2020
DOI: 10.1109/access.2019.2962091
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Network-Based Computational Approach to Identify Delineating Common Cell Pathways Influencing Type 2 Diabetes and Diseases of Bone and Joints

Abstract: Developing type 2 diabetes (T2D) can increase patient risk of developing other common diseases and exacerbate their severity, including diseases that affect bone and joints. Such comorbidity interactions are hard to study in detail by traditional endocrinological methods. Thus, we developed tissue transcript analytical approaches to identify common pathways through which these diseases can interact. We examined RNAseq and microarray transcript datasets from studies of T2D and chronic bone and joint diseases, n… Show more

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Cited by 15 publications
(8 citation statements)
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“…Traditional statistical approaches are not suitable for detecting gene interactions, especially when interactions appear between more than two genes, or when the data are high-dimensional, meaning the data have many attributes or independent variables ( McKinney et al, 2006 ; Lai et al, 2019 ). Machine learning approaches have been widely used to identify disease biomarkers ( Lim et al, 2019 ; Moni et al, 2019 ; Tabl et al, 2019 ; Sanchez and Mackenzie, 2020 ). Recently, Sanchez et al identified methylation biomarkers for leukemia by investigating PPI for differentially methylated genes (DMGs) and differentially expressed genes (DEGs) using machine learning approach ( Sanchez and Mackenzie, 2020 ).…”
Section: Introductionmentioning
confidence: 99%
“…Traditional statistical approaches are not suitable for detecting gene interactions, especially when interactions appear between more than two genes, or when the data are high-dimensional, meaning the data have many attributes or independent variables ( McKinney et al, 2006 ; Lai et al, 2019 ). Machine learning approaches have been widely used to identify disease biomarkers ( Lim et al, 2019 ; Moni et al, 2019 ; Tabl et al, 2019 ; Sanchez and Mackenzie, 2020 ). Recently, Sanchez et al identified methylation biomarkers for leukemia by investigating PPI for differentially methylated genes (DMGs) and differentially expressed genes (DEGs) using machine learning approach ( Sanchez and Mackenzie, 2020 ).…”
Section: Introductionmentioning
confidence: 99%
“…A number of studies have developed models for predicting several diseases and comorbidities [9,[43][44][45][46][47][48]. Chun and colleagues [49] have introduced a comorbidity prediction method using filtering technique to predict likely comorbid conditions for individuals and a trajectory prediction graph model to reveal progression paths of the conditions.…”
Section: Discussionmentioning
confidence: 99%
“…Identification of disease-causing crucial biomarkers may shed light on a deeper understanding of the molecular mechanism of disease [ 58 , 59 , 60 , 61 , 62 , 63 ]. The present study was conducted to analyze the NSCLC gene expression data to determine the DEGs, extensive molecular pathways, significant hub proteins, and associated regulatory biomolecules in order to pick up the potential therapeutic targets for NSCLC through a multi-omics data integration framework.…”
Section: Discussionmentioning
confidence: 99%
“…The higher expression pattern of YY1 transcription factor triggered the patients having larger tumor size, differentiation, higher TNM stage, and lymph node metastasis [ 83 ]. The reported TFs are also involved in other cancer diseases [ 58 , 59 , 60 , 61 , 62 , 63 ]. In various types of cancer tissues, the miR-26b-5p acts as a tumor suppressor [ 84 ].…”
Section: Discussionmentioning
confidence: 99%