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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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Prediction of Genetic Interactions Using Machine Learning and Network Properties.

Neel S Madhukar1, Olivier Elemento1, Gaurav Pandey2

  • 1Department of Physiology and Biophysics, Meyer Cancer Center, Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medical College , New York, NY , USA ; Tri-Institutional Training Program in Computational Biology and Medicine , New York, NY , USA.

Frontiers in Bioengineering and Biotechnology
|November 19, 2015
PubMed
Summary

Genetic interactions (GIs) reveal gene relationships, crucial for understanding biology and disease. Computational methods are vital for predicting these interactions, especially synthetic lethality, in complex organisms like humans.

Keywords:
cancerdrug discoverygenetic interactionsmachine learningnetwork analysisprediction

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Area of Science:

  • Genetics
  • Systems Biology
  • Computational Biology

Background:

  • Genetic interactions (GIs) describe how gene functions influence each other.
  • Synthetic sickness and lethality are key GIs where combined gene loss severely impacts cell fitness or viability.
  • Identifying GIs aids in understanding biological pathways, protein complexes, and disease mechanisms, with synthetic lethal interactions offering therapeutic targets in cancer.

Purpose of the Study:

  • To review current computational approaches for predicting genetic interactions.
  • To discuss strategies and evaluation methods for learning GIs in various biological contexts.
  • To highlight the need for reliable computational tools for GI prediction in mammalian cells.

Main Methods:

  • Review of state-of-the-art computational methods for GI prediction.
  • Analysis of strategies for learning GIs under general and specific conditions (e.g., diseases).
  • Examination of rigorous evaluation methodologies for predictive models.

Main Results:

  • Systematic high-content screening for GIs is feasible in single-cell organisms but challenging in mammalian cells.
  • Computational approaches are essential for reliable GI prediction in complex organisms.
  • The review covers diverse methods applicable to both healthy and disease states.

Conclusions:

  • Predicting genetic interactions, particularly synthetic lethality, is critical for biological and clinical insights.
  • Computational methods are indispensable for overcoming experimental limitations in discovering GIs in mammalian systems.
  • Accurate GI prediction can uncover novel therapeutic strategies, especially for diseases like cancer.