Differential analysis of gene-level expression is a commonly used approach for identifying disease-associated genes. Recently, intron retention (IR) has been shown to be associated with complex diseases such as cancer...
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ISBN:
(数字)9781728162157
ISBN:
(纸本)9781728162164
Differential analysis of gene-level expression is a commonly used approach for identifying disease-associated genes. Recently, intron retention (IR) has been shown to be associated with complex diseases such as cancers. IR provides value that is complementary to traditional gene-level expression. However, a systematic method to exploit IR for identifying disease-associated genes remains largely unexplored. We developed a pipeline to identify the disease-associated gene based on differential intron retention (IRDAG), which integrates IR events detected by two methods, IRFinder and iREAD. We applied it to Alzheimer's disease (AD). We found that many of the differential genes detected based on IR were not able to be discovered by the traditional gene-level differential expression method, suggesting that our method is complementary to traditional methods. We showed that the differential genes identified with IRDAG were functionally related to AD based on the analysis of protein-protein interaction networks and brain-specific functional gene networks. Being complementary to the existing method, IRDAG provides a new and generic approach for identifying the disease-associated gene.
Background: Oseltamivir, a neuraminidase inhibitor (NAI), is the primary and first-line anti-influenza drug. In recent years, more and more oseltamivir resistant strains appeared frequently. Purpose: To identify anti-...
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Dear editor,In recent years, the number of mobile malware has increased at an alarming rate. According to a report from G DATA [1], there are approximately 9000 new Android malware instances each *** malicious applica...
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Dear editor,In recent years, the number of mobile malware has increased at an alarming rate. According to a report from G DATA [1], there are approximately 9000 new Android malware instances each *** malicious applications pose grave threats to the security of the Android ecosystem.
With the rapid development of information technology,the explosive growth of data information has become a common challenge and *** network services represented by WeChat,Weibo and Twitter,drive a large amount of info...
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With the rapid development of information technology,the explosive growth of data information has become a common challenge and *** network services represented by WeChat,Weibo and Twitter,drive a large amount of information due to the continuous spread,evolution and emergence of users through these *** dynamic modeling,analysis,and network information prediction,has very important research and application value,and plays a very important role in the discovery of popular events,personalized information recommendation,and early warning of bad *** these reasons,this paper proposes an adaptive prediction algorithm for network information transmission.A popularity prediction algorithm is designed to control the transmission trend based on the gray Verhulst model to analyze the law of development and capture popular *** simulations show that the proposed perceptual prediction model in this paper has a better fitting effect than the existing models.
MicroRNAs (miRNAs) are a class of non-coding RNAs of approximately 22 nucleotides. Cumulative evidence from biological experiments has confirmed that miRNAs play a key role in many complex human diseases. Therefore, t...
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Epidermal growth factor receptor (EGFR) genotyping is essential to treatment guidelines for the use of tyrosine kinase inhibitors in lung adenocarcinoma. However, accurate and noninvasive methods to detect the EGFR ge...
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Epidermal growth factor receptor (EGFR) genotyping is essential to treatment guidelines for the use of tyrosine kinase inhibitors in lung adenocarcinoma. However, accurate and noninvasive methods to detect the EGFR gene are ongoing challenges. In this study, we propose a hybrid framework, namely HC-DLR, to noninvasively predict EGFR mutation status by fusing multi-source features including low-level handcrafted radiomics (HCR) features, high-level deep learning-based radiomics (DLR) features, and demographics features. The HCR features first are selected from massive handcrafted features extracted from CT images. The DLR features are also extracted from CT images using the pre-trained 3D DenseNet. Then, multi-source feature representations are refined and fused to build an HC-DLR model for improving the predictive performance of EGFR mutations. The proposed method is evaluated on a newly collected dataset with 670 patients. Experimental results show that the HC-DLR model achieves an encouraging predictive performance with an AUC of 0.76, an accuracy of 72.47%, and an F1-score of 71.35%, which may have potential clinical value for predicting EGFR mutations in lung adenocarcinoma.
Given a graph (Formula Presented) and a fixed linear order (Formula Presented) of V, the problem fixed-order book thickness asks whether there is a page assignment (Formula Presented) such that (Formula Presented) is ...
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Medical image segmentation is the primary measure of medical image analysis. With the development of deep learning, U-net based approaches have presented for different medical image segmentation tasks. However, the po...
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ISBN:
(数字)9781728162157
ISBN:
(纸本)9781728162164
Medical image segmentation is the primary measure of medical image analysis. With the development of deep learning, U-net based approaches have presented for different medical image segmentation tasks. However, the pooling and the simple convolution operation for deep feature maps in the U-shaped network would lead to the coarse segmentation result. In this paper, we design a local adaptive U-net (LA U-net) for medical image segmentation. There are two major modules: the Local Adaptive Module (LAM) and Multi-scale Convolution Module (MCM) in the network. The LAM get more feature maps from each down-sampling process. The MCM capture more global information for the encoding path. To validate the proposed network's performance, we verify it on two datasets: DRIVE dataset, and ISIC 2018 dataset; the results show that LA U-net achieves superior performance on two datasets.
The cause of Alzheimer's disease (AD) is insufficient to understand so far, and its diagnosis is challenging in clinical practice. Recently, the convolutional neural network (CNN) model has shown impressive perfor...
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ISBN:
(数字)9781728162157
ISBN:
(纸本)9781728162164
The cause of Alzheimer's disease (AD) is insufficient to understand so far, and its diagnosis is challenging in clinical practice. Recently, the convolutional neural network (CNN) model has shown impressive performance in medical image analysis. Combining CNN with magnetic resonance imaging (MRI) image has excellent potential for AD diagnosis. However, it is still a challenging task. To address the challenge, we propose a joint learning method based on multi-scale representation (JL-MSR). The multi-scale representation is proposed to obtain more feature maps by the multi-scale atrous convolutions. Furthermore, in order to use the intrinsic relationship between diagnostic results and clinical scores, we propose a joint learning strategy using the diagnosis result as the primary label and the Mini-Mental State Examination (MMSE) score as the secondary label to joint training. The proposed method is evaluated on a dataset of 417 subjects (including 188 AD and 229 health controls (HC)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed method achieves an accuracy of 88.1% and an area under the receiver operating characteristic (ROC) curve (AUC) value of 0.942 for AD diagnosis, respectively. Compared with a state-of-the-art method in AD diagnosis, our proposed method performs better, and has potential in clinical diagnosis.
Trial and error learning is an approach with uncertain consequences. How to maintain policy security, stability, and efficiency under controlled circumstances, posing a significant academic challenge. Such as Reinforc...
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