What is it about?

Using Multiple Feature Fusion on Hypergraph Neural Networks to Realize Membrane Protein Classification and Prediction

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Why is it important?

Membrane proteins play an extremely important role in living organisms as one of the main components of biological membranes. The problem of membrane protein classification and prediction is an important branch of membrane proteomics research, because the function of proteins can be quickly determined if membrane protein types can be discriminated.


Most current methods to classify membrane proteins was labor-intensive and wasteful of resources. In this study, five methods, Average Block (AvBlock), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Histogram of Orientation Gradient (HOG) and Pseudo-PSSM (PsePSSM), are used to extract features in order to predict membrane proteins on a large scale. Then, we fuse the five obtained feature matrices and construct the corresponding hypergraph association matrix. Finally, the feature matrices and hypergraph association matrices are integrated to identify the types of membrane proteins using a hypergraph neural network model (HGNN).

Meiling Qian
Suzhou University of Science and Technology

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This page is a summary of: Identification of Membrane Protein Types Based Using Hypergraph Neural Network, Current Bioinformatics, May 2023, Bentham Science Publishers, DOI: 10.2174/1574893618666230224143726.
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