Where Do We Stand in Regularization for Life Science Studies?

Area of Science:

• Life Sciences
• Bioinformatics
• Computational Biology

Background:

• Biologists and bioinformaticians increasingly use machine learning for large-scale data analysis.
• Life science datasets present unique challenges: low signal-to-noise, high dimensionality, and limited samples.
• Selecting appropriate data analysis pipelines is crucial for reliable results.

Purpose of the Study:

• To provide a comprehensive overview of regularization techniques in life science studies.
• To explain the concept and implementation of regularization for robust model building.
• To guide the selection of regularization methods based on data type and analysis goals.

Main Methods:

• Describing the fundamental concept of regularization as adding information to solve ill-posed problems.
• Presenting four general life science problem types where regularization is essential.
• Enumerating various regularization techniques with examples and case studies for each problem type.

Main Results:

• Demonstrating the impact and importance of regularization for handling complex biological data.
• Providing an intuitive understanding of different regularization implementations.
• Offering a unified framework for applying regularization techniques to diverse data types.

Conclusions:

• Regularization is a fundamental tool for achieving robust machine learning models in life sciences.
• Appropriate selection of regularization methods is critical for successful analysis of challenging biological datasets.
• This work serves as a guide for applying regularization across various life science research scenarios.