Identification of Intrinsically Disordered Protein Regions Based on Deep Neural Network-VGG16

Xu, Pengchang; Zhao, Jiaxiang; Zhang, Jie

doi:10.3390/a14040107

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…In this section, we will demonstrate the performance of our deep neural network on the different test sets: DIS166 [ 34 ], R80 [ 25 ], and MXD494 [ 33 ]. As a comparison, we also present the simulation results of the best known predictors for these datasets, such as RFPR-IDP (available at (accessed on 26 March 2021)), SPOT-Disorder2 (available at (accessed on 26 March 2021)), DISvgg [ 16 ], and IDP-Seq2Seq [ 18 ]. For convenience, we refer to our method as MLP-VGG19-MLP.…”

Section: Experimental Resultsmentioning

confidence: 99%

“…These methods can be divided into three categories: (1) Physicochemical-based methods, such as FoldIndex [ 11 ], GlobPlot [ 12 ], IUPred [ 13 ], FoldUnfold [ 14 ], and IsUnstruct [ 15 ], which rely on the amino acid physiochemical properties for identifying disorder. (2) Machine learning-based methods—for instance, DISvgg [ 16 ], RFPR-IDP [ 17 ], IDP-Seq2Seq [ 18 ], SPOT-Disorder [ 19 ], SPOT-Disorder2 [ 20 ], DISOPRED3 [ 21 ], SPINE-D [ 22 ], ESpritz [ 23 ], BVDEA [ 10 ], POODLE-S [ 24 ], RONN [ 25 ], and PONDRs [ 26 ]—which treat the identification of IDRs as labeling each amino acid of a protein sequence or as a classification problem. (3) Meta methods, including MFDp [ 27 ], MetaPrDOS [ 28 ], and Meta-Disorder predictor [ 29 ], which fuse multiple predictors to yield the final prediction for IDPRs.…”

Section: Introductionmentioning

confidence: 99%

“…The MLPs are employed for transforming the features into the formats suitable for serving as the inputs of the variant VGG19 and classification network, respectively. Compared with our previous DISpre algorithm [ 31 ] and DISvgg algorithm [ 16 ], we introduce VGG19 as a part of the network instead of as a single MLP network, and additionally use one VGG19 instead of ten VGG16. Moreover, to further improve the performance of prediction, we introduce new features for prediction.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Intrinsically Disordered Protein Regions through a Deep Neural Network with Three Novel Sequence Features

Zhao

Wang

2022

Life

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The fast, reliable, and accurate identification of IDPRs is essential, as in recent years it has come to be recognized more and more that IDPRs have a wide impact on many important physiological processes, such as molecular recognition and molecular assembly, the regulation of transcription and translation, protein phosphorylation, cellular signal transduction, etc. For the sake of cost-effectiveness, it is imperative to develop computational approaches for identifying IDPRs. In this study, a deep neural structure where a variant VGG19 is situated between two MLP networks is developed for identifying IDPRs. Furthermore, for the first time, three novel sequence features—i.e., persistent entropy and the probabilities associated with two and three consecutive amino acids of the protein sequence—are introduced for identifying IDPRs. The simulation results show that our neural structure either performs considerably better than other known methods or, when relying on a much smaller training set, attains a similar performance. Our deep neural structure, which exploits the VGG19 structure, is effective for identifying IDPRs. Furthermore, three novel sequence features—i.e., the persistent entropy and the probabilities associated with two and three consecutive amino acids of the protein sequence—could be used as valuable sequence features in the further development of identifying IDPRs.

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Section: Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying Intrinsically Disordered Protein Regions through a Deep Neural Network with Three Novel Sequence Features

Zhao

Wang

2022

Life

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“…Notably, the structure of VGG16 is very simple. However, the number of VGG network channels is too large, and its structure determines that it requires more parameters and brings more memory usage ( 48 ). In addition, the VGG16 network structure is too densely connected, resulting in a long training time, these factors may lead to the relatively poor effect of VGG16 in this study.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Tumor Mutation Load in Colorectal Cancer Histopathological Images Based on Deep Learning

Liu

Huang²,

Yang

et al. 2022

Front. Oncol.

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Colorectal cancer (CRC) is one of the most prevalent malignancies, and immunotherapy can be applied to CRC patients of all ages, while its efficacy is uncertain. Tumor mutational burden (TMB) is important for predicting the effect of immunotherapy. Currently, whole-exome sequencing (WES) is a standard method to measure TMB, but it is costly and inefficient. Therefore, it is urgent to explore a method to assess TMB without WES to improve immunotherapy outcomes. In this study, we propose a deep learning method, DeepHE, based on the Residual Network (ResNet) model. On images of tissue, DeepHE can efficiently identify and analyze characteristics of tumor cells in CRC to predict the TMB. In our study, we used ×40 magnification images and grouped them by patients followed by thresholding at the 10th and 20th quantiles, which significantly improves the performance. Also, our model is superior compared with multiple models. In summary, deep learning methods can explore the association between histopathological images and genetic mutations, which will contribute to the precise treatment of CRC patients.

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“…Network Structure. In this study, the SSD model with VGG16 [17,19,20] as the main network was selected.…”

Section: Lead Networkmentioning

confidence: 99%

UAV Image Small Object Detection Based on Composite Backbone Network

Liu

Qiang

et al. 2022

Mobile Information Systems

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Small objects in traffic scenes are difficult to detect. To improve the accuracy of small object detection using images taken by unmanned aerial vehicles (UAV), this study proposes a feature-enhancement detection algorithm based on a single shot multibox detector (SSD), named composite backbone single shot multibox detector (CBSSD), which uses a composite connection backbone to enhance feature representation. First, to enhance the detection effect of small objects, the lead backbone network, VGG16, is kept constant, and ResNet50 is added as an assistant backbone network, and the residual structure in ResNet50 is used to obtain lower feature information. The obtained lower feature information is then fused to the lead network through feature fusion, allowing the lead network to retain rich lower feature information. Finally, the lower feature information in the prediction layer increases. The experimental results show that CBSSD has a significantly higher recognition rate and a lower false detection rate than conventional algorithms, and it still maintains a good detection effect under low illumination. This is of great significance to small object detection using images taken by UAVs in traffic scenes. Furthermore, a method to improve the SSD algorithm is proposed.

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Identification of Intrinsically Disordered Protein Regions Based on Deep Neural Network-VGG16

Cited by 12 publications

References 35 publications

Identifying Intrinsically Disordered Protein Regions through a Deep Neural Network with Three Novel Sequence Features

Identifying Intrinsically Disordered Protein Regions through a Deep Neural Network with Three Novel Sequence Features

Prediction of Tumor Mutation Load in Colorectal Cancer Histopathological Images Based on Deep Learning

UAV Image Small Object Detection Based on Composite Backbone Network

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