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Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
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Integrative computational epigenomics to build data-driven gene regulation hypotheses.

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Summary

This review analyzes data harmonization methods for complex diseases. It highlights matrix factorization, latent variable analysis, and deep learning as key strategies for integrating multi-modal datasets to understand disease mechanisms and advance precision medicine.

Keywords:
bioinformaticscomputational biologydata integrationdeep learningepigeneticsepigenomicsgene regulationgenomicshigh-throughput sequencingmachine learning

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Diseases manifest as complex phenotypes from intricate genetic and epigenetic interactions.
  • Integrating large, heterogeneous, and noisy multi-modal datasets into interpretable phenotypes is challenging.
  • Existing data integration methods have limitations in scope and applicability.

Purpose of the Study:

  • To critically analyze existing data harmonization methods.
  • To identify potent strategies for unifying multi-modal biological data.
  • To describe the characteristics of an ideal universal data harmonization framework.

Main Methods:

  • Review and critical analysis of data harmonization techniques.
  • Focus on methods for general data harmonization, not case-specific integration.
  • Evaluation of matrix factorization, latent variable analysis, and deep learning.

Main Results:

  • Matrix factorization, latent variable analysis, and deep learning are effective strategies for data harmonization.
  • These methods offer powerful approaches for integrating diverse biological datasets.
  • The review outlines the desirable properties of a universal data harmonization framework.

Conclusions:

  • A universal data harmonizer has significant implications for disease diagnostics and understanding.
  • It can identify dysregulated pathways, aiding in disease mechanism elucidation.
  • Advancements in harmonization support precision medicine and epigenome editing applications.