Predicting prostate cancer progression with penalized logistic regression model based on co-expressed genes

2012 ◽  
Author(s):  
Hongya Zhao ◽  
Songru Qi ◽  
Qi Dong
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Cancer Progression ◽  
Logistic Regression Model ◽  
Prostate Cancer Progression ◽  
Model Based ◽  
Penalized Logistic Regression

An Optimized Logistic Regression Model Based on the Maximum Entropy Estimation Under the Hesitant Fuzzy Environment

2021 ◽  
pp. 1-25
Author(s):  
Chenyang Song ◽  
Liguo Wang ◽  
Zeshui Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Maximum Entropy ◽  
Logistic Regression Model ◽  
Uncertain Information ◽  
Entropy Estimation ◽  
Model Based ◽  
Fuzzy Environment ◽  
Maximum Entropy Estimation ◽  
Specific Implementation

The logistic regression model is one of the most widely used classification models. In some practical situations, few samples and massive uncertain information bring more challenges to the application of the traditional logistic regression. This paper takes advantages of the hesitant fuzzy set (HFS) in depicting uncertain information and develops the logistic regression model under hesitant fuzzy environment. Considering the complexity and uncertainty in the application of this logistic regression, the concept of hesitant fuzzy information flow (HFIF) and the correlation coefficient between HFSs are introduced to determine the main factors. In order to better manage situations with small samples, a new optimized method based on the maximum entropy estimation is also proposed to determine the parameters. Then the Levenberg–Marquardt Algorithm (LMA) under hesitant fuzzy environment is developed to solve the parameter estimation problem with fewer samples and uncertain information in the logistic regression model. A specific implementation process for the optimized logistic regression model based on the maximum entropy estimation under the hesitant fuzzy environment is also provided. Moreover, we apply the proposed model to the prediction problem of Emergency Extreme Air Pollution Event (EEAPE). A comparative analysis and a sensitivity analysis are further conducted to illustrate the advantages of the optimized logistic regression model under hesitant fuzzy environment.

scholarly journals A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis

2009 ◽  
Vol 192 (4) ◽  
pp. 1117-1127 ◽  
Author(s):  
Jagpreet Chhatwal ◽  
Oguzhan Alagoz ◽  
Mary J. Lindstrom ◽  
Charles E. Kahn ◽  
Katherine A. Shaffer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Cancer Diagnosis ◽  
Logistic Regression Model ◽  
Breast Cancer Diagnosis ◽  
Model Based

Evaluation of a Multivariate Prostate-Specific Antigen and Percentage of Free Prostate-Specific Antigen Logistic Regression Model in the Diagnosis of Prostate Cancer

Tumor Biology ◽  
1999 ◽  
Vol 20 (6) ◽  
pp. 312-318 ◽  
Author(s):  
Xavier Filella ◽  
Juan Alcover ◽  
Llorenç Quintó ◽  
Rafael Molina ◽  
Xavier Bosch-Capblanch ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Prostate Specific Antigen ◽  
Logistic Regression Model ◽  
Specific Antigen

scholarly journals LogSum + L2 penalized logistic regression model for biomarker selection and cancer classification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiao-Ying Liu ◽  
Sheng-Bing Wu ◽  
Wen-Quan Zeng ◽  
Zhan-Jiang Yuan ◽  
Hong-Bo Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cancer Classification ◽  
Classification Problems ◽  
Proposed Model ◽  
Different Types ◽  
Penalized Logistic Regression ◽  
Biomarker Selection

AbstractBiomarker selection and cancer classification play an important role in knowledge discovery using genomic data. Successful identification of gene biomarkers and biological pathways can significantly improve the accuracy of diagnosis and help machine learning models have better performance on classification of different types of cancer. In this paper, we proposed a LogSum + L2 penalized logistic regression model, and furthermore used a coordinate decent algorithm to solve it. The results of simulations and real experiments indicate that the proposed method is highly competitive among several state-of-the-art methods. Our proposed model achieves the excellent performance in group feature selection and classification problems.

scholarly journals Seroprevalence of Herpes Simplex Virus Type 2 and Human Herpesvirus Type 8 among Prostate Cancer Patients Compared to Cancer-Free Men

2021 ◽  
Vol 14 (4) ◽  
Author(s):  
Solmaz Ohadian Moghadam ◽  
Kamyar Mansori ◽  
Mohammad Reza Nowroozi ◽  
Davoud Afshar ◽  
Ali Nowroozi
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Human Herpesvirus ◽  
Multivariate Logistic Regression Model ◽  
Statistical Association ◽  
Multivariate Logistic Regression ◽  
Simplex Virus ◽  
Significant Statistical Association

Background: As one of the most prevalent cancers in men, prostate cancer is a condition with multiple causes. Viral infections have been identified as one of the major sources of elevated incidence of prostate cancer. Objectives: The purpose of this research was to assess the association of the risk of prostate cancer and its aggressiveness with seropositivity of herpes simplex virus 2 (HSV-2) and/or human herpesvirus 8 (HHV-8). Methods: Totally, 103 men with prostate cancer as cases and 81 healthy individuals as controls were included in this case-control analysis and provided a serum sample. The specific IgG antibodies against HSV-2 and HHV-8 were screened by enzyme-linked immunosorbent assay (ELISA). To determine the association between HSV-2, HHV-8, prostate-specific antigen (PSA) level, and demographic variables with incidence of prostate cancer, univariate and multivariate logistic regression models were applied. Results: The results of the univariate logistic regression model showed a statistically significant association between HSV-2 and HHV-8 seropositivity, PSA level, age, and smoking with prostate cancer incidence (P ≤ 0.20). The multivariate logistic regression model results after adjusting for the potential confounding variables showed a significant statistical association between the mean of PSA level [adjusted odds ratio (OR): 3.44; 95% CI: 2.15 - 5.51; P < 0.001) and incidence of prostate cancer. Moreover, the results of univariate and multivariate logistic regression model showed a significant statistical association between age [adjusted OR: 0.88; 95% confidence interval (CI): 0.81 - 0.95; P = 0.001] and HSV-2 and also significant statistical association was found between PSA (adjusted OR: 1.02; 95% CI: 1.005 - 1.03; P = 0.006) and HHV-8. Conclusions: Although the seroprevalence of HSV-2 and HHV-8 was higher in patients with prostate cancer than in the control group, it cannot be concluded that there is a significant association between the seropositivity of these viruses and prostate cancer incidence. However, the findings showed a significant statistical association between age and seropositivity of HSV-2 and also a significant statistical association between PSA levels and seropositivity of HHV-8.

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