Machine Learning Applications in Secondary Structure Prediction

Bioinformatics › Protein Folding and Structure Prediction

Machine Learning Applications in Secondary Structure Prediction

Interns will develop and train machine learning models (neural networks, random forests) to predict alpha-helices, beta-sheets, and coil regions from amino acid sequences. This includes feature engineering from sequence data, model validation, and comparing performance with established tools like PSIPRED or JPRED.

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