Abstract

With the significant drop in the cost of many high-throughput technologies, vast amounts of biological data are being generated and made available to researchers. Machine learning (ML) has emerged as a powerful tool for analyzing data related to cellular processes, genomics, proteomics, post-translational modifications, metabolism, and drug discovery, offering the potential for transformative medical advancements.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• How should data provenance be documented to ensure transparency in AI research?
• What strategies can be employed to manage redundancy between training and test datasets in biological research?
• Why is it important to make datasets and model configurations publicly available, and how can this be achieved?
• What are the key considerations in selecting and documenting optimization algorithms and parameters for AI models?
• How can the interpretability of AI models be enhanced, and why is this crucial in fields like drug design and diagnostics?

Learning Objectives

• Explain the importance of data provenance and dataset splits in ensuring the integrity and reproducibility of AI research.
• Develop a comprehensive plan for documenting and sharing AI model configurations, datasets, and evaluation results to enhance transparency and reproducibility in their research.