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Related Experiment Video

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Artificial intelligence and omics in malignant gliomas.

Richa Tambi1, Binte Zehra2, Aswathy Vijayakumar2

  • 1Center for Applied and Translational Genomics (CATG), Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates.

Physiological Genomics
|October 22, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) applied to glioblastoma multiforme (GBM) omics data shows promise for improving subtype classification, prognosis, and survival. This review explores AI techniques and resources for advancing GBM research and precision medicine.

Keywords:
artificial intelligenceglioblastomamachine learningomicsprecision medicine

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Area of Science:

  • Neuro-oncology
  • Computational Biology
  • Bioinformatics

Background:

  • Glioblastoma multiforme (GBM) is an aggressive brain cancer with poor prognosis despite current treatments.
  • Tumor heterogeneity and the blood-brain barrier present significant therapeutic challenges.
  • Multifaceted approaches, including omics research, are crucial for understanding GBM biology and developing effective therapies.

Purpose of the Study:

  • To review artificial intelligence (AI) techniques and database resources for studying glioblastoma multiforme (GBM) pathogenesis using multiomics data.
  • To explore the application of AI in GBM subtype classification, prognosis, and survival prediction.
  • To highlight the potential impact of AI on advancing GBM research and precision medicine.

Main Methods:

  • Review of AI-based techniques applied to GBM multiomics data over the past decade.
  • Summarization of GBM-related omics resources suitable for AI model development.
  • Exploration of AI tools utilizing individual or integrated multiomics data.

Main Results:

  • AI is emerging as a powerful tool for integrating large omics databases in GBM research.
  • Various AI tools have been developed using genomics, transcriptomics, proteomics, and epigenomics data.
  • These AI applications aim to improve GBM subtype classification, prognosis, and survival prediction.

Conclusions:

  • AI holds significant potential to advance glioblastoma research and clinical treatment by leveraging multiomics data.
  • Further exploration of AI utilization in GBM-omics can uncover critical biological insights and therapeutic targets.
  • The integration of AI with multiomics data is key to implementing precision medicine for GBM patients.