Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308787105> ?p ?o ?g. }
- W4308787105 abstract "Multiomic analysis comprises genomics, proteomics, and metabolomics leads to meaningful insights but necessitates sifting through voluminous amounts of complex data. Proteomics in particular focuses on the end product of gene expression – i.e., proteins. The mass spectrometric approach has proven to be a workhorse for the qualitative and quantitative study of protein interactions as well as post-translational modifications (PTMs). A key component of mass spectrometry (MS) is spectral data analysis, which is complex and has many challenges as it involves identifying patterns across a multitude of spectra in combination with the meta-data related to the origin of the spectrum. Artificial Intelligence (AI) along with Machine Learning (ML), and Deep Learning (DL) algorithms have gained more attention lately for analyzing the complex spectral data to identify patterns and to create networks of value for biomarker discovery. In this chapter, we discuss the nature of MS proteomic data, the relevant AI methods, and demonstrate their applicability. We also show that AI can successfully identify biomarkers and aid in the diagnosis, prognosis, and treatment of specific diseases." @default.
- W4308787105 created "2022-11-15" @default.
- W4308787105 creator A5036941429 @default.
- W4308787105 creator A5040523956 @default.
- W4308787105 creator A5064651221 @default.
- W4308787105 creator A5079570233 @default.
- W4308787105 date "2022-11-03" @default.
- W4308787105 modified "2023-09-26" @default.
- W4308787105 title "Pattern Recognition for Mass-Spectrometry-Based Proteomics" @default.
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