Protein Secondary Structure Prediction
13 papers with code • 8 benchmarks • 1 datasets
Protein secondary structure prediction is a vital task in bioinformatics, aiming to determine the arrangement of amino acids in proteins, including α-helices, β-sheets, and coils. By analyzing amino acid sequences, computational algorithms and machine learning techniques predict these structural elements. This knowledge is crucial for understanding protein function and interactions. While progress has been made, challenges remain, especially with non-local interactions and low sequence homology. Advancements in machine learning hold promise for improving prediction accuracy, furthering our understanding of protein biology.
Benchmarks
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PS4
Latest papers
Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks
Inspired by the recent successes of deep neural networks, in this paper, we propose an end-to-end deep network that predicts protein secondary structures from integrated local and global contextual features.
Protein secondary structure prediction using deep convolutional neural fields
LucaAngioloni/ProteinSecondaryStructure-CNN
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Protein secondary structure (SS) prediction is important for studying protein structure and function.
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
LucaAngioloni/ProteinSecondaryStructure-CNN
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Here we present a new supervised generative stochastic network (GSN) based method to predict local secondary structure with deep hierarchical representations.
