Student: “Rafail N. Adam”
Program: “Data Science and Information Technologies”
Title: “MicroRNA target prediction with novel machine learning methods beyond quadratic attention”
Abstract:
MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate
gene expression, primarily by suppressing mRNA translation through
binding to MicroRNA Response Elements (MREs). They are involved in
many physiological processes, and their dysregulation is associated with
disease. Advances in multi-omics and personalized medicine have
enabled miRNA discovery and analysis through high-throughput
techniques such as small RNA-seq, AGO-eCLIP, and AGO-CLASH, which
quantify miRNA–mRNA interactions. Because a single miRNA can target
multiple mRNAs, understanding their regulatory effects requires
advanced computational approaches. Consequently, numerous machine
learning and deep learning models have been developed for miRNA
target prediction, either by identifying valid miRNA–MRE interactions or
by predicting gene-level effects using transcriptomic data. This thesis
presents the development of a machine/deep-learning model that
predicts interactions between miRNAs and MREs. AGO2-eCLIP data were
used to train and evaluate several models, resulting in a weighted
ensemble approach capable of distinguishing valid from invalid
interactions with Average Precision performance comparable to current
state-of-the-art methods. The model also provides interpretability by
estimating the contribution of each feature to the final prediction.
Date-Time: 08/06/2026 – 11:00 AM
Examination Committee:
Prof. Martin Reczko
Prof. Artemis Hatzigeorgiou
Prof. Alexandros Dimopoulos

Msc Thesis Presentation
Monday, June 8 · 11:00am – 12:00pm
Time zone: Europe/Athens
Google Meet joining info
Video call link: https://meet.google.com/wfp-snxk-trn