Pytorch implementation of DeePromoter Active sequence detection for promoter(DNA subsequence regulates transcription initiation of the gene by controlling the binding of RNA polymerase)

DNA classifier using Natural Language Processing. Used K-mer method to convert sequence strings into fixed size words

data mining class project for DNA sequences's clustering. Released on 2019.

This project was developed during a 3-month internship program at Feynn Labs. The repository contains a DNA classification app for detecting E. Coli, leveraging machine learning techniques.

This project provides the classification of DNA sequences for Breast cancer prediction which into promoter regions associated. Using machine learning and deep learning techniques, I analyze and try to predict sequence data for negative and positive answers in cancer prediction.

The DNA Classification for E.Coli project, built on Django, uses an MLPClassifier to predict E.Coli presence in DNA sequences. The web app allows users to input sequences and view classification results. The project is structured for reproducibility and deployment.