Models of the Gene Must Inform Data-Mining Strategies in Genomics.

Area of Science:

• Genetics
• Bioinformatics
• Data Science

Background:

• The gene concept originated with Mendelian heredity but has diversified across classical, population, molecular, genomics, and systems genetics.
• Diverse gene models influence data integration and mining approaches in various genetic sub-disciplines.

Purpose of the Study:

• To investigate how the evolving concept of the gene affects data-integration and data-mining strategies in bioinformatics, genomics, and data science.
• To explore the theoretical underpinnings of gene concepts, including empiricism, experimentalism, and reductionist/anti-reductionist narratives.

Main Methods:

• Conceptual analysis of the gene across different genetics sub-disciplines.
• Review of theoretical frameworks (empiricism, experimentalism, reductionism).
• Analysis and re-interpretation of published data-mining project examples.

Main Results:

• The diversity of gene concepts presents challenges for data integration and mining.
• Theoretical perspectives shape the understanding and application of gene data.
• Specific data-mining strategies are discussed and re-interpreted through theoretical lenses.

Conclusions:

• The choice of an optimal level of abstraction for the gene is critical for effective genome analysis.
• Understanding the historical and theoretical diversity of the gene concept is vital for bioinformatics and data science applications.