scholarly journals Study onYang-XuUsing Body Constitution Questionnaire and Blood Variables in Healthy Volunteers

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Hong-Jhang Chen ◽  
Yii-Jeng Lin ◽  
Pei-Chen Wu ◽  
Wei-Hsiang Hsu ◽  
Wan-Chung Hu ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Logistic Regression Model ◽  
Cross Validation ◽  
Blood Biomarkers ◽  
Metabolic Characteristics ◽  
Body Constitution ◽  
Leave One Out ◽  
The Relationship ◽  
Fold Cross Validation ◽  
Blood Variables

Traditional Chinese medicine (TCM) formulates treatment according to body constitution (BC) differentiation. Different constitutions have specific metabolic characteristics and different susceptibility to certain diseases. This study aimed to assess theYang-Xuconstitution using a body constitution questionnaire (BCQ) and clinical blood variables. A BCQ was employed to assess the clinical manifestation ofYang-Xu. The logistic regression model was conducted to explore the relationship between BC scores and biomarkers. Leave-one-out cross-validation (LOOCV) and K-fold cross-validation were performed to evaluate the accuracy of a predictive model in practice. Decision trees (DTs) were conducted to determine the possible relationships between blood biomarkers and BC scores. According to the BCQ analysis, 49% participants without any BC were classified as healthy subjects. Among them, 130 samples were selected for further analysis and divided into two groups. One group comprised healthy subjects without any BC (68%), while subjects of the other group, named as the sub-healthy group, had three BCs (32%). Six biomarkers, CRE, TSH, HB, MONO, RBC, and LH, were found to have the greatest impact on BCQ outcomes inYang-Xusubjects. This study indicated significant biochemical differences inYang-Xusubjects, which may provide a connection between blood variables and theYang-XuBC.

scholarly journals IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Cheng Yan ◽  
Guihua Duan ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
Infectious Diseases ◽  
Cross Validation ◽  
Sequence Similarity ◽  
Least Square ◽  
Computational Method ◽  
Receptor Interaction ◽  
Virus Receptor ◽  
Receptor Interactions ◽  
Leave One Out ◽  
Fold Cross Validation

Abstract Background Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited. Result In this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF). Conlusion The experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.

scholarly journals Reduced MLH3 Expression in the Syndrome of Gan-Shen Yin Deficiency in Patients with Different Diseases

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Juan Du ◽  
Maofeng Zhong ◽  
Dong Liu ◽  
Shufang Liang ◽  
Xiaolin Liu ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Chinese Medicine ◽  
Roc Analysis ◽  
Primary Liver Cancer ◽  
The Body ◽  
Metabolic Characteristics ◽  
Yin Deficiency ◽  
Body Constitution ◽  
Deficiency Group ◽  
The Relationship

Traditional Chinese medicine formulates treatment according to body constitution (BC) differentiation. Different constitutions have specific metabolic characteristics and different susceptibility to certain diseases. This study aimed to assess the characteristic genes of gan-shen Yin deficiency constitution in different diseases. Fifty primary liver cancer (PLC) patients, 94 hypertension (HBP) patients, and 100 diabetes mellitus (DM) patients were enrolled and classified into gan-shen Yin deficiency group and non-gan-shen Yin deficiency group according to the body constitution questionnaire to assess the clinical manifestation of patients. The mRNA expressions of 17 genes in PLC patients with gan-shen Yin deficiency were different from those without gan-shen Yin deficiency. However, considering all patients with PLC, HBP, and DM, only MLH3 was significantly lower in gan-shen Yin deficiency group than that in non-gen-shen Yin deficiency. By ROC analysis, the relationship between MLH3 and gan-shen Yin deficiency constitution was confirmed. Treatment of MLH3 (−/− and −/+) mice with Liuweidihuang wan, classical prescriptions for Yin deficiency, partly ameliorates the body constitution of Yin deficiency in MLH3 (−/+) mice, but not in MLH3 (−/−) mice. MLH3 might be one of material bases of gan-shen Yin deficiency constitution.

RESAMPLING METHODS IN SOFTWARE QUALITY CLASSIFICATION

2012 ◽  
Vol 22 (02) ◽  
pp. 203-223 ◽  
Author(s):  
WASIF AFZAL ◽  
RICHARD TORKAR ◽  
ROBERT FELDT
Keyword(s):  
Software Engineering ◽  
Software Quality ◽  
Cross Validation ◽  
Predictor Variables ◽  
Primary Study ◽  
Data Sets ◽  
Resampling Methods ◽  
Quality Classification ◽  
Leave One Out ◽  
Fold Cross Validation

In the presence of a number of algorithms for classification and prediction in software engineering, there is a need to have a systematic way of assessing their performances. The performance assessment is typically done by some form of partitioning or resampling of the original data to alleviate biased estimation. For predictive and classification studies in software engineering, there is a lack of a definitive advice on the most appropriate resampling method to use. This is seen as one of the contributing factors for not being able to draw general conclusions on what modeling technique or set of predictor variables are the most appropriate. Furthermore, the use of a variety of resampling methods make it impossible to perform any formal meta-analysis of the primary study results. Therefore, it is desirable to examine the influence of various resampling methods and to quantify possible differences. Objective and method: This study empirically compares five common resampling methods (hold-out validation, repeated random sub-sampling, 10-fold cross-validation, leave-one-out cross-validation and non-parametric bootstrapping) using 8 publicly available data sets with genetic programming (GP) and multiple linear regression (MLR) as software quality classification approaches. Location of (PF, PD) pairs in the ROC (receiver operating characteristics) space and area under an ROC curve (AUC) are used as accuracy indicators. Results: The results show that in terms of the location of (PF, PD) pairs in the ROC space, bootstrapping results are in the preferred region for 3 of the 8 data sets for GP and for 4 of the 8 data sets for MLR. Based on the AUC measure, there are no significant differences between the different resampling methods using GP and MLR. Conclusion: There can be certain data set properties responsible for insignificant differences between the resampling methods based on AUC. These include imbalanced data sets, insignificant predictor variables and high-dimensional data sets. With the current selection of data sets and classification techniques, bootstrapping is a preferred method based on the location of (PF, PD) pair data in the ROC space. Hold-out validation is not a good choice for comparatively smaller data sets, where leave-one-out cross-validation (LOOCV) performs better. For comparatively larger data sets, 10-fold cross-validation performs better than LOOCV.

scholarly journals Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study

10.2196/22555 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e22555
Author(s):  
Yao Lu ◽  
Tianshu Zhou ◽  
Yu Tian ◽  
Shiqiang Zhu ◽  
Jingsong Li
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Logistic Regression Model ◽  
Cross Validation ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Medical Data ◽  
Multiple Sources ◽  
Model Training ◽  
Fold Cross Validation

Background Data sharing in multicenter medical research can improve the generalizability of research, accelerate progress, enhance collaborations among institutions, and lead to new discoveries from data pooled from multiple sources. Despite these benefits, many medical institutions are unwilling to share their data, as sharing may cause sensitive information to be leaked to researchers, other institutions, and unauthorized users. Great progress has been made in the development of secure machine learning frameworks based on homomorphic encryption in recent years; however, nearly all such frameworks use a single secret key and lack a description of how to securely evaluate the trained model, which makes them impractical for multicenter medical applications. Objective The aim of this study is to provide a privacy-preserving machine learning protocol for multiple data providers and researchers (eg, logistic regression). This protocol allows researchers to train models and then evaluate them on medical data from multiple sources while providing privacy protection for both the sensitive data and the learned model. Methods We adapted a novel threshold homomorphic encryption scheme to guarantee privacy requirements. We devised new relinearization key generation techniques for greater scalability and multiplicative depth and new model training strategies for simultaneously training multiple models through x-fold cross-validation. Results Using a client-server architecture, we evaluated the performance of our protocol. The experimental results demonstrated that, with 10-fold cross-validation, our privacy-preserving logistic regression model training and evaluation over 10 attributes in a data set of 49,152 samples took approximately 7 minutes and 20 minutes, respectively. Conclusions We present the first privacy-preserving multiparty logistic regression model training and evaluation protocol based on threshold homomorphic encryption. Our protocol is practical for real-world use and may promote multicenter medical research to some extent.

scholarly journals Rapid Identification of COVID-19 Severity in CT Scans through Classification of Deep Features

2020 ◽  
Author(s):  
Zekuan Yu ◽  
Xiaohu Li ◽  
Haitao Sun ◽  
Jian Wang ◽  
Tongtong Zhao ◽  
...  
Keyword(s):  
Cross Validation ◽  
Rapid Identification ◽  
Ct Scans ◽  
Accurate Identification ◽  
Linear Discriminant ◽  
Holdout Validation ◽  
Novel Coronavirus ◽  
Leave One Out ◽  
Fold Cross Validation

Abstract Background: To implement the real-time diagnosis of the severity of patients infected with novel coronavirus 2019 (COVID-19) and guide the follow-up therapeutic treatment, We collected chest CT scans of 202 patients diagnosed with the COVID-19 from three hospitals in Anhui Province, China.Methods: A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. Four pre-trained deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) with multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) were applied to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, 10-fold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines. Results and conclusion: The experimental results demonstrate that classification of the features from pre-trained deep models show the promising application in COVID-19 screening whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for 10-fold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions.

scholarly journals First-Trimester Screening for Gestational Diabetes Mellitus Based on Maternal Characteristics and History

2014 ◽  
Vol 38 (1) ◽  
pp. 14-21 ◽  
Author(s):  
Argyro Syngelaki ◽  
Alice Pastides ◽  
Reena Kotecha ◽  
Alan Wright ◽  
Ranjit Akolekar ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Logistic Regression Model ◽  
Cross Validation ◽  
Nice Guidelines ◽  
Maternal Characteristics ◽  
History Of ◽  
Fold Cross Validation

Objectives: To develop and validate a prediction model for gestational diabetes mellitus (GDM) at 11-13 weeks' gestation based on maternal characteristics and history and to compare its performance with the method recommended by the National Institute of Health and Care Excellence (NICE) and five other published prediction models. Methods: A predictive logistic regression model for GDM was developed from 1,827 cases (2.4%) who developed GDM and 73,334 unaffected controls. A 5-fold cross-validation study was performed to validate this model and to compare its performance with those of the NICE guidelines and the previously published models. Results: In the logistic regression model, maternal age, weight, height, racial origin, family history of diabetes, use of ovulation drugs, birth weight, and previous history of GDM were found to be significant predictors of GDM. In screening for GDM in the 5-fold cross-validation study, detection rates (DRs) were higher (p < 0.0001) for the proposed model (DR = 83.2%) than for the NICE guidelines (DR = 77.5%) for a false positive rate of approximately 40% (determined by NICE). The area under the receiver operating characteristic curve of the new model was higher (p < 0.0001) than that of the previous five models (0.823 vs. 0.688-786). Conclusions: Early effective screening for GDM can be achieved based on maternal characteristics and history.

scholarly journals QSPR study of supercooled liquid vapour pressures of PBDEs by using molecular distance-edge vector index

2015 ◽  
Vol 80 (4) ◽  
pp. 499-508 ◽  
Author(s):  
Long Jiao ◽  
Xiaofei Wang ◽  
Shan Bing ◽  
Zhiwei Xue ◽  
Hua Li
Keyword(s):  
Cross Validation ◽  
Quantitative Relationship ◽  
Supercooled Liquid ◽  
Structure Property ◽  
Ann Model ◽  
Linear Network ◽  
Prediction Ability ◽  
Leave One Out ◽  
Fold Cross Validation ◽  
Edge Vector

The quantitative structure property relationship (QSPR) for supercooled liquid vapour pressures (PL) of PBDEs was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and lgPL was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave-one-out cross validation and k-fold cross validation were carried out to assess the prediction ability of the developed models. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and k-fold cross validation is 9.95 and 9.05 respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and k-fold cross validation is 8.75 and 8.31 respectively. It is demonstrated the established models are practicable for predicting the lgPL of PBDEs. The MDEV index is quantitatively related to the lgPL of PBDEs. MLR and L-ANN are practicable for modeling this relationship. Compared with MLR, ANN shows slightly higher prediction accuracy. Subsequently, an MLR model, which regression equation is lgPL = 0.2868 M11 - 0.8449 M12 - 0.0605, and an ANN model, which is a two inputs linear network, were developed. The two models can be used to predict the lgPL of each PBDE.

scholarly journals Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

2021 ◽  
Vol 9 ◽  
Author(s):  
Deliang Sun ◽  
Haijia Wen ◽  
Jiahui Xu ◽  
Yalan Zhang ◽  
Danzhou Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Landslide Susceptibility ◽  
Logistic Regression Model ◽  
Cross Validation ◽  
Dominant Factor ◽  
Mountainous Area ◽  
Prediction Ability ◽  
Before And After ◽  
Fold Cross Validation

This study aims to develop a logistic regression model of landslide susceptibility based on GeoDetector for dominant-factor screening and 10-fold cross validation for training sample optimization. First, Fengjie county, a typical mountainous area, was selected as the study area since it experienced 1,522 landslides from 2001 to 2016. Second, 22 factors were selected as the initial conditioning factors, and a geospatial database was established with a grid of 30 m precision. Factor detection of the geographic detector and the stepwise regression method included in logistic regression were used to screen out the dominant factors from the database. Then, based on the sample dataset with a 1:10 ratio of landslides and nonlandslides, 10-fold cross validation was used to select the optimized sample to train the logistic regression model of landslide susceptibility in the study area. Finally, the accuracy and efficiency of the two models before and after screening out the dominant factors were evaluated and compared. The results showed that the total accuracy of the two models was both more than 0.9, and the area under the curve value of the receiver operating characteristic curve was more than 0.8, indicating that the models before and after screening factor both had high reliability and good prediction ability. Besides, the screened factors had an active leading role in the geospatial distribution of the historical landslide, indicating that the screened dominant factors have individual rationality. Improving the geospatial agreement between landslide susceptibility and actual landslide-prone by the screening of dominant factors and the optimization of the training samples, a simple, efficient, and reliable logistic-regression–based landslide susceptibility model can be constructed.

scholarly journals The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds

2019 ◽  
pp. 100-105
Author(s):  
Gokhan Altan ◽  
Yakup Kutlu ◽  
Adnan Ozhan Pekmezci ◽  
Serkan Nural
Keyword(s):  
Healthy Subjects ◽  
Cross Validation ◽  
Classification Performance ◽  
Lung Sounds ◽  
Deep Belief Networks ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Sensitivity And Selectivity ◽  
Fold Cross Validation

Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the Hilbert-Huang Transform to the lung sounds from 12 different auscultation regions on the chest and back. The classification of the lung sounds from asthma and healthy subjects is performed using Deep Belief Networks (DBN). The DBN classifier model with two hidden layers has been tested using 5-fold cross validation method. The proposed DBN separated lung sounds from asthmatic and healthy subjects with high classification performance rates of 84.61%, 85.83%, and 77.11% for overall accuracy, sensitivity, and selectivity, respectively using frequencytime analysis.

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