FIGURE 5 - available via license: Creative Commons Attribution 4.0 International
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| Level effect of Correlated Louvain: (A) shows the intermediate clustering of cluster 21 with k 4 = 0.8, representing the common behavior of intermediate clusters; (B) shows uncommon behavior or intermediate clusters, such as cluster 1 with k 4 = 0.2.
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Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canon...
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Context 1
... study the behavior of the algorithm, the correlation of all intermediate subgraphs that merged to form a subgraph in the final partitioning were plotted. Two examples are shown in Figure 5. For the majority of the top subgraphs, they behave similar to subgraph 21 in Figure 5A, with nodes merging into a single subgraph by the second iteration of the algorithm, with a consistent increase in absolute correlation when doing so. ...
Context 2
... examples are shown in Figure 5. For the majority of the top subgraphs, they behave similar to subgraph 21 in Figure 5A, with nodes merging into a single subgraph by the second iteration of the algorithm, with a consistent increase in absolute correlation when doing so. However, Figure 5B shows that this is not always the case, with a subgraph following a less direct path toward the optimal partitioning. ...
Context 3
... the majority of the top subgraphs, they behave similar to subgraph 21 in Figure 5A, with nodes merging into a single subgraph by the second iteration of the algorithm, with a consistent increase in absolute correlation when doing so. However, Figure 5B shows that this is not always the case, with a subgraph following a less direct path toward the optimal partitioning. Notably, this behavior occurred when the correlation portion of the objective function was weighted less (k L = 0.8), which is also the partitioning which resulted in larger but less correlated top subgraphs. ...
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... Pathway analysis revealed extracellular matrix and phosphatidylinositol-4,5-bisphosphate 3-kinase-protein kinase B signaling pathways as essential signals in the tumor adjacent stroma [114]. A multi-omics approach to decipher complex pathways and networks of potential biomarkers will improve diagnosis, assist staging, decrease complications (PAH and cancer risk) and improve outcomes in COPD [115]. ...
Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.
... COPD is one of the leading causes of death in the United States and could benefit from a multiomics approach to decipher complex pathways and networks of potential biomarkers [76]. Yan et al report airway microbe-host interactions in a study of 99 patients with COPD compared to 36 controls from China [77] for 2 endotypes, neutrophilic or eosinophilic inflammation. ...
Diseases of the lung account for more than 5 million deaths worldwide and are a burden to healthcare. Improving clinical outcomes including mortality and quality of life involves a holistic understanding the etiopathogenesis, which can be provided by multi-omics integration of lung data. An enhanced understanding of large comprehensive datasets provides opportunities to mine those datasets for features that contribute to prevention and amelioration of disease. In this review, we evaluate lung disease models including animal models, organoids and single cell lines as mechanisms to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided a deeper insight into pathogenesis that has resulted in improved preventive and therapeutic interventions.
Biological data may be separated into primary data, such as gene expression, and secondary data, such as pathways and protein-protein interactions. Methods using secondary data to enhance the analysis of primary data are promising, because secondary data have background information that is not included in primary data. In this study, we proposed an end-to-end framework to integrally handle secondary data to construct a classification model for primary data. We applied this framework to cancer prognosis prediction using gene expression data and a biological network. Cross-validation results indicated that our model achieved higher accuracy compared with a deep neural network model without background biological network information. Experiments conducted in patient groups by cancer type showed improvement in ROC-area under the curve for many groups. Visualizations of high accuracy cancer types identified contributing genes and pathways by enrichment analysis. Known biomarkers and novel biomarker candidates were identified through these experiments.
