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Navigating the pitfalls of applying machine learning in genomics.

Sean Whalen¹, Jacob Schreiber², William S Noble³

¹Gladstone Institutes, San Francisco, CA, USA.

Nature Reviews. Genetics

|November 27, 2021

Summary

Supervised machine learning (ML) in genomics faces challenges due to unmet statistical assumptions and data biases. This review highlights common pitfalls and offers solutions for effective ML application in genomic research.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

The increasing volume of genomic, epigenomic, transcriptomic, cheminformatic, and proteomic data has spurred machine learning (ML) adoption in genomics.
Existing ML software often relies on statistical assumptions not aligned with complex biological systems.

Purpose of the Study:

To identify and illustrate common pitfalls when applying supervised ML in genomics research.
To provide solutions and define appropriate use cases for ML in genomics.

Main Methods:

Review of common challenges in applying supervised ML to genomic data.
Analysis of how genomic data structures can impact ML model performance and predictions.
Discussion of potential solutions and best practices for ML in genomics.

Main Results:

Genomic data structures can introduce biases affecting ML model evaluation and predictive accuracy.
Standard ML statistical assumptions are frequently violated in biological data.
Identified specific pitfalls in applying supervised ML to genomics.

Conclusions:

Addressing data biases and assumption violations is crucial for reliable ML in genomics.
Careful consideration of ML methods and data characteristics is necessary for successful genomic research.
ML modeling holds significant potential in genomics when applied appropriately.