A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China

Objective. To establish a machine learning model for identifying patients coinfected with hepatitis B virus (HBV) and human immunodeficiency virus (HIV) through two sexual transmission routes in Jiangsu, China. Methods. A total of 14197 HIV cases transmitted by homosexual and heterosexual routes were recruited. After data processing, 12469 cases (HIV and HBV, 1033; HIV, 11436) were left for further analysis, including 7849 cases with homosexual transmission and 4620 cases with heterosexual transmission. Univariate logistic regression was used to select variables with significant P value and odds ratio for multivariable analysis. In homosexual transmission and heterosexual transmission groups, 10 and 6 variables were selected, respectively. For identifying HIV individuals coinfected with HBV, a machine learning model was constructed with four algorithms, including Decision Tree, Random Forest, AdaBoost with decision tree (AdaBoost), and extreme gradient boosting decision tree (XGBoost). The detective value of each variable was calculated using the optimal machine learning algorithm. Results. AdaBoost algorithm showed the highest efficiency in both transmission groups (homosexual transmission group: accuracy = 0.928 , precision = 0.915 , recall = 0.944 , F − 1 = 0.930 , and AUC = 0.96 ; heterosexual transmission group: accuracy = 0.892 , precision = 0.881 , recall = 0.905 , F − 1 = 0.893 , and AUC = 0.98 ). Calculated by AdaBoost algorithm, the detective value of PLA was the highest in homosexual transmission group, followed by CR, AST, HB, ALT, TBIL, leucocyte, age, marital status, and treatment condition; in the heterosexual transmission group, the detective value of PLA was the highest (consistent with the condition in the homosexual group), followed by ALT, AST, TBIL, leucocyte, and symptom severity. Conclusions. The univariate logistics regression combined with the AdaBoost algorithm could accurately screen the risk factors of HBV in HIV coinfection without invasive testing. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

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Quasispecies Pattern of Hepatitis B Virus Predicts Hepatocellular Carcinoma Using Deep-Sequencing and Machine Learning

SSRN Electronic Journal ◽

10.2139/ssrn.3582725 ◽

2020 ◽

Author(s):

Shipeng Chen ◽

Zihan Zhang ◽

Ying Wang ◽

Meng Fang ◽

Jun Zhou ◽

...

Keyword(s):

Machine Learning ◽

Hepatocellular Carcinoma ◽

Hepatitis B Virus ◽

Hepatitis B ◽

Deep Sequencing ◽

B Virus

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AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200325104317 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dhilsath Fathima.M ◽

S. Justin Samuel ◽

R. Hari Haran

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Decision Tree ◽

Learning Model ◽

Training Dataset ◽

Data Set ◽

Machine Learning Model ◽

Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

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Predicting hepatitis B virus–positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning

Nature Medicine ◽

10.1038/nm843 ◽

2003 ◽

Vol 9 (4) ◽

pp. 416-423 ◽

Cited By ~ 574

Author(s):

Qing-Hai Ye ◽

Lun-Xiu Qin ◽

Marshonna Forgues ◽

Ping He ◽

Jin Woo Kim ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Hepatitis B Virus ◽

Hepatitis B ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Supervised Machine Learning ◽

Hepatocellular Carcinomas ◽

B Virus

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Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts

Scientific Reports ◽

10.1038/s41598-019-55445-8 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Alan J. Mueller-Breckenridge ◽

Fernando Garcia-Alcalde ◽

Steffen Wildum ◽

Saskia L. Smits ◽

Robert A. de Man ◽

...

Keyword(s):

Machine Learning ◽

Hepatitis B Virus ◽

Hepatitis B ◽

Clinical Decision ◽

Relative Contribution ◽

Viral Mutant ◽

B Virus ◽

Machine Learning Approach ◽

Comprehensive Survey ◽

Full Length Genome

AbstractChronic infection with Hepatitis B virus (HBV) is a major risk factor for the development of advanced liver disease including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The relative contribution of virological factors to disease progression has not been fully defined and tools aiding the deconvolution of complex patient virus profiles is an unmet clinical need. Variable viral mutant signatures develop within individual patients due to the low-fidelity replication of the viral polymerase creating ‘quasispecies’ populations. Here we present the first comprehensive survey of the diversity of HBV quasispecies through ultra-deep sequencing of the complete HBV genome across two distinct European and Asian patient populations. Seroconversion to the HBV e antigen (HBeAg) represents a critical clinical waymark in infected individuals. Using a machine learning approach, a model was developed to determine the viral variants that accurately classify HBeAg status. Serial surveys of patient quasispecies populations and advanced analytics will facilitate clinical decision support for chronic HBV infection and direct therapeutic strategies through improved patient stratification.

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Heterosexual Transmission as the Most Common Route of Acute Hepatitis B Virus Infection Among Adults in Taiwan--The Importance of Extending Vaccination to Susceptible Adults

The Journal of Infectious Diseases ◽

10.1093/infdis/167.4.938 ◽

1993 ◽

Vol 167 (4) ◽

pp. 938-941 ◽

Cited By ~ 17

Author(s):

M.-C. Hou ◽

J.-C. Wu ◽

B. I.-T. Kuo ◽

W.-Y. Sheng ◽

T.-Z. Chen ◽

...

Keyword(s):

Hepatitis B Virus ◽

Virus Infection ◽

Hepatitis B ◽

Acute Hepatitis ◽

Heterosexual Transmission ◽

Hepatitis B Virus Infection ◽

B Virus ◽

Acute Hepatitis B

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DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites.

10.1101/2021.01.08.425855 ◽

2021 ◽

Author(s):

Canbiao Wu ◽

Xiaofang Guo ◽

Mengyuan Li ◽

Xiayu Fu ◽

Zeliang Hou ◽

...

Keyword(s):

Deep Learning ◽

Hepatitis B Virus ◽

Hepatitis B ◽

Binding Sites ◽

Learning Model ◽

Validation Dataset ◽

B Virus ◽

Integration Sites ◽

Deep Learning Model ◽

Pan Cancer

Hepatitis B virus (HBV) is one of the main causes for viral hepatitis and liver cancer. Previous studies showed HBV can integrate into host genome and further promote malignant transformation. In this study, we developed an attention-based deep learning model DeepHBV to predict HBV integration sites by learning local genomic features automatically. We trained and tested DeepHBV using the HBV integration sites data from dsVIS database. Initially, DeepHBV showed AUROC of 0.6363 and AUPR of 0.5471 on the dataset. Adding repeat peaks and TCGA Pan Cancer peaks can significantly improve the model performance, with an AUROC of 0.8378 and 0.9430 and an AUPR of 0.7535 and 0.9310, respectively. On independent validation dataset of HBV integration sites from VISDB, DeepHBV with HBV integration sequences plus TCGA Pan Cancer (AUROC of 0.7603 and AUPR of 0.6189) performed better than HBV integration sequences plus repeat peaks (AUROC of 0.6657 and AUPR of 0.5737). Next, we found the transcriptional factor binding sites (TFBS) were significantly enriched near genomic positions that were paid attention to by convolution neural network. The binding sites of AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra and Foxo3 were highlighted by DeepHBV attention mechanism in both dsVIS dataset and VISDB dataset, revealing the HBV integration preference. In summary, DeepHBV is a robust and explainable deep learning model not only for the prediction of HBV integration sites but also for further mechanism study of HBV induced cancer.

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