An Introduction to Machine Learning

Area of Science:

• Computer Science
• Pharmacology

Background:

• Machine learning (ML) and artificial intelligence (AI) have gained significant attention due to advances in data availability, computational power, and algorithms.
• The core concepts of ML, involving systems learning from data, have existed since the 1950s.
• Many ML principles are already familiar to professionals in pharmacometrics and clinical pharmacology.

Purpose of the Study:

• To introduce the fundamental concepts of ML to the pharmacometrics and clinical pharmacology community.
• To equip readers with the necessary knowledge to comprehend ML-related publications.
• To provide perspective on ML applications in molecular biology, pharmacometrics, and clinical pharmacology.

Main Methods:

• Conceptual introduction to machine learning principles.
• Literature review and synthesis of ML applications.
• Focus on foundational ideas rather than deep theoretical details.

Main Results:

• Readers will gain foundational understanding of ML.
• Readers will be better equipped to interpret ML literature.
• Contextualization of ML's role in relevant scientific fields.

Conclusions:

• ML offers valuable tools for pharmacometrics and clinical pharmacology.
• Understanding ML fundamentals is crucial for leveraging its potential.
• This work serves as a primer for researchers entering the field of ML in drug development and biological sciences.