Identification of OncomiRs by ILP Keerthieshwar V & Ramakrishnan S Department of Biotechnology, Kumaraguru College of Technology, Coimbatore, TN
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Overview
Method
I MicroRNAs (miRNAs) are endogenous ∼ 22 nucleotide RNAs that can play important regulatory roles in animals and plants by targeting mRNAs for cleavage or translational repression. I Our main objective in this study is identification of miRNA which involves in cancer cells. I In our study we use a machine language called inductive logic programming for identification of oncomiR and non-oncomiR.
I I I I
Introduction I The regulation of the miRNA is completely dependent on the binding efficiency of the miRNA with the mRNA and the pattern in which they bind with them. I They regulate diverse bioinformatics data indicating that each miRNA can control hundreds of gene targets. I Experimental cloning and in silico methods are the two important strategies so far used to predict the miRNA. I In a cloning-based approach, distinct ∼22 nucleotide RNA transcripts were first isolated and then intensively cloned and sequenced. I However, these methods are highly biased towards abundantly and/or ubiquitously expressed miRNAs; only abundant miRNA genes can be easily detected I Thus, computational approaches have been developed to complement experimental approaches to miRNA gene identification.
Only experimental valid dataset were utilized for the study. miRNAs were obtained from miRBASE 19.0 mRNA genes were collected from GENBANK For the purpose of the study, only 3’Untranslated Regions from humans were considered. I Database which collected is taken as background hypothesis (B) for identification OncomiR using inductive logic programming. I Using background knowledge, cancer occurring genes (positive results, E+) were separate from normal genes (negative results. I From the results which occurred, OncomiR, cancer occurring genes can be identified through logic based machine language.
Workflow
Fig. 3: General Pipeline of the Work
Tools Inductive Logic Programming I I I I I
It is useful to learn target function as a set of if-then-rules. Rules are learned directly. Designed to learn first-order rules (i.e. including variables) Sequential covering to incrementally grow the final set of rules The aim of ILP concept learning is to discover, from a given set of preclassified examples, a set of classification rules with high predictive power. I In ILP, hypothesis induction is the sum of hypothesis abduction and hypothesis justification.
I Waikato Environment for Knowledge Anlaysis (WEKA) I Pre-processing of dataset I Feature selection ? Unsupervised method ? PCA I Ensemble construction I Optimization I R-package I For graphical Visualization
Conclusion miRNA Regulation Mechanism
I In summary, we present an integrated approach identifying miRNAs associated with cancer using ILP I Further, work will progress on identifying feature importance and classifying mIRNAs into specific cancer types. I Inductive logic programming machine language can be useful in the prediction of oncomiR sequences.
Acknowledgement References
Fig. 1: miRNA Regulation
Structure of an miRNA
I TheRam authors would like to acknowledge Kothandan, Sumit Biswas, "Identifying microRNAs involved in I cancer Kumaraguru College Technology for machines" support Computational Biolpathway usingofsupport vector I ogy Dr. Ram K for guidance and Chemistry 55 (2015) 31–36. I David P. Bartel1,2, "MicroRNAs: Genomics, Biogenesis, Mechanism, and Function" Cell, Vol. 116, 281–297, January 23, 2004. I Qin, T., Liu, T.Y., Zhang, X.D., Chen, Z., Ma, W.Y.: A study of relevance propagation for web search. In: SIGIR 2005: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 2005, 408–415. I Todorovski, L., Bridewell, W., Shiran, O., Langley, P.: Inducing hierarchical process models in dynamic domains. In: Proceedings of the Twentieth National Conference on Artificial Intelligence, Pittsburgh, PA, 2005, 892–897. I Giraud-Carrier, C., Vilalta, R., Brazdil, P.: Introduction to the special issue on meta-learning. Machine Learning 2004, 54, 187–193. I McCreath, E., Sharma, A.: Extraction of meta-knowledge to restrict the hypothesis space for ILP systems. In: Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence, Canberra, Australia, 1997, 75–82. I Huang, Y., Selman, B., Kautz, H.A.: Learning declarative control rules for constraint-based planning. In: Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA, 2006, 415–422. Morgan Kaufmann, San Francisco .
