Protein Secondary Structure Prediction using Multi-input Convolutional Neural Network

Jalal, Shayan Ihsan; Zhong, Jianhui; Kumar, Suman

doi:10.1109/southeastcon42311.2019.9020333

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Notably, GCN-based protein feature extraction significantly impacted the study. [7] proposed a combination of CNN and SVM for protein secondary structure prediction using the CullPDB and CB513 datasets. They focused on the Shift and Stitch CNN architecture, which preserved the protein sequence length during training and output.…”

Section: Related Workmentioning

confidence: 99%

N-Grams Modeling for Protein Secondary Structure Prediction: Exploring Local Features and Optimal CNN Parameters

Rizqiana,

Afiahayati

2023

BIO Web Conf.

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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 bigrams and trigrams demonstrate similar performance trends, with bigrams slightly more effective. The study offers insights into local feature extraction, which is n-grams for protein modeling. These findings contribute to protein structure analysis and bioinformatics advancements, facilitating improved protein function prediction.

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Section: Related Workmentioning

confidence: 99%

N-Grams Modeling for Protein Secondary Structure Prediction: Exploring Local Features and Optimal CNN Parameters

Rizqiana,

Afiahayati

2023

BIO Web Conf.

View full text Add to dashboard Cite

show abstract

“…Shapovalov et al [13] proposed a PSSP model that employed four stacked convolutional layers in its architecture. Jalal et al [106] used multi-input CNN layers and merged the convolution outputs of each input channel.…”

Section: Pssp In Pre-alphafold Publicationmentioning

confidence: 99%