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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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Genomics02:02

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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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Related Experiment Video

Updated: Dec 13, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Generating Proteomic Big Data for Precision Medicine.

Liang Yue1,2, Fangfei Zhang1,2, Rui Sun1,2

  • 1Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang Province, 310024, China.

Proteomics
|July 30, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning (DL) can address complex medical issues using proteomics data. Large-scale clinical studies are needed to generate sufficient proteomic big data for effective DL deployment in healthcare.

Keywords:
clinical cohortdata-independent acquisitiondeep learninghigh-throughput proteomicsprecision medicineproteomic big data

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Area of Science:

  • Proteomics
  • Deep Learning
  • Medical Science

Background:

  • Deep learning (DL) shows promise for tackling complex medical problems.
  • Proteomics research requires large datasets for effective DL application.
  • Advancements in high-throughput proteomics enable generation of extensive quantitative proteome maps.

Purpose of the Study:

  • To explore the potential of deep learning (DL) in addressing complex medical problems using proteomics data.
  • To highlight the necessity of large-scale datasets for successful DL implementation in proteomics.
  • To discuss the implications of high-throughput proteomics advancements for clinical studies.

Main Methods:

  • Review of deep learning applications in medical imaging classification.
  • Analysis of contemporary high-throughput proteomics techniques, including liquid chromatography mass spectrometry and data-independent acquisition.
  • Consideration of requirements for large-scale clinical proteomics study design.

Main Results:

  • Deep learning (DL) is a viable technology for analyzing complex medical problems within proteomics.
  • Thousands of samples are considered the minimum requirement for effective DL in proteomics.
  • Data-independent acquisition facilitates the creation of numerous quantitative proteome maps from limited clinical samples.

Conclusions:

  • Large-scale clinical proteomics studies are essential for generating the proteomic big data needed for DL.
  • Deep learning (DL) deployment holds significant potential for advancing the understanding and treatment of complex medical conditions.
  • Innovations in proteomics technology support the generation of big data for DL-driven medical research.