Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data.

ABSTRACT:

Background

Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: first, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Furthermore, existing approaches for signal detection usually rely on strong assumptions concerning the signals properties.

Results

In this study, it is shown that locality-sensitive hashing enables signal classification in mass spectrometry raw data at scale. Through appropriate choice of algor

SUBMITTER: Bob K 

PROVIDER: S-EPMC9301846 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature