al., 2006. Association of DRD2 gene variant with schizophrenia. Neurosci. Lett., 392: 68-71.
CrossRef  |  PubMed  |  Direct Link  |  

Li, J.Q., Z.H. Rong, X. Chen, G.Y. Yan and Z.H. You, 2017. MCMDA: Matrix completion for MiRNA-disease association prediction. Oncotarget, 8: 21187-21199.
CrossRef  |  PubMed  |  Direct Link  |  

Paul, D., R. Su, M. Romain, V. Sebastien and V. Pierre et al., 2017. Feature selection for outcome prediction in Oesophageal Cancer using genetic algorithm and random forest classifier. Computerized Med. Imaging Graphics, 60: 42-49.
CrossRef  |  PubMed  |  Direct Link  |  

Phongwattana, T., W. Engchuan and J.H. Chan, 2015. Clustering-based multi-class classification of complex disease. Proceedings of the 2015 7th International Conference on Knowledge and Smart Technology (KST), January 28-31, 2015, IEEE, Chonburi, Thailand, ISBN:978-1-4799-6048-4, pp: 25-29.

Sakthivel, N.K., N.P. Gopalan and S. Subasree, 2016. A comparative study and analysis of DNA sequence classifiers for predicting human diseases. Prceedings of the International Conference on Informatics and Analytics (ICIA-16), August 25-26, 2016, ACM, Pondicherry, India, ISBN:978-1-4503-4756-3, pp: 1-5.

Sakthivel, N.K., N.P. Gopalan and S. Subasree, 2017. G-HR: Gene signature based HRF cluster for predicting human diseases. Intl. J. Pure Appl. Math., 117: 157-161.
CrossRef  |  Direct Link  |  

Shi, C., X. Kong, Y. Huang, P.S. Yu and B. Wu, 2014. HeteSim: A general framework for relevance measure in heterogeneous networks. IEEE. Trans. Knowl. Data Eng., 26: 2479-2492.
CrossRef  |  Direct Link  |  

Tahmasebipour, K. and S. Houghten, 2014. Disease-gene association using a genetic algorithm. Proceedings of the 2014 IEEE International Conference on Bioinformatics and Bioengineering, November 10-12, 2014, IEEE, Boca Raton, Florida, USA., ISBN:978-1-4799-7502-0, pp: 191-197.

Zahari, Z., L.K. Teh, R. Ismail and S.M. Razali, 2011. Influence of DRD2 polymorphisms on the clinical outcomes of patients with schizophrenia. Psychiatric Genet., 21: 183-189.
CrossRef  |  PubMed  |  Direct Link  |  

Zeng, X., Y. Liao, Y. Liu and Q. Zou, 2017. Prediction and validation of disease genes using HeteSim Scores. IEEE/ACM. Trans. Comput. Biol. Bioinf., 14: 687-695.
CrossRef  |  PubMed  |  Direct Link  |