Machine learning applications for transcription level and phenotype predictions.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Predicting phenotypes from genomic variations is challenging due to environmental influences on gene expression.
• Omic data and machine learning (ML) offer new opportunities to understand gene expression and phenotypes.

Purpose of the Study:

• To summarize fundamental ML concepts for molecular biologists.
• To highlight ML applications in transcriptomics for predicting gene expression and phenotypes.
• To promote ML adoption in molecular biology and synthetic biology.

Main Methods:

• Focus on transcriptomics due to data abundance and reproducibility.
• Describes two ML tasks: predicting transcriptomic profiles from genomic variations and predicting phenotypes from transcriptomic profiles.
• Discusses potential applications in multi-omic studies.

Main Results:

• Provides a framework for using ML in molecular biology data analysis.
• Demonstrates ML's utility in linking genomic variations to transcriptomic profiles and phenotypes.
• Highlights the potential for improved predictions in synthetic biology.

Conclusions:

• ML empowers researchers to analyze large-scale omic data.
• Facilitates uncovering molecular mechanisms controlling gene expression and phenotypes.
• Aims to enhance systematic predictions for synthetic biology applications.