Deep Learning for Reintegrating Biology.

Area of Science:

• Integrative biology
• Computational biology
• Bioinformatics

Background:

• Biological research is fragmented across disciplines.
• Current research methods struggle to connect disparate biological data.
• Life is a multidimensional phenomenon across time, space, and scale.

Purpose of the Study:

• To explore the role of advanced machine learning, particularly deep learning (DL), in reintegrating biological disciplines.
• To conceptualize life as a multidimensional phenomenon and biological research as creating connections within this domain.
• To propose DL as a method for establishing long-range connections between biological data points.

Main Methods:

• Conceptualizing life as a multidimensional phenomenon (time, space, scale).
• Viewing biological research as establishing connections within the domain of life.
• Leveraging deep learning (DL) for identifying complex relationships in large biological datasets.

Main Results:

• Deep learning (DL) excels at finding complex patterns in large datasets, surpassing traditional methods.
• DL can establish long-range connections between biological data points, addressing research fragmentation.
• Increasing availability of large quantitative biological datasets supports DL applications.

Conclusions:

• Deep learning (DL) offers a powerful approach to reintegrate fragmented biological disciplines.
• Developing tailored DL methods is crucial for effectively linking biological datasets and gaining insights.
• Future research should focus on specialized DL architectures for integrative biology.