Predicting Hepatoma-Related Genes Based on Representation Learning of PPI network and Gene Ontology Annotations

Wang, Tao; Shao, Zhiyuan; Xiao, Yifu; Zhang, Xuchao; Chen, Yitian; Shi, Binze; Chen, Siyu; Wang, Yuxian; Peng, Jiajie; Shang, Xuequn

doi:10.1109/bibm52615.2021.9669479

Cited by 7 publications

(8 citation statements)

References 37 publications

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“…102 MS-related miRNAs are extracted from the database as positive samples. Previous works usually randomly select the negative samples from the unlabeled disease associations ( Peng et al, 2019 ; Wang et al, 2021 ). And they usually select a collection of negative samples with size equal to the positive samples.…”

Section: Resultsmentioning

confidence: 99%

Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods

Sun¹,

Ren²,

Zhang³

et al. 2022

Front. Genet.

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Identifying biomarkers of Multiple Sclerosis is important for the diagnosis and treatment of Multiple Sclerosis. The existing study has shown that miRNA is one of the most important biomarkers for diseases. However, few existing methods are designed for predicting Multiple Sclerosis-related miRNAs. To fill this gap, we proposed a novel computation framework for predicting Multiple Sclerosis-associated miRNAs. The proposed framework uses a network representation model to learn the feature representation of miRNA and uses a deep learning-based model to predict the miRNAs associated with Multiple Sclerosis. The evaluation result shows that the proposed model can predict the miRNAs associated with Multiple Sclerosis precisely. In addition, the proposed model can outperform several existing methods in a large margin.

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Section: Resultsmentioning

confidence: 99%

Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods

Sun¹,

Ren²,

Zhang³

et al. 2022

Front. Genet.

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“…35 In paper, 36 xQTLImp is specifically designed to provide a framework for efficient imputation of molecular QTL summary statistics, which can enhance the discoveries of QTL studies, especially for those with small sample size. In paper, 37 the authors proposed a novel framework to predict hepatoma-related genes based on representation learning from both protein-protein interactions networks and gene ontology annotations. The results showed that the framework could accurately predict liver cancer-related genes.…”

Section: Output Layermentioning

confidence: 99%

Diagnosis of Parkinson's disease based on feature fusion on T2 MRI images

Cui

Lou

et al. 2022

Int J of Intelligent Sys

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Deep‐learning methods (especially convolutional neural networks) using magnetic resonance imaging (MRI) data have been successfully applied to computer‐aided diagnosis of Parkinson's Disease (PD). Early detection and prior care may help patients improve their quality of life, although this neurodegenerative disease has no known cure. In this study, we propose a FResnet18 model to classify MRI images of PD and Health Control (HC) by fusing image texture features with deep features. First, Local Binary Pattern and Gray‐Level Co‐occurrence Matrix are used to extract the handcrafted features. Second, the modified ResNet18 network is used to extract deep features. Finally, the fused features are classified by Support Vector Machine. The classification accuracy rate for MRI images reaches 98.66%, and the findings demonstrate that the model can successfully differentiate between PD and HC. The suggested FResnet18 provides greater performance compared with existing approaches, and it is shown through extensive experimental findings on the Parkinson's Disease Progression Markers Initiative data set that feature fusion may improve classification performance.

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“…With the development of experimental instruments, the EEG data has been accumulated and aided the psychological and biological research together with multimodal omics ( 6 ). A series of computational methods and tools have been developed to deal with such data challenges ( 7 – 9 ). Particularly, a variety of signal analysis methods have been proposed to capture the characteristics of the EEG signals ( 10 , 11 ).…”

Section: Introductionmentioning

confidence: 99%

DEEMD-SPP: A Novel Framework for Emotion Recognition Based on EEG Signals

Chen

et al. 2022

Front. Psychiatry

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Electroencephalography (EEG) is one of the most widely-used biosignal capturing technology for investigating brain activities, cognitive diseases, and affective disorders. To understand the underlying principles of brain activities and affective disorders using EEG data, one of the fundamental tasks is to accurately identify emotions from EEG signals, which has attracted huge attention in the field of affective computing. To improve the accuracy and effectiveness of emotion recognition based on EEG data, previous studies have successfully developed numerous feature extraction methods and classifiers. Among them, ensemble empirical mode decomposition (EEMD) is an efficient signal decomposition technique for extracting EEG features. It can alleviate the mode-mixing problem by adding white noise to the source signal. However, there remain some issues when applying this method to recognition tasks. As the added noise cannot be filtered completely, spurious modes are generated due to the residual noise. Therefore, it is crucial to perform intrinsic mode function (IMF) selection to find the most valuable IMF components that represent brain activities. Furthermore, the number of decomposed IMFs is various to different original signals, thus how to unify feature dimensions needs better solutions. To solve these issues, we propose a novel forecasting framework, named DEEMD-SPP, to identify emotions from EEG signals, based on the combination of denoising ensemble empirical mode decomposition (DEEMD) and Spatial Pyramid Pooling Network (SPP-Net). First, DEEMD is proposed to decompose the EEG signals, which effectively eliminates residual noise in the IMFs and selects the most valuable IMFs. Second, time-domain and frequency-domain features are extracted from the selected IMFs. Finally, SPP-net is employed as the classifier to recognize emotions, which can effectively transform various-sized feature maps into fixed-sized feature vectors through the pyramid pooling layer. The experimental results demonstrate that our proposed DEEMD-SPP framework can effectively reduce the effect of spike-in white noise, accurately extract EEG features, and significantly improve the performance of emotion recognition.

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Predicting Hepatoma-Related Genes Based on Representation Learning of PPI network and Gene Ontology Annotations

Cited by 7 publications

References 37 publications

Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods

Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods

Diagnosis of Parkinson's disease based on feature fusion on T2 MRI images

DEEMD-SPP: A Novel Framework for Emotion Recognition Based on EEG Signals

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