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The aim of the project is to design, develop and evaluate novel strategies to enhance the quality
of the 3D models produced by I-Tasser, a leading ab-initio protein structure prediction
framework. Those novel strategies will be based on the integration of accurate contact maps
that will be generated by deep learning architectures.
Successful completion of the project requires addressing the following scientific objectives:
1. Contact map prediction by enhancing standard Convolutional Neural Network (CNN)
architectures by integrating a multi-distance approach and autoencoder stacks to increase
the representation richness while reducing computational complexity.
2. Contact map prediction when dealing with small training set, e.g. for membrane proteins,
by adapting and enhancing Small Data-Driven CNN (SDD-CNN) architectures.
3. Binarisation of the previously developed architectures so that contact map predictions can
be performed much faster without requirement of specialised hardware.
Surrey and Borders partnership trust NHS
From November 2019 to July 2020
the digital parent company - Guildford
From September 2019
To November 2019
from July 2019 to September 2019
First Class BSc Software Engineering (Sandwich) Kingston University July 2019
BSc Hons Software Engineering (Final year Results) Module Programming IIIGrade AModule Dependable SystemsGrade AModule Individual ProjectGrade AModule Internet SecurityGrade A