DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

Transcription factor binding site prediction for novel DNA sequence data aiding in mutation identification and drug discovery

This project implements a hybrid machine learning approach for classifying breast cancer from DNA sequences using bidirectional embeddings generated by DNABERT. The study processes over 46 million high-quality DNA sequences to distinguish between cancerous and non-cancerous genomic material.

Promoter detection of DNA sequences using Transformers from scratch and DNABERT