View Proposal

Proposer
Heba Elshimy
Title
Personalized Cancer Treatment: Predicting Drug Response Using Genomic Profiles and Deep Learning
Goal
Description
The same drug compound could have various levels of responses in different patients. To design drug for individual or a group with certain characteristics is the central goal of precision medicine. For example, the same anti-cancer drug could have various responses to different cancer cell lines. This task aims to predict the drug response rate given a pair of drug and the cell line genomics profile. Impact: The combinations of available drugs and all types of cell line genomics profiles are very large while to test each combination in the wet lab is prohibitively expensive. A machine learning model that can accurately predict a drug's response given various cell lines in silico can thus make the combination search feasible and greatly reduce the burdens on experiments. The fast prediction speed also allows us to screen a large set of drugs to circumvent the potential missing potent drugs.
Resources
Dataset: https://tdcommons.ai/multi_pred_tasks/drugres/ Relevant research: 1. Attention is all you need: utilizing attention in AI-enabled drug discovery: https://doi.org/10.1093/bib/bbad467 2. Deep learning methods for drug response prediction in cancer: Predominant and emerging trends: https://doi.org/10.3389/fmed.2023.1086097 3. DeepTTA: a transformer-based model for predicting cancer drug response: https://academic.oup.com/bib/article/23/3/bbac100/6554594?login=false#355037663 4. TCR: A Transformer Based Deep Network for Predicting Cancer Drugs Response: https://arxiv.org/abs/2207.04457
Background
Python, PyTorch/TensorFlow/Keras
Url
Difficulty Level
High
Ethical Approval
None
Number Of Students
1
Supervisor
Heba Elshimy
Keywords
deep learning, genomics, personalized medicine
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
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