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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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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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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.
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MALDI-TOF Mass Spectrometry01:19

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
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Applications of Molecular Taxonomy01:20

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Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
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Applications Of NMR In Biology01:25

Applications Of NMR In Biology

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Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
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Machine learning meets omics: applications and perspectives.

Rufeng Li1, Lixin Li2, Yungang Xu1

  • 1Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China.

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This summary is machine-generated.

Machine learning is revolutionizing omics data analysis, enabling deeper biological insights and advancing precision medicine. This survey explores current and future applications of artificial intelligence in genomics and other omics fields.

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Area of Science:

  • Bioinformatics and Computational Biology
  • Artificial Intelligence in Life Sciences
  • Genomics and Multi-omics

Background:

  • The rapid growth of omics data, driven by biotechnological innovation, presents significant challenges for knowledge extraction in bioinformatics.
  • Traditional methods struggle with the scale and complexity of multi-omics datasets, necessitating advanced analytical approaches.
  • Integrated analysis and computational modeling are emerging as key strategies for handling diverse omics information.

Purpose of the Study:

  • To provide a comprehensive survey of the intersection between machine learning (ML) and omics data analysis.
  • To explore the past, present, and future impact of artificial intelligence (AI) on biological and biomedical research.
  • To synthesize current insights, challenges, and future perspectives in ML-driven omics studies.

Main Methods:

  • Review and discussion of AI applications across various omics disciplines.
  • Examination of advancements in machine learning for genomics, transcriptomics, proteomics, metabolomics, and radiomics.
  • Analysis of machine learning applications at the single-cell resolution level.

Main Results:

  • Machine learning significantly enhances biological and biomedical insights derived from omics data.
  • AI applications are accelerating the development of novel therapeutic strategies, particularly in precision medicine.
  • Recent advancements show harmonious integration of multi-omics data through computational modeling and ML.

Conclusions:

  • Machine learning is a transformative force in omics research, unlocking complex biological questions.
  • The integration of AI with diverse omics data holds immense potential for future biomedical discoveries and personalized treatments.
  • Addressing current challenges and exploring new perspectives is crucial for maximizing the impact of ML in omics.