N-Grams Modeling for Protein Secondary Structure Prediction: Exploring Local Features and Optimal CNN Parameters
Annisa Rizqiana,
Afiahayati
Abstract:This study explores the potential of n-gram modeling in protein secondary structure prediction. Experiments are conducted on three datasets using bigrams, trigrams, and a combination of the best n-grams with PSSM profiles. Optimal parameters for Convolutional Neural Networks (CNNs) are investigated. Results indicate that bigrams outperform trigrams in Q8 accuracy. Adding another feature, that is, PSSM, can improve model performance. Deeper convolution layers and longer convolution sizes enhance accuracy. Both … Show more
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