Machine learning and data mining in complex genomic data--a review on the lessons learned in Genetic Analysis...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• The increasing complexity of genomic data necessitates advanced analytical techniques.
• Machine learning (ML) and data mining (DM) offer powerful tools for extracting insights from large-scale genetic datasets.

Framework:

• Exploration of ML and DM approaches for genomic data analysis.
• Motivated by identifying underlying data structures and advancing computational methods for big data.

Implementation:

• Application of ML and DM to identify hidden structures in genomic data.
• Development of computational methods to efficiently process large volumes of genetic information.

Implications:

• Six common messages extracted highlight the current state of ML/DM in complex genomics.
• Progress in integrating diverse data types and evaluating evidence for genetic association.
• Mining for genetic or phenotypic structure significantly aids downstream analyses, with future potential for more complex datasets.