emirhanai/AID362-Bioassay-Classification-and-Regression-Neuronal-Network-and-Extra-Tree-with-Machine-Learnin

I developed Machine Learning Software with multiple models that predict and classify AID362 biology lab data. Accuracy values are 99% and above, and F1, Recall and Precision scores are average (average of 3) 78.33%. The purpose of this study is to prove that we can establish an artificial intelligence (machine learning) system in health. With my regression model, you can predict whether it is Inactive or Inactive (Neural Network or Extra Trees). In classification (Neural Network or Extra Trees), you can easily classify the provided data whether it is Inactive or Active.

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Experimental

This software helps researchers and scientists in biology labs analyze AID362 bioassay data. By inputting your bioassay results, it can predict whether a sample is 'Inactive' or 'Active', or classify it as such, using established machine learning models. It's designed for anyone working with biological assay data who needs to quickly assess experimental outcomes.

No commits in the last 6 months.

Use this if you are a biologist or biochemist regularly working with AID362 bioassay data and need to automate the classification or prediction of sample activity.

Not ideal if you are working with bioassay data other than AID362, or if you need to develop custom machine learning models rather than use pre-built ones.

bioassay-analysis drug-discovery biological-research lab-automation health-research
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 0 / 25

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10

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Language

Python

License

MIT

Last pushed

Sep 13, 2021

Commits (30d)

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