Tree-based machine learning algorithms for identifying minimal set of miRNA biomarkers for cancer diagnosis and molecular subtyping

سال انتشار: 1397
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 423

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شناسه ملی سند علمی:

CIGS15_206

تاریخ نمایه سازی: 13 بهمن 1398

چکیده مقاله:

Introduction: Cancer is a complex disease and its effective treatment needs affordable diagnosis and subtyping signatures. While the use of machine learning approach in clinical computation biology is still in its infancy, the prevalent approach in identifying molecular biomarkers remains to be screening of all biomarkers by differential expression analysis. Many of these attempts used miRNAs expression data in breast cancer and amounted to the multitude of differentially expressed miRNAs in this cancer; hence, the minimal set of miRNA biomarkers to classify cancer is yet to be identified. Methods: Availability of diverse and vast amount of cancer datasets like The Cancer Genome Atlas facilitated the molecular profiling of patients’ tumors and introduced new challenges like clinical grade interpretations from big data. In this study, miRNA expression dataset of cancer patients from TCGA database was used to develop prediction models from which miRNA biomarkers were identified for diagnosis and molecular subtyping of this cancer. I took the advantage of interpretability of tree-based classification models to extract their rules and identify minimal set of biomarkers in this cancer. Results: Empirical negative control miRNAs in cancer obtained and used to normalize the dataset. Tree-based machine learning models trained in my analysis used to classify tumors from normal samples, and further classify these tumors into major subtypes of cancer. The most important miRNAs in classification were also presented.

نویسندگان

Masih Sherafatian

Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran