Reinforcement Learning-Based Framework for RNA Secondary Structure Prediction
MIE Department Seminar
April 2, 2025
10:00 AM - 11:00 AM America/Chicago
Location
ERF 1043 or Zoom at https://uic.zoom.us/j/85737083583?pwd=saBlpSfs2iH7giw2ghxVKtLQBy7XVt.1
Address
842 W. Taylor St., Chicago, IL 60607
Calendar
Download iCal FilePresenter: Menghan Liu, PhD, Arizona State University
Location: ERF 1043 or via Zoom
Abstract: Ribonucleic acid (RNA) is a fundamental biological molecule that is essential to all living organisms, performing a versatile array of cellular tasks. The function of many RNA molecules is strongly related to the structure it adopts. As a result, many efforts were put to solve the RNA secondary structure prediction problem: given a sequence of nucleotides, return a probable list of base pairs, referred to as the secondary structure prediction.
Early algorithms have largely relied on finding the structure with minimum free energy. However, the predictions rely on effective simplified free energy models that may not correctly identify the correct structure as the one with the lowest free energy. In light of this, new, data-driven approaches that not only consider free energy, but also use machine learning techniques to learn motifs have also been investigated, and have recently been shown to outperform free energy based algorithms on several experimental data sets.
In this work, we introduce the new ExpertRNA algorithm that provides a modular framework which can easily incorporate an arbitrary number of rewards (free energy or non-parametric/data driven) and secondary structure prediction algorithms. We argue that this capability of ExpertRNA has the potential to balance out different strengths and weaknesses of state-of-the-art folding tools. We test the ExpertRNA on several RNA sequence-structure data sets, and we compare the performance of ExpertRNA against a state-of-the-art folding algorithm. We find that ExpertRNA produces, on average, more accurate predictions of non-pseudoknotted secondary structures than the structure prediction algorithm used, thus validating the promise of the approach.
Speaker Bio: Menghan Liu received her Ph.D in industrial engineering from Arizona State University and is a postdoctoral fellow in Radiation Oncology at the University of California, San Francisco. Her research focuses on data-driven optimization and control methods development based on simulation and operation research in dynamic and stochastic environment. She centers her research in the field of healthcare and therapeutic research, with applications ranging from pharmaceutical product design to clinical cancer study. She is also a CAMPEP-certified medical physicist in progress. Liu is a passionate educator and a caring mentor. She always encourages students to be active learners and critical thinkers and is deeply enthusiast about interdisciplinary education and collaborations.
Date posted
Mar 28, 2025
Date updated
Apr 1, 2025