View Proposal

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Proposer

Marwan Fuad

Title

Machine Learning Applications in Immunology and Personalized Medicine

Goal

Applying deep learning for prediction of Band T-cell epitopes

Description

Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics. Based on information from whole-genome sequencing, exome sequencing and RNA sequencing, it is possible to characterize an individual’s human leukocyte antigen (HLA) allotype. New opportunities for translational applications of epitope prediction arose, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer. Several approaches based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM) have been successfully applied for HLA class I binding prediction. Applications to HLA class II binding prediction were also applied but not with as much success. Applications to B-cell epitopes prediction were also applied but with less success

Resources

Computational Immunology Models and Tools, Josep Bassaganya-Riera (2016) Immunoinformatics, Rajat K. De. Springer (2014)

Background

Url

Difficulty Level

Easy

Ethical Approval

Full

Number Of Students

1

Supervisor

Marwan Fuad

Keywords

immunoinformatics, deep learning

Degrees

Bachelor of Science in Computer Science
Master of Science in Artificial Intelligence
Master of Science in Data Science
Bachelor of Science in Computing Science
Bachelor of Science in Statistical Data Science