Current article: Mechanism of CAV and CAVIN Family Genes in Acute Lung Injury based on DeepGene

Author(s): Changsheng LiHexiao TangZetian YangZheng TangNitao ChengJingyu Huang* and Xuefeng Zhou*

Background: The fatality rate of acute lung injury (ALI) is as high as 40% to 60%. Although various factors, such as sepsis, trauma, pneumonia, burns, blood transfusion, cardiopulmonary bypass, and pancreatitis, can induce ALI, patients with these risk factors will eventually develop ALI. The rate of developing ALI is not high, and the outcomes of ALI patients vary, indicating that it is related to genetic differences between individuals. In a previous study, we found multiple functions of cavin-2 in lung function. In addition, many other studies have revealed that CAV1 is a critical regulator of lung injury. Due to the strong relationship between cavin-2 and CAV1, we suspect that cavin-2 is also associated with ALI. Furthermore, we are curious about the role of the CAV family and Cavin family genes in ALI.

Methods: To reveal the mechanism of CAV and CAVIN family genes in ALI, we propose Deepgene to predict whether CAV and CAVIN family genes are associated with ALI. This method constructs a gene interaction network and extracts gene expression in 84 tissues. We divided these features into two groups and used two network encoders to encode and learn the features.

Results: Compared with DNN, GBDT, RF and KNN, the AUC of Deepgene increased by 7.89%, 16.84%, 20.19% and 32.01%, respectively. The AUPR scores increased by 8.05%, 15.58%, 22.56% and 23.34%. DeepGENE shows that CAVIN-1, CAVIN-2, CAVIN-3 and CAV2 are related to ALI.

Conclusion: DeepGENE is a reliable method for identifying acute lung injury-related genes. Multiple CAV and CAVIN family genes are associated with acute lung injury-related genes through multiple pathways and gene functions.

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Most Cited Article – An Automatic Instrument Recognition Approach Based on Deep Convolutional Neural Network

Author(s):Jiangyan Ke Rongchuan Lin and Ashutosh Sharma*

Volume 14, Issue 6, 2021

Published on: 21 March, 2021

Page: [660 – 670]

Pages: 11

DOI: 10.2174/2352096514666210322155008


Background: This paper presents an automatic instrument recognition method highlighting the deep learning aspect of instrument identification in order to advance the automatic process of video monitoring remotely equipment of substation.

Methods: This work utilizes the Scale Invariant Feature Transform approach (SIFT) and the Gaussian difference model for instrument positioning while proposing a design scheme of the instrument identification system.

Results: The experimental outcomes obtained proves that the proposed system is capable of automatically recognizing a modest graphical interface and study independently while improving the effectiveness of the appliance, thereby, realizing the purpose of spontaneous self-check. The proposed approach is applicable for musical instrument recognition, and it provides 92% of the accuracy rate, 87.5% precision value and recall rate of 91.2%.

Conclusion: The comparative analysis with other state-of-the-art methods justifies that the proposed deep learning-based music recognition method outperforms the other existing approaches in terms of accuracy, thereby providing a practicable music instrument recognition solution. Read now:

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