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• W3191462512 abstract "Membrane proteins are an important part of daily life activities in biological information. Predicting membrane proteins can improve drug targeting accuracy and artificial intelligence-assisted drug progression. Traditional methods such as X-ray and MRI are more accurate but consume huge human and material resources, and as science progresses, traditional experimental methods have become more and more difficult to match the needs of experts. In this paper, from the perspective of machine learning, pseudo-PSSM (PsePSSM), averaging block (AvBlock), discrete cosine transform (DCT), discrete wavelet transform (DWT) and histogram of oriented gradients (HOG) are used, and then features are extracted via position scoring matrix (PSSM). An evolutionary feature and fuzzy support vector machine based membrane protein prediction model is proposed. The results show that this method has better prediction and higher accuracy than other methods on two benchmark data sets, TRAIN1 and TRAIN2, reaching 90.6% and 89.7% accuracy, respectively." @default.
• W3191462512 created "2021-08-16" @default.
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• W3191462512 date "2021-01-01" @default.
• W3191462512 modified "2023-09-26" @default.
• W3191462512 title "Membrane Protein Identification via Multiple Kernel Fuzzy SVM" @default.
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• W3191462512 doi "https://doi.org/10.1007/978-3-030-84532-2_57" @default.
• W3191462512 hasPublicationYear "2021" @default.
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