High-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty

2017 ◽  
Vol 31 (6) ◽  
pp. e2889 ◽  
Author(s):  
Zakariya Yahya Algamal ◽  
Muhammad Hisyam Lee ◽  
Abdo M. Al-Fakih ◽  
Madzlan Aziz
Keyword(s):  
Logistic Regression ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Classification Model ◽  
High Dimensional ◽  
Thiourea Derivatives ◽  
Sparse Logistic Regression ◽  
Virus Activity

scholarly journals Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model

2021 ◽  
Vol 29 ◽  
pp. 287-295
Author(s):  
Zhiming Zhou ◽  
Haihui Huang ◽  
Yong Liang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gene Selection ◽  
Simulated Data ◽  
Biological Data ◽  
Cancer Classification ◽  
High Dimensional ◽  
Data Set ◽  
Biomarker Selection

BACKGROUND: In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable to fulfill biomarker selection. OBJECTIVE: The aim of this paper is to give the model efficient gene selection capability. METHODS: In this paper, we propose a new penalized logsum network-based regularization logistic regression model for gene selection and cancer classification. RESULTS: Experimental results on simulated data sets show that our method is effective in the analysis of high-dimensional data. For a large data set, the proposed method has achieved 89.66% (training) and 90.02% (testing) AUC performances, which are, on average, 5.17% (training) and 4.49% (testing) better than mainstream methods. CONCLUSIONS: The proposed method can be considered a promising tool for gene selection and cancer classification of high-dimensional biological data.

Quality Classification Model of Material Products Based on Ordinal Logistic Regression

2012 ◽  
Vol 460 ◽  
pp. 393-397 ◽  
Author(s):  
Peng Fei Mu ◽  
Dong Ling Zhang ◽  
Xiao Mei Xu ◽  
Yang Liu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Quality Evaluation ◽  
Ordinal Logistic Regression ◽  
Processing Method ◽  
Classification Model ◽  
Classification Result ◽  
Quality Classification ◽  
Ordinal Logistic Regression Model

It presents a proposed method for the development of quality evaluation and classification for material products, and shows the application of the ordinal logistic regression model and its advantages. It involved several steps: applying the linguistic information processing method, building the ordinal logistic regression model, differentiating and analyzing the quality evaluation to reach the quality classification result

scholarly journals 294. Hepatitis C Virologic Response in Hepatitis B and C Coinfected Persons Treated with Directly Acting Antiviral Agents: Results from ERCHIVES

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S159-S160
Author(s):  
Adeel A Butt ◽  
Peng Yan ◽  
Samia Aslam ◽  
Kenneth Sherman ◽  
Dawd Siraj ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Hepatitis C ◽  
Liver Fibrosis ◽  
Hepatitis B ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Antiviral Agents ◽  
Virologic Response ◽  
Hbv Infection ◽  
Factors Associated

Abstract Background There are scant data regarding hepatitis C (HCV) virologic response to directly acting antiviral agents (DAAs) in chronic hepatitis B (HBV) and HCV coinfected persons. HCV treatment response in those with spontaneously cleared HBV infection is unknown. Methods All HCV-infected persons treated with a DAA regimen in ERCHIVES were identified and categorized into HBV/HCV-coinfected (HBsAg, HBV DNA or both positive), HCV-monoinfected, and resolved HBV (isolated HBcAb+). SVR rates were determined and compared for all groups. A logistic regression model was used to determine factors associated with SVR. Results Among 115 HCV/HBV-coinfected, 38,570 HCV-monoinfected persons, and 13,096 persons with resolved HBV, 31.6% of HCV/HBV-coinfected, 24.6% of HCV-monoinfected and 26.4% with resolved HBV had cirrhosis at baseline. SVR was achieved in 90.4% of HCV/HBV-coinfected, 83.4% of HCV-monoinfected and 84.5% of those with resolved HBV infection (P = 0.04 HCV/HBV vs. HCV monoinfected). In a logistic regression model, those with HCV/HBV were more likely to achieve SVR compared with HCV monoinfected (OR 2.25, 95% CI 1.17, 4.31). For HCV/HBV coinfected, the SVR rates dropped numerically with increasing severity of liver fibrosis (P-value non-significant). Factors associated with a lower likelihood of attaining SVR included cirrhosis at baseline (OR 0.85, 95% CI 0.80, 0.92), diabetes (OR 0.93, 95% CI 0.87, 0.99) and higher pretreatment HCV RNA (OR 0.86, 95% CI 0.84, 0.87). Conclusion HBV/HCV-coinfected persons have higher overall SVR rates with newer DAA regimens. The virologic response is graded, with decreasing SVR rates with increasing degree of liver fibrosis as determined by the FIB-4 scores. Disclosures All authors: No reported disclosures.

scholarly journals CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

2021 ◽  
Vol 12 ◽  
Author(s):  
Osamah Alwalid ◽  
Xi Long ◽  
Mingfei Xie ◽  
Jiehua Yang ◽  
Chunyuan Cen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Intracranial Aneurysm ◽  
Regression Model ◽  
Ct Angiography ◽  
Logistic Regression Model ◽  
Aneurysm Rupture ◽  
Classification Model ◽  
High Morbidity ◽  
Unruptured Aneurysms

Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture.Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms.Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89–0.95] and 0.86 [95% CI: 0.80–0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. −1.60 and 2.35 vs. −1.01 on training and test cohorts, respectively, p < 0.001).Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.

scholarly journals Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bo Sun
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Machine Learning Algorithm ◽  
Music Classification ◽  
Music Signal

Music classification is conducive to online music retrieval, but the current music classification model finds it difficult to accurately identify various types of music, which makes the classification effect of the current music classification model poor. In order to improve the accuracy of music classification, a music classification model based on multifeature fusion and machine learning algorithm is proposed. First, we obtain the music signal, and then extract various features from the classification of the music signal, and use machine learning algorithms to describe the type of music signal and the relationship between the features. The music classifier and deep belief network machine learning models in shallow logistic regression are established, respectively. Experiments were designed for these two models to verify the applicability of the model for music classification. By comparing the experimental results, it is found that the classification accuracy of the deep confidence network model is higher than that of the logistic regression model, but the number of iterations needed for its accuracy to converge is also higher than that of the logistic regression model. Compared with other current music classification models, this model reduces the time of constructing music classifier, speeds up the speed of music classification, and can identify various types of music with high precision. The accuracy of music classification is obviously improved, which verifies the superiority of this music classification model.

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