HyperSpec: Ultrafast Mass Spectra Clustering in Hyperdimensional Space.

ABSTRACT: As current shotgun proteomics experiments can produce gigabytes of mass spectrometry data per hour, processing these massive data volumes has become progressively more challenging. Spectral clustering is an effective approach to speed up downstream data processing by merging highly similar spectra to minimize data redundancy. However, because state-of-the-art spectral clustering tools fail to achieve optimal runtimes, this simply moves the processing bottleneck. In this work, we present a fast spectral clustering tool, HyperSpec, based on hyperdimensional computing (HDC). HDC shows promising clustering capability while only requiring lightweight binary operations with high parallelism that can be optimized using low-level hardware architectures, making it possible to run HyperSpec on graph

SUBMITTER: Xu W 

PROVIDER: S-EPMC10243109 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature