Machine learning technology in the application of genome analysis: A systematic review

Area of Science:

• Bioinformatics
• Genomics
• Data Mining
• Predictive Analytics

Background:

• High-throughput technologies generate vast amounts of biological data.
• Machine learning (ML) is increasingly vital for data analysis in life sciences.
• Genomic data analysis presents unique challenges and opportunities for ML.

Purpose of the Study:

• To review major machine learning algorithms applicable to bioinformatics.
• To highlight key considerations for applying ML to genomic data.
• To provide examples of ML applications in current genomic research.

Main Methods:

• Literature review of machine learning algorithms.
• Analysis of ML applicability in bioinformatics and genomics.
• Compilation of case studies and data analysis challenges.

Main Results:

• Identification of major ML algorithms suitable for biological data.
• Discussion of critical factors for successful ML implementation in genomics.
• Presentation of diverse examples illustrating ML's utility and challenges.

Conclusions:

• Machine learning holds significant potential for advancing bioinformatics.
• Careful consideration of algorithms and data is crucial for effective genomic analysis.
• ML is a key enabler for extracting insights from complex biological datasets.