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One Step Protein Identification from Tandem Mass Spectrometry Data Using Deep Neural Networks

ФИО студента: Polina Kudriavtseva

Руководитель: Attila Kertesz-Farkas

Кампус/факультет: Faculty of Computer Science

Программа: Data Analysis for Biology and Medicine (Master)

Год защиты: 2021

One of the mass spectrometry tasks is the matching of a spectrum to a protein sequence from the database. Currently, the most popular way to solve this problem includes many steps for processing experimental spectra, gener- ating theoretical ones and comparing them. With each step, the total error grows and the result becomes less and less accurate. In this work, we propose to replace the standard pipeline with a deep neural network. The developed architecture will make it possible to directly match a spectrum to a protein sequence, rather than a peptide, eliminating the need to generate theoretical spectra and remove noise from experimental data. We compared a standard pipeline and a neural network using the false discovery rate metric. The devel- oped model provides a greater number of acceptable protein spectrum matches at all threshold values above 1%.

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