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Evaluation of Machine Bias in State-of-the-Art Methods for Tandem Mass Spectrometry Data Annotation

ФИО студента: Ho lum Cheung

Руководитель: Attila Kertesz-Farkas

Кампус/факультет: Faculty of Computer Science

Программа: Data Science (Master)

Год защиты: 2021

Mass spectrometry(MS), a method used to identify the components of a substance, is the primary method used to identify peptide chains. In proteomics, the study of proteins, it is common practice to process and compare the experimental results of MS with a database in order to identify peptide chains using the target-decoy search method. Different peptide search methods are then evaluated by how accurately they can identify target peptides over decoy ones. Deep learning methods such as neural networks have been implemented to help with this process. Although these tools are more accurate than previous methods, they are prone to general deep learning issues such as machine bias. In this thesis, we analyze machine bias in two new state-of-the-art deep learning models: MS2Pip and Prosit. Using generated or publicly available data, we ran tests on these tools and analyzed the results for any machine bias.

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