Machine learning in postgenomic biology and personalized medicine

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• The advent of rapid gene sequencing and molecular structure determination generates vast biological datasets.
• Classical statistical methods are insufficient for analyzing these large-scale, complex biological data.
• Machine learning (ML) offers novel, data-intensive approaches to address biological challenges previously reliant on computationally expensive mechanistic models.

Purpose of the Study:

• To provide a comprehensive overview of machine learning applications in post-genomic biology.
• To identify emerging research areas in ML-driven biological discovery.
• To highlight the significance of explainable artificial intelligence (XAI) in healthcare.

Main Methods:

• Review of current literature on machine learning applications in biological sciences.
• Analysis of trends in data-intensive ML algorithms for biological problem-solving.
• Exploration of the role of ML in advancing fields like genomics, proteomics, and agricultural technology.

Main Results:

• Machine learning is revolutionizing data analysis in genomics, proteomics, and agricultural technology.
• ML enables the development of novel solutions for biological problems, moving beyond traditional computational methods.
• Explainable AI (XAI) is crucial for advancing human health applications.

Conclusions:

• Machine learning is a transformative force in post-genomic biology, impacting medicine, public health, and agricultural technology.
• Future contributions of ML are expected to provide gene-based guidance for managing complex environmental challenges, particularly in the context of global warming.
• Continued research in ML, especially XAI, will drive significant advancements across various life science disciplines.