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Protein sequential, structural and functional analysis using deep learning

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thesis
posted on 01.03.2019, 02:17 authored by YING XU
This thesis introduces the application of deep learning in protein analysis. Three research questions are proposed in terms of protein sequence representation, deep learning framework design and biological background knowledge incorporation. To address these research questions, the distributed representation of protein sequences is proposed and evaluated in sequence-level, residue-level and biological interaction-level prediction tasks; a deep learning framework is proposed for any protein sequence-based residue-level prediction tasks; two multitask deep learning frameworks are applied to take advantage of the correlation between protein structural properties; and a deep transfer learning framework is proposed to incorporate the hierarchical classification systems in biology.

History

Campus location

Australia

Principal supervisor

Campbell Wilson

Additional supervisor 1

James Whisstock

Additional supervisor 2

Jiangning Song

Additional supervisor 3

Jue (Grace) Xie

Year of Award

2019

Department, School or Centre

Caulfield School of IT

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Information Technology