Disorder protein genomic binding analysis toolkit designed for DisP-seq
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Updated
May 28, 2024 - Python
Disorder protein genomic binding analysis toolkit designed for DisP-seq
Developing classification models for DNA-Binding proteins through machine learning and large language models
DBP-PSSM
Predicting transcription factor-DNA binding from sequence data
Multiview Random Vector Functional Link Network for Predicting DNA-binding Proteins
A deep learning-based method for the prediction of DNA interacting residues in a protein
This project explores the use of machine learning techniques to classify DNA-binding proteins (DBPs) from non-DNA-binding proteins. Accurate DBP identification is critical for understanding biological processes such as gene regulation, replication, and transcription, with applications in drug design and gene therapy.
Identification of DNA-binding proteins using support vector machines and evolutionary profiles
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