Dataset Information

HN-CNN: A Heterogeneous Network Based on Convolutional Neural Network for m⁷ G Site Disease Association Prediction.

ABSTRACT: N⁷-methylguanosine (m⁷G) is a typical positively charged RNA modification, playing a vital role in transcriptional regulation. m⁷G can affect the biological processes of mRNA and tRNA and has associations with multiple diseases including cancers. Wet-lab experiments are cost and time ineffective for the identification of disease-related m⁷G sites. Thus, a heterogeneous network method based on Convolutional Neural Networks (HN-CNN) has been proposed to predict unknown associations between m⁷G sites and diseases. HN-CNN constructs a heterogeneous network with m⁷G site similarity, disease similarity, and disease-associated m⁷G sites to formulate features for m⁷G site-disease pairs. Next, a convolutional neural network (CNN) obtains multidimensional and irrelevant features prominently. Finally, XGBoost is adopted to predict the association between m⁷G sites and diseases. The performance of HN-CNN is compared with Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), as well as Gradient Boosting Decision Tree (GBDT) through 10-fold cross-validation. The average AUC of HN-CNN is 0.827, which is superior to others.

SUBMITTER: Zhang L

PROVIDER: S-EPMC7970120 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

HN-CNN: A Heterogeneous Network Based on Convolutional Neural Network for m7 G Site Disease Association Prediction.

Zhang Lin L Chen Jin J Ma Jiani J Liu Hui H

Frontiers in genetics 20210304

N7-methylguanosine (m7G) is a typical positively charged RNA modification, playing a vital role in transcriptional regulation. m7G can affect the biological processes of mRNA and tRNA and has associations with multiple diseases including cancers. Wet-lab experiments are cost and time ineffective for the identification of disease-related m7G sites. Thus, a heterogeneous network method based on Convolutional Neural Networks (HN-CNN) has been proposed to ...[more]

PMID: 33747055

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Project description:Radiation pneumonitis (RP) is one of the major side effects of thoracic radiotherapy. The aim of this study is to build a dose distribution based prediction model, and investigate the correlation of RP incidence and high-order features of dose distribution. A convolution 3D (C3D) neural network was used to construct the prediction model. The C3D network was pre-trained for action recognition. The dose distribution was used as input of the prediction model. With the C3D network, the convolution operation was performed in 3D space. The guided gradient-weighted class activation map (grad-CAM) was utilized to locate the regions of dose distribution which were strongly correlated with grade≥2 and grade<2 RP cases, respectively. The features learned by the convolution filters were generated with gradient ascend to understand the deep network. The performance of the C3D prediction model was evaluated by comparing with three multivariate logistic regression (LR) prediction models, which used the dosimetric, normal tissue complication probability (NTCP) or dosiomics factors as input, respectively. All the prediction models were validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT). The area under curve (AUC) of C3D prediction model was 0.842. While the AUC of the three LR models were 0.676, 0.744 and 0.782, respectively. The guided grad-CAM indicated that the low-dose region of contralateral lung and high-dose region of ipsilateral lung were strongly correlated with the grade≥2 and grade<2 RP cases, respectively. The features learned by shallow filters were simple and globally consistent, and of monotonous color. The features of deeper filters displayed more complicated pattern, which was hard or impossible to give strict mathematical definition. In conclusion, we built a C3D model for thoracic radiotherapy toxicity prediction. The results demonstrate its performance is superior over the classical LR models. In addition, CNN also offers a new perspective to further understand RP incidence.

Dataset Information

HN-CNN: A Heterogeneous Network Based on Convolutional Neural Network for m⁷ G Site Disease Association Prediction.

Publications

HN-CNN: A Heterogeneous Network Based on Convolutional Neural Network for m<sup>7</sup> G Site Disease Association Prediction.

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