bioscience 0.1.2

bioScience: A new Python science library for High-Performance Computing Bioinformatics Analytics

Deployment & Documentation & Stats

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.

Installation

It is recommended to use pip for installation. Please make sure the latest version is installed, as bioScience is updated frequently:

pip install bioscience            # normal install
pip install --upgrade bioscience  # or update if needed
pip install --pre bioscience      # or include pre-release version for new features

Alternatively, you could clone and run setup.py file:

git clone https://github.com/aureliolfdez/bioscience.git
pip install .

Required Dependencies:

• Python>=3.10

• numpy>=1.26.0

• pandas>=2.1.1

• scikit-learn>=1.3.1

• numba>=0.58.0

API demo

import bioscience as bs

if __name__ == "__main__":

   # RNA-Seq dataset load
   dataset = load(path="datasets/rnaseq.txt", index_gene=0, index_lengths=1 ,naFilter=True, head = 0)

   # RNA-Seq preprocessing
   bs.tpm(dataset)

   # Binary preprocessing
   bs.binarize(dataset)

   # Data mining phase
   listModels = bs.bibit(dataset, cMnr=2, cMnc=2, mode=3, deviceCount=1, debug = True)

   # Save results
   bs.saveGenes(path="/path/", models=listModels, data=dataset)

Citing bioScience:

bioScience is published in (under review). If you use bioScience in a scientific publication, we would appreciate citations to the following paper:

Under review

Key Links and Resources:

• View the latest codes on Github

• View the documentation & API

• View all examples