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Illuminating the Noncoding Genome in Cancer Using Artificial Intelligence.

Maria Del Mar Alvarez-Torres1, Xi Fu1,2, Raul Rabadan1,2

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Artificial intelligence (AI) advances cancer genome research by analyzing noncoding mutations. This review compares AI models for identifying functional variants and predicting gene expression impacts in cancer.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • The noncoding cancer genome is vast and complex, requiring advanced analytical strategies.
  • Artificial intelligence (AI) offers powerful tools for understanding genome regulation in cancer.
  • Noncoding mutations play a significant role in cancer development but are often poorly understood.

Purpose of the Study:

  • To review key AI models for analyzing noncoding cancer genomes.
  • To compare AI approaches for identifying functional noncoding variants and predicting their impact on gene expression.
  • To provide practical insights for researchers integrating AI into cancer genomics studies.

Main Methods:

  • Review of AI models developed over the last decade for noncoding variant analysis.
  • Focus on models identifying functional noncoding variants and predicting gene expression impacts.
  • Analysis of model goals, data requirements, features, and outcomes.

Main Results:

  • AI models are increasingly effective at identifying functional noncoding mutations in cancer.
  • These models can predict the impact of noncoding variants on gene expression.
  • A decade of AI development has yielded diverse tools for cancer genomics.

Conclusions:

  • AI is revolutionizing the analysis of the noncoding cancer genome.
  • Understanding noncoding mutations is crucial for advancing cancer research and treatment.
  • Researchers can leverage AI tools to enhance their studies of cancer genomics, irrespective of computational background.