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An Introduction to Machine Learning.

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Machine learning (ML) and artificial intelligence (AI) are increasingly powerful tools. This paper introduces foundational ML concepts to pharmacometrics and clinical pharmacology researchers for better understanding of current applications.

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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.