https://github.com/supernovasatsangi23/modifying-biomarker-gene-identification-for-effective-cancer-categorization
A project that focuses on implementing a hybrid approach that modifies the identification of biomarker genes for better categorization of cancer. The methodology is a fusion of MRMR filter method for feature selection, steady state genetic algorithm and a MLP classifier.
https://github.com/supernovasatsangi23/modifying-biomarker-gene-identification-for-effective-cancer-categorization
dataset deep-learning deep-neural-networks feature-selection genetic-algorithm machine-learning machine-learning-algorithms mlp-classifier mrmr neural-network numpy pandas-dataframe python python3 scikit-learn scikit-learn-python tkinter-gui tkinter-python
Last synced: 2 months ago
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A project that focuses on implementing a hybrid approach that modifies the identification of biomarker genes for better categorization of cancer. The methodology is a fusion of MRMR filter method for feature selection, steady state genetic algorithm and a MLP classifier.
- Host: GitHub
- URL: https://github.com/supernovasatsangi23/modifying-biomarker-gene-identification-for-effective-cancer-categorization
- Owner: SupernovaSatsangi23
- Created: 2022-08-19T18:22:16.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-08-19T18:23:43.000Z (over 3 years ago)
- Last Synced: 2025-02-03T07:11:19.552Z (11 months ago)
- Topics: dataset, deep-learning, deep-neural-networks, feature-selection, genetic-algorithm, machine-learning, machine-learning-algorithms, mlp-classifier, mrmr, neural-network, numpy, pandas-dataframe, python, python3, scikit-learn, scikit-learn-python, tkinter-gui, tkinter-python
- Language: Python
- Homepage:
- Size: 6.84 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Modifying-Biomarker-Gene-Identification-For-Effective-Cancer-Categorization
A project that focuses on implementing a hybrid approach that modifies the identification of biomarker genes for better categorization of cancer. The methodology is a fusion of MRMR filter method for feature selection, steady state genetic algorithm and a MLP classifier.