Risk Prediction of Diabetes: Big data mining with fusion of multifarious physical examination indicators

Yang, Hui; Luo, Yu; Ren, Xiaolei; Wu, Ming; He, Xiaolin; Peng, Bowen; Deng, Kejun; Yan, Dan; Tang, Hua; Lin, Hao

doi:10.1016/j.inffus.2021.02.015

Cited by 139 publications

(78 citation statements)

References 38 publications

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“…However, since the current study would involve feature selection as described above, to reduce the computational time, the 10-fold crossvalidation test and independent dataset test would be adopted as done by many investigators using SVM as the prediction engine. The performance can be measured in term of Sensitivity (Sn), Specificity (Sp), F1 score, Matthew's correlation coefficient (MCC), and Accuracy (Acc; Li et al, 2020;Shen and Zou, 2020;Yang et al, 2021)…”

Section: Performance Evaluationmentioning

confidence: 99%

Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy

Zhou

Wang

et al. 2021

Front. Cell Dev. Biol.

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The 2-oxoglutarate/Fe (II)-dependent (2OG) oxygenase superfamily is mainly responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism and plays important roles in cancer, cardiovascular disease, and other diseases. They are likely to become new targets for the treatment of cancer and other diseases, so the accurate identification of 2OG oxygenases is of great significance. Many computational methods have been proposed to predict functional proteins to compensate for the time-consuming and expensive experimental identification. However, machine learning has not been applied to the study of 2OG oxygenases. In this study, we developed OGFE_RAAC, a prediction model to identify whether a protein is a 2OG oxygenase. To improve the performance of OGFE_RAAC, 673 amino acid reduction alphabets were used to determine the optimal feature representation scheme by recoding the protein sequence. The 10-fold cross-validation test showed that the accuracy of the model in identifying 2OG oxygenases is 91.04%. Besides, the independent dataset results also proved that the model has excellent generalization and robustness. It is expected to become an effective tool for the identification of 2OG oxygenases. With further research, we have also found that the function of 2OG oxygenases may be related to their polarity and hydrophobicity, which will help the follow-up study on the catalytic mechanism of 2OG oxygenases and the way they interact with the substrate. Based on the model we built, a user-friendly web server was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ogferaac.

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Section: Performance Evaluationmentioning

confidence: 99%

Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy

Zhou

Wang

et al. 2021

Front. Cell Dev. Biol.

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“…There are a vast array of microbes in human gut, collectively referred to as the microbiota, which is a complex community. The metabolic activities and interactions with the immune system of gut microbiota are not limited the gut itself ( Sonnenburg and Backhed, 2016 ), but also involve a variety of immune-mediated diseases and metabolic diseases such as diabetes, obesity, digestive system diseases, asthma, arthritis, cancers, and cardiovascular disease ( Vieira et al, 2014 ; Sonnenburg and Backhed, 2016 ; Grigoryan et al, 2019 ; Ingham et al, 2019 ; Kolodziejczyk et al, 2019 ; Alemao et al, 2020 ; Morel et al, 2020 ; Yang et al, 2021 ). The abundance of Enterobacteriaceae and Streptococcus spp.…”

Section: Introductionmentioning

confidence: 99%

Dysbiosis of Gut Microbiota in Patients With Acute Myocardial Infarction

et al. 2021

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Acute myocardial infarction (AMI) continues as the main cause of morbidity and mortality worldwide. Interestingly, emerging evidence highlights the role of gut microbiota in regulating the pathogenesis of coronary heart disease, but few studies have systematically assessed the alterations and influence of gut microbiota in AMI patients. As one approach to address this deficiency, in this study the composition of fecal microflora was determined from Chinese AMI patients and links between gut microflora and clinical features and functional pathways of AMI were assessed. Fecal samples from 30 AMI patients and 30 healthy controls were collected to identify the gut microbiota composition and the alterations using bacterial 16S rRNA gene sequencing. We found that gut microflora in AMI patients contained a lower abundance of the phylum Firmicutes and a slightly higher abundance of the phylum Bacteroidetes compared to the healthy controls. Chao1 (P = 0.0472) and PD-whole-tree (P = 0.0426) indices were significantly lower in the AMI versus control group. The AMI group was characterized by higher levels of the genera Megasphaera, Butyricimonas, Acidaminococcus, and Desulfovibrio, and lower levels of Tyzzerella 3, Dialister, [Eubacterium] ventriosum group, Pseudobutyrivibrio, and Lachnospiraceae ND3007 group as compared to that in the healthy controls (P < 0.05). The common metabolites of these genera are mostly short-chain fatty acids, which reveals that the gut flora is most likely to affect the occurrence and development of AMI through the short-chain fatty acid pathway. In addition, our results provide the first evidence revealing remarkable differences in fecal microflora among subgroups of AMI patients, including the STEMI vs. NSTEMI, IRA-LAD vs. IRA-Non-LAD and Multiple (≥2 coronary stenosis) vs. Single coronary stenosis groups. Several gut microflora were also correlated with clinically significant characteristics of AMI patients, including LVEDD, LVEF, serum TnI and NT-proBNP, Syntax score, counts of leukocytes, neutrophils and monocytes, and fasting serum glucose levels. Taken together, the data generated enables the prediction of several functional pathways as based on the fecal microfloral composition of AMI patients. Such information may enhance our comprehension of AMI pathogenesis.

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“…In order to obtain ROC curves and the area under it (Wang et al, 2013;Zhao et al, 2015Zhao et al, , 2017Yang et al, 2020Yang et al, , 2021Zhai et al, 2020), a certain classifier needs to be assigned. Here, we utilize Fisher's linear discriminative analysis.…”

Section: Discussionmentioning

confidence: 99%

Detection of circRNA Biomarker for Acute Myocardial Infarction Based on System Biological Analysis of RNA Expression

et al. 2021

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Acute myocardial infarction (AMI) is myocardial necrosis caused by the persistent interruption of myocardial blood supply, which has high incidence rate and high mortality in middle-aged and elderly people in the worldwide. Biomarkers play an important role in the early diagnosis and treatment of AMI. Recently, more and more researches confirmed that circRNA may be a potential diagnostic biomarker and therapeutic target for cardiovascular diseases. In this paper, a series of biological analyses were performed to find new effective circRNA biomarkers for AMI. Firstly, the expression levels of circRNAs in blood samples of patients with AMI and those with mild coronary stenosis were compared to reveal circRNAs which were involved in AMI. Then, circRNAs which were significant expressed abnormally in the blood samples of patients with AMI were selected from those circRNAs. Next, a ceRNA network was constructed based on interactions of circRNA, miRNA and mRNA through biological analyses to detect crucial circRNA associated with AMI. Finally, one circRNA was selected as candidate biomarker for AMI. To validate effectivity and efficiency of the candidate biomarker, fluorescence in situ hybridization, hypoxia model of human cardiomyocytes, and knockdown and overexpression analyses were performed on candidate circRNA biomarker. In conclusion, experimental results demonstrated that the candidate circRNA was an effective biomarker for diagnosis and therapy of AMI.

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Risk Prediction of Diabetes: Big data mining with fusion of multifarious physical examination indicators

Cited by 139 publications

References 38 publications

Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy

Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy

Dysbiosis of Gut Microbiota in Patients With Acute Myocardial Infarction

Detection of circRNA Biomarker for Acute Myocardial Infarction Based on System Biological Analysis of RNA Expression

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