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Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.

Stefanos Tsimenidis1, Eleni Vrochidou1, George A Papakostas1

  • 1MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece.

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Summary
This summary is machine-generated.

This study bridges the gap between computer science and biology, offering essential information for applying deep learning (DL) to biological data analysis. It guides experts in leveraging DL for advancements in biomedicine and biotechnology.

Keywords:
artificial intelligencebiological datacomplex systemsdeep learningdrug discoveryomicsreviewsystem biology

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Biological data analysis is crucial for medical discoveries, with rapidly growing datasets from decreasing sequencing costs.
  • Deep learning (DL) offers powerful tools for analyzing these massive biological datasets.
  • A significant challenge exists in integrating DL expertise with rapidly evolving life sciences.

Purpose of the Study:

  • To bridge the interdisciplinary gap between computer science and biology.
  • To provide computer scientists with foundational knowledge of biological data and DL applications in life sciences.
  • To assist biology researchers in understanding and utilizing DL for omics data analysis.

Main Methods:

  • Overview of common biological data types and their representations for DL model training.
  • Explanation of prevalent DL models used in biological research.
  • Discussion of various biological tasks addressed by DL.

Main Results:

  • Provides essential information for DL experts to engage in biomedical, biotechnology, and drug discovery projects.
  • Empowers biology researchers to utilize DL for enhanced insights from omics data.
  • Facilitates interdisciplinary collaboration through shared knowledge.

Conclusions:

  • This work serves as a crucial guide for applying deep learning to biological data.
  • It enables computer scientists to contribute effectively to life sciences research.
  • It empowers biologists to harness the power of DL for novel discoveries.