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Machine learning approach to gene essentiality prediction: a review.

Olufemi Aromolaran^1,2, Damilare Aromolaran^1,2, Itunuoluwa Isewon^1,2

¹Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.

Briefings in Bioinformatics

|April 12, 2021

Summary

Machine learning aids in identifying essential genes, crucial for organism survival. However, predicting conditionally essential genes remains challenging due to limited labeled data for specific conditions.

Keywords:

conditional essentiality conditionally essential genes essential genes essential proteins feature selection supervised learning

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Essential genes are vital for organism survival and function.
Machine learning (ML) offers a complementary approach to experimental methods for identifying essential genes.
Current ML models face limitations in predicting conditionally essential genes, whose importance varies with environmental or biological conditions.

Purpose of the Study:

To review methods for essential gene prediction.
To highlight factors influencing the effectiveness of computational essential gene prediction.
To discuss limitations of ML in predicting conditional essentiality.

Main Methods:

Comparative analysis of feature categories (gene sequence, protein sequence, network topology, homology, gene ontology) for essentiality prediction in Caenorhabditis elegans.
Examination of various ML approaches and their strengths/limitations.
Discussion of data requirements for training predictive models.

Main Results:

Gene ontology-based features showed high correlation with biological functions.
Network topology features provided the highest discriminatory power for essentiality prediction.
A key limitation is the lack of labeled data for specific conditions, hindering conditional essentiality prediction.

Conclusions:

Feature selection and ML technique choice are critical for effective essential gene prediction.
Gene ontology and network topology features offer valuable insights.
Cooperative machine learning approaches may improve conditional essentiality predictions.