semvec/pypi-packages huggingface dataset:
oscb 0.1.1
OSCB aims to provide automated end-to-end single-cell analyses ML pipelines to simplify and standardize the process of single-cell data formatting, quality control, loading, model development, and model evaluation. 
License: MIT
Requires Python: <=3.12,>=3.6
Keywords: single-cell, benchmarks

# Overview

--------------------------------------------------------------------------------


Machine learning (ML) is transforming single-cell sequencing data analysis; however, the barriers of technology complexity and biology knowledge remain challenging for the involvement of the ML community in single-cell data analysis. We present an ML development environment for single-cell sequencing data analyses with a diverse set of AI-Ready benchmark datasets. A cloud-based platform is built to dynamically scale workflows for collecting, processing, and managing various single-cell sequencing data to make them ML-ready. In addition, benchmarks for each problem formulation and a code-level and web-interface IDE for single-cell analysis method development are provided. 


![Workflow](https://oscb.missouri.edu/assets/30e6000a-5e6f-440f-bec5-5c7ceb256c55)

OSCB aims to provide automated end-to-end single-cell analyses ML pipelines to simplify and standardize the process of single-cell data formatting, quality control, loading, model development, and model evaluation. 


**Workflows** are developed for collecting, processing, and managing diverse single-cell sequencing data to make them ML-ready and build benchmarks.

**IDE** is provided for supporting partial method development.

**Assessment utilities** are provided for evaluating results and report generation.

This **end-to-end pipeline** transforms the traditional “static” machine Learning into **continuous learning** on extensive new data.


By **in-depth fusing models with data**, this platform could ultimately help many single-cell sequencing researchers substantially.

 
![Tools](https://oscb.missouri.edu/assets/c18ffb2a-814f-452c-921b-e399b99c41b4)


OSCB is an on-going effort, and we are planning to increase our coverage in the future.