View Proposal

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Proposer

Marta Vallejo

Title

The Pro-Act Dataset Exploring Machine Learning Opportunities in ALS Research

Goal

Description

The Pro-Act (Pooled Resource Open-Access ALS Clinical Trials Database) stores a wealth of data crucial for understanding Amyotrophic Lateral Sclerosis (ALS). This project aims to identify promising research questions and design proof-of-concept machine learning models utilising the Pro-Act dataset. The study of these research questions could uncover key insights into ALS progression, prognosis, and treatment response, culminating in the development of models and showcasing the potential of machine learning and the Pro-Act dataset in advancing ALS research. Objectives: 1.- Conduct exploratory data analysis to understand the structure and characteristics of the Pro-Act dataset. 2.- Identify research questions relevant to ALS prognosis, disease progression, and treatment response. 3.- Design and propose machine learning models to address the identified research questions. 4.- Develop a proof-of-concept machine learning model using a subset of the Pro-Act dataset.

Resources

Access to the Pro-Act dataset

Background

A basic understanding of machine learning principles, coupled with knowledge of Python programming, is important. Given the dataset's multimodality, the capability of selecting appropriate machine learning algorithms, preprocessing techniques, and model evaluation methods is important.

Url

External Link

Difficulty Level

High

Ethical Approval

Full

Number Of Students

3

Supervisor

Marta Vallejo

Keywords

machine learning, deep learning, healthcare

Degrees

Bachelor of Science in Computer Science
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Computing (2 Years)
Master of Science in Data Science
Master of Science in Software Engineering
Bachelor of Science in Computing Science
Postgraduate Diploma in Artificial Intelligence
Bachelor of Science in Statistical Data Science