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Related Concept Videos

Proteomics01:33

Proteomics

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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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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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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.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Proteome analysis using machine learning approaches and its applications to diseases.

Abhishek Sengupta1, G Naresh1, Astha Mishra1

  • 1Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India.

Advances in Protein Chemistry and Structural Biology
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Summary

Artificial intelligence and deep learning (DL) analyze complex biological data, including protein sequences. These machine learning tools are crucial for advancing proteomics by improving protein prediction, identification, and quantification, especially when used with mass spectrometry.

Keywords:
AlgorithmsArtificial intelligenceBiomarkersDeep learningMachine learningMass spectrometryProteomics

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Data Science

Background:

  • Biological and medical technologies generate vast datasets (genomic, imaging, protein sequences).
  • Analyzing this data enhances understanding of diseases and human health.
  • Artificial intelligence (AI) and deep learning (DL) are powerful tools for extracting insights from complex biological data.

Purpose of the Study:

  • To explore the applications of deep learning (DL) and machine learning (ML) algorithms in proteomics.
  • To highlight the role of DL and ML in addressing challenges in protein analysis.
  • To discuss the integration of informatics approaches with techniques like mass spectrometry (MS) for effective data interpretation.

Main Methods:

  • Utilizing deep learning (DL) algorithms with artificial neural networks to identify patterns in large datasets.
  • Applying machine learning (ML) approaches for data analysis and interpretation in proteomics.
  • Employing mass spectrometry (MS) in conjunction with informatics tools for protein studies.

Main Results:

  • DL algorithms effectively extract viable data and recognize patterns in complex biological datasets.
  • ML and DL significantly aid in protein prediction, identification, and quantification.
  • Informatics approaches, including ML and DL, are essential for efficient analysis of large-scale MS data in proteomics.

Conclusions:

  • Deep learning and machine learning are indispensable tools in modern proteomics research.
  • The integration of AI with techniques like MS is transforming our ability to understand protein functions and roles in health and disease.
  • Continued development and application of these computational methods will drive further advancements in genomics and personalized medicine.