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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Proteomics01:33

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Methodology for Good Machine Learning with Multi-Omics Data.

Thibaud Coroller1, Berkman Sahiner2, Anup Amatya3

  • 1Novartis Pharmaceutical Company, East Hanover, New Jersey, USA.

Clinical Pharmacology and Therapeutics
|November 15, 2023
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Summary
This summary is machine-generated.

Novartis and the FDA collaborated on a 4-year project to discover new radio-genomics factors for metastatic breast cancer using advanced analytics. This research offers guidelines for future multi-omics projects integrating artificial intelligence and machine learning.

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Area of Science:

  • Oncology
  • Radiology
  • Genomics
  • Data Science

Background:

  • A 4-year collaboration between Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) began in 2020.
  • The project focuses on novel data modalities and advanced analytics.
  • The primary scientific question concerns identifying prognostic and predictive factors for HR+/HER- metastatic breast cancer.

Purpose of the Study:

  • To provide tangible guidelines for multi-omics projects involving multidisciplinary teams across institutions.
  • To share insights gained from a collaborative project utilizing artificial intelligence (AI) and machine learning (ML).
  • To offer actionable guidance for implementing exploratory data science projects.

Main Methods:

  • Exploration of novel radio-genomics-based prognostic and predictive factors.
  • Application of advanced analytics, including artificial intelligence and machine learning.
  • Structured approach to multi-omics projects through four key steps: plan, design, develop, and disseminate.

Main Results:

  • Valuable insights have been generated to facilitate future scientific projects.
  • Demonstrated the potential of integrating complex data modalities and advanced analytics.
  • Provided practical strategies for effective communication and good data science practices in collaborative research.

Conclusions:

  • The collaboration has yielded significant insights into radio-genomics for metastatic breast cancer.
  • The developed guidelines offer a framework for successful multi-omics research endeavors.
  • Effective communication and robust data science practices are crucial for inter-institutional scientific projects.