Abstract
There is increasing evidence that long non-coding RNAs (lncRNAs) are closely related to many human diseases. Developing powerful computational models for potential lncRNA-disease association identification would facilitate biomarker identification and drug discovery for human disease diagnosis, treatment, prognosis and prevention. Now there exist a number of methods specially for this problem based on inductive matrix completion, random walk or classification. In terms of this issue, classification has just come to the fore. Extracting important features from disease network and RNA network, namely network embedding, is the top priority. Moreover, taking the complexity into consideration, genetic algorithm is adopted to tune the hyper-parameters of our network embedding model. Due to a lack of negative samples, we also exploit Positive-Unlabeled (PU) learning to help out. In brief, we propose a weighted bagging lightGBM model for lncRNA-disease association prediction based on network embedding and PU learning.
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References
Crick, F.: General nature of the genetic code for proteins. Nature 192, 1227–1232 (1961)
Yanofsky, C.: Establishing the triplet nature of the genetic code. Cell 128, 815–C818 (2007)
Mattick, J.S., Makunin, I.V.: Non-coding RNA. Hum. Mol. Genet. 15, R17–R29 (2006)
Djebali, S., et al.: (2012)
Lander, E.S., et al.: Initial sequencing and analysis of the human genome. Nature 409(6822), 860–C921 (2001)
Kapranov, P.: RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 316, 1484–1488 (2007)
Mercer, T.R.: Long non-coding RNAs: insights into functions. Nat. Rev. Genet 10, 155–159 (2009)
Wapinski, O., Chang, H.Y.: Long noncoding RNAs and human disease. Trends Cell Biol. 21, 354–361 (2011)
Bu, D.: NONCODE v3: integrative annotation of long noncoding RNAs. Nucleic Acids Res. 40, 210–215 (2012)
Chen, X., Yan, G.Y.: Novel human lncRNA-disease association inference based on lncrna expression profiles. Bioinform. 29(20), 2617–2624 (2013)
Wang, L., He, W., Hao, D., Liu, S., Zhou, M.: Inferring novel lncRNA-disease associations based on a random walk model of a lncRNA functional similarity network. Mol. BioSyst. 10(8), 2074–2081 (2014)
Lan, W., et al.: LDAP: a web server for lncRNA-disease association prediction. Bioinform. 33(3), 458–460 (2016)
Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. In: Advances in Neural Information Processing Systems (2015)
Emmanuel, D., Bassett, B.A.: EDEN: evolutionary deep networks for efficient machine learning (2017)
Michael, D., Xavier, B., Pierre V.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems (2016)
Thomas, K.: https://tkipf.github.io/graph-convolutional-networks/
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Charles, E., Keith, N.: Learning classifiers from only positive and unlabeled data (KDD). In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 213–220. ACM (2008)
Mordelet, F.: A bagging SVM to learn from positive and unlabeled examples. Pattern Recognit. Lett. 37, 201–209 (2014)
Claesen, M.: A robust ensemble approach to learn from positive and unlabeled data using SVM base models. Neurocomput. 160, 73–84 (2015)
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Chen, X., Liu, X. (2018). A Weighted Bagging LightGBM Model for Potential lncRNA-Disease Association Identification. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_27
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DOI: https://doi.org/10.1007/978-981-13-2826-8_27
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