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Related Concept Videos

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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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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine.

Nikolaos Theodorakis1,2, Georgios Feretzakis3, Lazaros Tzelves4

  • 1Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece.

Journal of Personalized Medicine
|September 28, 2024
PubMed
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Machine learning (ML) combined with multi-omics data analysis reveals molecular aging hallmarks. This integration aids in developing personalized medicine for healthy aging and extending lifespan.

Keywords:
aging researchgerosciencehallmarks of agingmachine learningmulti-omics technologiespersonalized medicine

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Area of Science:

  • Molecular Biology
  • Computational Biology
  • Gerontology

Background:

  • Aging is a natural biological process involving physiological decline and increased disease risk.
  • Understanding molecular aging mechanisms is key to developing interventions.
  • Personalized medicine approaches are emerging to combat aging effects.

Purpose of the Study:

  • To review the integration of machine learning (ML) with multi-omics technologies for studying aging.
  • To explore how ML aids in understanding the molecular hallmarks of aging.
  • To highlight the potential for personalized anti-aging strategies.

Main Methods:

  • Review of current literature integrating ML and multi-omics data.
  • Analysis of multi-omics datasets (genomics, transcriptomics, epigenomics, proteomics, metabolomics).
  • Application of ML algorithms to identify molecular interactions and pathways in aging.

Main Results:

  • ML effectively analyzes complex multi-omics data to uncover aging-related molecular pathways.
  • Identified key molecular hallmarks of aging, including genomic instability, epigenetic alterations, and cellular senescence.
  • ML facilitates the discovery of novel biomarkers and therapeutic targets for aging.

Conclusions:

  • The synergy of ML and multi-omics provides deep insights into the molecular basis of aging.
  • This approach is crucial for developing personalized medicine interventions to promote healthy aging.
  • Future research directions focus on leveraging these technologies to extend healthspan and lifespan.