AI Article Synopsis
- SIMBSIG is a new Python package designed for handling extremely large datasets typically encountered in bioinformatics, using GPU acceleration to perform similarity searches, principal component analysis, and clustering efficiently.* -
- The package features a user-friendly interface similar to scikit-learn, making it accessible for users familiar with machine learning in Python.* -
- SIMBSIG is open-source and can be downloaded from PyPI, with its source code and documentation available on GitHub, licensed under BSD-3.*
Article Abstract
Summary: In many modern bioinformatics applications, such as statistical genetics, or single-cell analysis, one frequently encounters datasets which are orders of magnitude too large for conventional in-memory analysis. To tackle this challenge, we introduce SIMBSIG (SIMmilarity Batched Search Integrated GPU), a highly scalable Python package which provides a scikit-learn-like interface for out-of-core, GPU-enabled similarity searches, principal component analysis and clustering. Due to the PyTorch backend, it is highly modular and particularly tailored to many data types with a particular focus on biobank data analysis.
Availability And Implementation: SIMBSIG is freely available from PyPI and its source code and documentation can be found on GitHub (https://github.com/BorgwardtLab/simbsig) under a BSD-3 license.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825260 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btac829 | DOI Listing |
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