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
These leaderboards are used to track progress in Protein Secondary Structure Prediction
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PS4
Latest papers with no code
A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration
Proteins are essential for life, and their structure determines their function.
Approximate Conditional Coverage & Calibration via Neural Model Approximations
A typical desideratum for quantifying the uncertainty from a classification model as a prediction set is class-conditional singleton set calibration.
Adaptive Residue-wise Profile Fusion for Low Homologous Protein SecondaryStructure Prediction Using External Knowledge
Protein secondary structure prediction (PSSP) is essential for protein function analysis.
PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State Protein Secondary Structure
In this study, we have presented a new deep convolutional neural network (DCNN), namely PS8-Net, to enhance the accuracy of eight-class PSS prediction.
Seq-SetNet: Exploring Sequence Sets for Inferring Structures
Therefore, the traditional deep neural networks designed for image processing cannot be directly applied on sequence sets.
Neural Edit Operations for Biological Sequences
The evolution of biological sequences, such as proteins or DNAs, is driven by the three basic edit operations: substitution, insertion, and deletion.
Predicting protein secondary structure with Neural Machine Translation
We present analysis of a novel tool for protein secondary structure prediction using the recently-investigated Neural Machine Translation framework.
Reaching Optimized Parameter Set, Protein Secondary Structure Prediction Using Neural Network
encoding scheme, window size, number of neurons in the hidden layer and type of learning algorithm.
MUFold-SS: Protein Secondary Structure Prediction Using Deep Inception-Inside-Inception Networks
Results: Here, a very deep neural network, the deep inception-inside-inception networks (Deep3I), is proposed for protein secondary structure prediction and a software tool was implemented using this network.
Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction
This sequential model achieves 70. 3% Q8 accuracy on CB513 with a single model; an ensemble of these models produces 71. 4% Q8 accuracy on the same test set, improving upon the previous overall state of the art for the eight-class secondary structure problem.
