Deep Learning Models for Protein Sequence Alignment

Bioinformatics › Machine Learning Sequence Alignment Research

Deep Learning Models for Protein Sequence Alignment

Interns will develop and optimize neural network architectures (transformers, LSTMs, or attention mechanisms) for improving protein sequence alignment accuracy. They will work with datasets like SCOP and Pfam to train models that can predict optimal alignment parameters and compare performance against classical algorithms like BLAST and Smith-Waterman.

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