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Explainable Machine Learning for Identifying the Full Heterogeneity of Peptidoforms and Proteoforms

CORDIS · observation · Publication date unknown

Explainable Machine Learning for Identifying the Full Heterogeneity of Peptidoforms and Proteoforms Mass spectrometry driven proteomics allows deep insights into the working of cells. Still, the vast majority of proteoforms, representing the full heterogeneity of molecular forms of protein products in a sample, currently remain undetected in proteomics experiments. This lack of information strongly restricts our knowledge of disease progression, possible biomarkers, and therapeutic targets across a large number of diseases. Several machine learning approaches have been developed for proteomics data, but not being trained end-to-end, they cannot capture the full wealth of proteomic mass spectra and commonly remain unexplained black boxes. Within explAInProt, my team and I will develop representations of spectra that allow deploying explainable, end-to-end machine learning models on the wealth of pro

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recordType

award

status

SIGNED

region

EU

value

1992500

unit

EUR