Skip to main content

GWAS software that combines traditional statistical methods with the power of Artificial Intelligence

Project description

About GWAStic

GWAStic is a software for Genome-Wide Association Study (GWAS) that combines traditional statistical methods with the power of Artificial Intelligence (AI) for comprehensive genetic analysis.

Key Features:

  • Comprehensive Genetic Analysis: GWAStic offers a wide range of methods to analyze your genomic data, allowing you to explore the associations between genetic variants and traits of interest comprehensively.

  • AI-Enhanced Data Analysis: Harness the capabilities of machine learning and AI to uncover subtle patterns, interactions, and associations that may be missed by conventional statistical methods.

  • User-Friendly Interface: GenoAI's intuitive interface makes it accessible to both novice and experienced researchers. Seamlessly navigate through your data, perform analyses, and visualize results with ease.

  • Customizable Workflows: Tailor your analysis to your specific research goals with customizable workflows. Define your parameters, select the appropriate statistical models, and integrate AI components as needed for a personalized analysis experience.

  • Collaborative Research: Collaborate seamlessly with colleagues and share your findings securely within the platform.

  • Frequent Updates: Stay at the forefront of genetic research with regular software updates. GWAStic incorporates the latest advancements in GWAS and AI methodologies to keep your analyses up-to-date.

Whether you are investigating the genetic basis of complex diseases, unraveling the mysteries of ancestry, or conducting groundbreaking research in personalized medicine, GenoAI empowers you with the tools you need to extract meaningful insights from your genetic data. With its innovative AI integration and user-friendly interface, GenoAI is the ultimate solution for researchers and scientists seeking to push the boundaries of genetic research. Explore the potential of your genetic data like never before with GenoAI.

Citation

Documentation

Installation

GWAStic software was build and successfully tested on Windows operating system (Windows 7 and 10).

->Install Anaconda (https://www.anaconda.com/distribution/)

conda create --name gwastic_env python=3.9

conda activate gwastic_env

conda install pip

pip install gwastic_desktop

Usage:

Contributions:

We are strongly looking for contributions.

References:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gwastic_desktop-0.1.0.tar.gz (53.6 MB view details)

Uploaded Source

Built Distribution

gwastic_desktop-0.1.0-py3-none-any.whl (55.1 MB view details)

Uploaded Python 3

File details

Details for the file gwastic_desktop-0.1.0.tar.gz.

File metadata

  • Download URL: gwastic_desktop-0.1.0.tar.gz
  • Upload date:
  • Size: 53.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.6.1 CPython/3.9.18 Windows/10

File hashes

Hashes for gwastic_desktop-0.1.0.tar.gz
Algorithm Hash digest
SHA256 66595bc8112d45968faeabd08bc67f8fc2fefe36da80f058c864fc353fc92833
MD5 32da70ebcb32f1cc2f858d81bca102e8
BLAKE2b-256 633088870ce68ed1fb575c81a79e353fe62f05c01b536be0b6a28cd0036cc0e4

See more details on using hashes here.

File details

Details for the file gwastic_desktop-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: gwastic_desktop-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 55.1 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.6.1 CPython/3.9.18 Windows/10

File hashes

Hashes for gwastic_desktop-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 0593005713c5cc09838710e3db756eb1d91671333f0f84a9d6e6dc60f78ff856
MD5 ae502c4878d9db86cf1002034a70cf74
BLAKE2b-256 4a570703ad8403968d8ae713ef1cdfb01106a2dcf43f7b7c0142d3b9b367e2e3

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page