Deep learning for de novo RNA secondary structure prediction

Research output: ThesisDoctoral Thesis

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Abstract

Over the last decade, deep learning has exploded for many applications, ranging from huge advancements in computer vision and natural language processing to biological applications like drug discovery or protein structure prediction (such as DeepMind’s AlphaFold). More recently, deep learning has been applied to RNA secondary structure prediction, with many papers reporting impressive results. This thesis is structured around four main contributions to deep learning for de novo RNA secondary structure prediction: in silico probing experiments, benchmarking existing deep learning models, learning on synthetic data sets, and finally, family prediction and generative models.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Supervisors/Advisors
  • Ward, Max, Supervisor
  • Wise, Michael, Supervisor
  • Datta, Amitava, Supervisor
Thesis sponsors
Award date22 Jun 2023
DOIs
Publication statusUnpublished - 2023

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