Affiliation:
1. Roxbury Latin School
2. Brigham and Women's Hospital
Abstract
Abstract
Deadly diseases, like cancer and Alzheimer’s, are complex conditions with elusive cures that consistently form the leading causes of death globally every year. MiRNAs have been shown to play a powerful regulatory role in the progression of these diseases, forming promising novel drug targets. Current experimental methods, like differential expression analysis, can discover disease-associated miRNAs, yet many of these miRNAs play no functional role in disease progression. Interventional experiments used to discover disease-causing miRNAs can be time consuming and costly. We present DisiMiR: a novel computational method that predicts pathogenic miRNAs by inferring biological characteristics of pathogenicity including network influence and gene conservation. DisiMiR separates disease-causal miRNAs from merely disease-associated miRNAs and was accurate in four diseases: breast cancer (0.817 AUC), Alzheimer's (0.727 AUC), gastric cancer (0.843 AUC) and hepatocellular cancer (0.949 AUC). Additionally, DisiMiR can generate hypotheses effectively: 41.2% of its false positives that are mentioned in the literature have been confirmed to be causal. In this work, we show that DisiMiR is a powerful tool that can be used to efficiently and flexibly predict pathogenic miRNAs in an expression dataset, for the further elucidation of disease mechanisms and the potential identification of novel drug targets.
Publisher
Research Square Platform LLC
Cited by
1 articles.
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