1-Dimensional Polynomial Neural Networks for audio signal related problems

Abdallah, Habib Ben; Henry, Christopher J.; Ramanna, Sheela

doi:10.1016/j.knosys.2022.108174

Cited by 3 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNN has advantages in local perception, which can ensure that the convolution kernel can fully consider local features [32]. 1D convolution is majorly used where the input is sequential such as text or audio [33], which is very suitable for protein sequences. The formula for the operation of the 1D convolution is defined as follows: Where O(i, j) represents the convolution operation result of the i-th row and j-th colomn located in the S. f(x) is the LeakyRelu activation function with setting negative slope=0.3.…”

Section: Mcn Layermentioning

confidence: 99%

DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction

et al. 2022

View full text Add to dashboard Cite

Background Protein-protein interaction (PPI) is very important for many biochemical processes. Therefore, accurate prediction of PPI can help us better understand the role of proteins in biochemical processes. Although there are many methods to predict PPI in biology, they are time-consuming and lack accuracy, so it is necessary to build an efficiently and accurately computational model in the field of PPI prediction. Results We present a novel sequence-based computational approach called DCSE (Double-Channel-Siamese-Ensemble) to predict potential PPI. In the encoding layer, we treat each amino acid as a word, and map it into an N-dimensional vector. In the feature extraction layer, we extract features from local and global perspectives by Multilayer Convolutional Neural Network (MCN) and Multilayer Bidirectional Gated Recurrent Unit with Convolutional Neural Networks (MBC). Finally, the output of the feature extraction layer is then fed into the prediction layer to output whether the input protein pair will interact each other. The MCN and MBC are siamese and ensemble based network, which can effectively improve the performance of the model. In order to demonstrate our model’s performance, we compare it with four machine learning based and three deep learning based models. The results show that our method outperforms other models in all evaluation criteria. The Accuracy, Precision, $$F_{1}$$ F 1 , Recall and MCC of our model are 0.9303, 0.9091, 0.9268, 0.9452, 0.8609. For the other seven models, the highest Accuracy, Precision, $$F_{1}$$ F 1 , Recall and MCC are 0.9288, 0.9243, 0.9246, 0.9250, 0.8572. We also test our model in the imbalanced dataset and transfer our model to another species. The results show our model is excellent. Conclusion Our model achieves the best performance by comparing it with seven other models. NLP-based coding method has a good effect on PPI prediction task. MCN and MBC extract protein sequence features from local and global perspectives and these two feature extraction layers are based on siamese and ensemble network structures. Siamese-based network structure can keep the features consistent and ensemble based network structure can effectively improve the accuracy of the model.

show abstract

Section: Mcn Layermentioning

confidence: 99%