bioScience documentation
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BioScience is an advanced Python library designed to satisfy the growing data analysis needs in the field of bioinformatics by leveraging High-Performance Computing (HPC). This library encompasses a vast multitude of functionalities, from loading specialised gene expression datasets (microarrays, RNA-Seq, etc.) to pre-processing techniques and data mining algorithms suitable for this type of datasets. BioScience is distinguished by its capacity to manage large amounts of biological data, providing users with efficient and scalable tools for the analysis of genomic and transcriptomic data through the use of parallel architectures for clusters composed of CPUs and GPUs.
BioScience is featured for:
Unified APIs, detailed documentation, and interactive examples available to the community.
Complete coverage for generate biological results from gene co-expression datasets.
Optimized models to generate results in the shortest possible time.
Optimization of a High-Performance Computing (HPC) and Big Data ecosystem.
Getting Started
- What is bioScience?
- Why the importance of High-Performance Computing (HPC) in bioScience?
- How does the bioScience library manage memory?
- What kind of pre-processing tasks can be performed with BioScience?
- Does bioScience include data mining algorithms and what can they be used for?
- Are there plans to add validation and results visualisation modules in future versions of bioScience?
- How does BioScience compare with other existing Bioinformatics libraries?
- Installation
- bioScience quickstart
User guide
Real use-cases
Contributor's Guide
Citing bioScience:
bioScience is published in SoftwareX. If you use bioScience in a scientific publication, we would appreciate citations to the following paper:
López-Fernández, A., Gómez-Vela, F. A., Gonzalez-Dominguez, J., & Bidare-Divakarachari, P. (2024). bioScience: A new python science library for high-performance computing bioinformatics analytics. SoftwareX, 26, 101666.
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