Dec. 29, 2020
13:00
-14:00

​​​​​​Zoom Link​​​

Speaker: Dr. Isana Veksler-Lublinsky

Title: Machine-learning-based analysis of the evolutionary-conservation of microRNA-target interactions


Abstract:

Gene expression is the process by which the genetic information encoded in the DNA, is used to synthesize proteins that perform most of the functions in the cells of all living organisms. This process is highly regulated, and errors can cause a broad range of diseases. MicroRNAs (miRNAs) are evolutionarily conserved small RNAs that play a major role in regulating gene expression via hybridization to complementary sequences on target mRNAs. MicroRNAs are found both in body fluids and tissues, and their composition and levels vary between normal and different pathological conditions, including cancer. Thus, microRNAs have emerged as a class of promising non-invasive biomarkers for rapid detection of human disease and targets for therapeutic intervention.

Identifying miRNA target sites on mRNAs is a fundamental step in understanding miRNA function. Due to the technical challenges involved in the application of experimental methods, datasets of direct bona-fide miRNA targets exist only for a few model organisms. Machine learning (ML) based target prediction methods were successfully trained and tested on some of these datasets. Nevertheless, there is a need to apply target prediction tools to other organisms as well, where experimental data is not available.

We examined miRNA-target interaction rules and features and used data science and ML approaches to investigate whether these rules are transferable between species. For our analysis, we used available datasets of direct miRNA-target interactions. Our results indicate that the transferability of miRNA-targeting rules between organisms depends on several factors, including evolutionary distance, the composition of seed families, and the diversity of interactions within the datasets. Our study lays the foundation for the future developments of target prediction tools that could be applied to "non-model" organisms for which minimal experimental data is available.

Dr. Isana Veksler-Lublinsky​​

Bio:

Dr. Isana Veksler-Lublinsky is a Senior Lecturer in the Department of Software and Information Systems Engineering, at Ben-Gurion University of the Negev (BGU), Israel, since 2017. She received her B.Sc., M.Sc. and Ph.D. degrees in Computer Science and Bioinformatics from BGU, and did her post-doctoral research at the University of Massachusetts Medical School.

Her research group develops computational techniques and applies them to study complex biological phenomena. The group performs multidisciplinary research in close collaboration with experimental biologists and clinicians from Israel and worldwide and specializes in several research domains. Current research focuses on basic questions in small RNA biology (e.g., functions, evolution, biogenesis) and the applicability of miRNAs in the diagnosis of human disease. In addition, they investigate the diversity of bacterial genomes to identify genetic factors responsible for different traits e.g., bacterial pathogenicity and antibiotic resistance. Group's website: https://bioinfolab.weebly.com/