Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier.
| Title: | Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier. |
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| Authors: | Pyman B; School of Computing, Queen's University, Kingston, Ontario K7L 3N6, Canada http://www.queensu.ca/, pyman@cs.queensu.ca.; Sedghi A; Azizi S; Tyryshkin K; Renwick N; Mousavi P |
| Source: | Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing [Pac Symp Biocomput] 2019; Vol. 24, pp. 160-171. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: World Scientific Country of Publication: United States NLM ID: 9711271 Publication Model: Print Cited Medium: Internet ISSN: 2335-6936 (Electronic) Linking ISSN: 23356928 NLM ISO Abbreviation: Pac Symp Biocomput |
| Imprint Name(s): | Publication: : Hackensack, NJ : World Scientific; Original Publication: Singapore ; River Edge, NJ : World Scientific, c1995- |
| MeSH Terms: | Deep Learning*; MicroRNAs/*genetics ; Neoplasms/*genetics; Databases, Nucleic Acid/statistics & numerical data ; Diagnosis, Computer-Assisted/methods ; Gene Expression Profiling/statistics & numerical data ; MicroRNAs/classification ; Neoplasms/classification ; Neoplasms/diagnosis ; Computational Biology ; Female ; Gene Expression Regulation, Neoplastic ; Gene Ontology ; High-Throughput Nucleotide Sequencing ; Humans ; Male ; Molecular Sequence Annotation ; Neural Networks, Computer ; Sequence Analysis, RNA |
| Abstract: | Background: MicroRNAs (miRNAs) are small, non-coding RNA that regulate gene expression through post-transcriptional silencing. Differential expression observed in miRNAs, combined with advancements in deep learning (DL), have the potential to improve cancer classification by modelling non-linear miRNA-phenotype associations. We propose a novel miRNA-based deep cancer classifier (DCC) incorporating genomic and hierarchical tissue annotation, capable of accurately predicting the presence of cancer in wide range of human tissues.; Methods: miRNA expression profiles were analyzed for 1746 neoplastic and 3871 normal samples, across 26 types of cancer involving six organ sub-structures and 68 cell types. miRNAs were ranked and filtered using a specificity score representing their information content in relation to neoplasticity, incorporating 3 levels of hierarchical biological annotation. A DL architecture composed of stacked autoencoders (AE) and a multi-layer perceptron (MLP) was trained to predict neoplasticity using 497 abundant and informative miRNAs. Additional DCCs were trained using expression of miRNA cistrons and sequence families, and combined as a diagnostic ensemble. Important miRNAs were identified using backpropagation, and analyzed in Cytoscape using iCTNet and BiNGO.; Results: Nested four-fold cross-validation was used to assess the performance of the DL model. The model achieved an accuracy, AUC/ROC, sensitivity, and specificity of 94.73%, 98.6%, 95.1%, and 94.3%, respectively.; Conclusion: Deep autoencoder networks are a powerful tool for modelling complex miRNA-phenotype associations in cancer. The proposed DCC improves classification accuracy by learning from the biological context of both samples and miRNAs, using anatomical and genomic annotation. Analyzing the deep structure of DCCs with backpropagation can also facilitate biological discovery, by performing gene ontology searches on the most highly significant features. |
| Substance Nomenclature: | 0 (MicroRNAs) |
| Entry Date(s): | Date Created: 20190314 Date Completed: 20190823 Latest Revision: 20191210 |
| Update Code: | 20260130 |
| PMID: | 30864319 |
| Database: | MEDLINE |
Journal Article