Variable selection in Logistic regression model with genetic algorithm

Zhang, Zhongheng; Treviño, Víctor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, A. Manivanna; Zhang, Ping; Gorunescu, Florin; Velappan, Subha; Dai, Songshi

doi:10.21037/atm.2018.01.15

Cited by 31 publications

(11 citation statements)

References 13 publications

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“…Finally, the combination sets of miRNAs target prediction were solely based on TargetScan database. Thus, the use of efficient genetic algorithms for variable selection in logistic regression model, rather than a linear model, may have advantages to select the clinically optimal markers among all extracted data sets, more accurately 39 .…”

Section: Discussionmentioning

confidence: 99%

miRNA Expression Profiles and Potential as Biomarkers in Nontuberculous Mycobacterial Pulmonary Disease

Han

Jhun

Kim

et al. 2020

Sci Rep

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Pulmonary disease (PD) due to nontuberculous mycobacteria (NTM) is increasing globally, but specific biomarkers for NTM-PD have not been established. As circulating miRNAs are promising biomarkers for various diseases, we investigated whether miRNAs have potential as NTM-PD biomarkers. Sera from 12 NTM-PD patients due to Mycobacterium avium, M. intracellulare, M. abscessus, or M. massiliense and three healthy controls were initially evaluated via small RNA sequencing. Multiple miRNAs showed significant differences in expression in patients compared to in healthy controls, with some expression differences unique to PD caused by a specific mycobacterial species. Notably, 14 miRNAs exhibited significant expression differences in PD associated with all four mycobacteria. Validation by quantitative reverse-transcription-PCR in an additional 40 patients with NTM-PD and 40 healthy controls confirmed that four differentially expressed miRNAs (hsa-miR-484, hsa-miR-584-5p, hsa-miR-625-3p, and hsa-miR-4732-5p) showed significantly higher serum expressions in NTM-PD patients than in controls. Receiver operating characteristic curve analysis of these four miRNAs supported the discriminative potential for NTM-PD and their combination provided an improved diagnostic value for NTM-PD. Furthermore, bioinformatics analysis revealed their 125 target genes, which were mostly associated with immune responses. Collectively, this study identified four miRNAs as potential biomarkers for NTM-PD and provided insight into NTM-PD pathophysiology.Nontuberculous mycobacteria (NTM) are mycobacteria other than the Mycobacterium tuberculosis complex and Mycobacterium leprae. More than 180 officially recognized NTM species have been identified, with the most frequent human pathogens associated with pulmonary disease (PD) due to NTM belonging to the Mycobacterium avium complex (MAC), followed by M. abscessus (MAB) 1,2 . MAC primarily comprises M. avium and M. intracellulare, and MAB primarily comprises M. abscessus subspecies abscessus (hereafter referred to as M. abscessus) and M. abscessus subspecies massiliense (hereafter referred to as M. massiliense) 1,2 . Despite the global increase in the burden of NTM-PD in recent decades, there is limited information regarding specific biomarkers for distinguishing patients with NTM-PD from healthy individuals or studies of the disease pathophysiology 3,4 .MicroRNAs (miRNAs) are small, 18-25-nucleotide, endogenous, stable, highly conserved noncoding RNAs with important functions in post-transcriptional gene regulation under both physiological and pathological conditions 5,6 . Numerous studies have reported the aberrant expression of several miRNAs in various conditions, including cancer and infectious diseases. miRNAs reportedly play an important regulatory role in the pathogenesis or progression of infectious diseases 7,8 and serve as potential modulators of innate and adaptive immune responses 9,10 . Serum miRNAs are not readily degraded by enzymes, unaffected by changes in temperature and time, and res...

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Section: Discussionmentioning

confidence: 99%

miRNA Expression Profiles and Potential as Biomarkers in Nontuberculous Mycobacterial Pulmonary Disease

Han

Jhun

Kim

et al. 2020

Sci Rep

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“…The authors used the generation interval, defined as the time needed for an infected person to infect another person and for reproduction rate estimation. The works by Zhang et al ( 35 ) and by Srinivasu et al ( 36 ), Panigrahi et al ( 37 , 38 ), Tamang ( 39 ), Chowdhary et al ( 40 ), and Gaur et al ( 41 ) demonstrated the efficacy of machine learning algorithms in various fields.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate

Kaliappan

Srinivasan

Qaisar

et al. 2021

Front. Public Health

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This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to significant factors, such as testing, death, positivity rate, active cases, stringency index, and population density are considered for the COVID-19 reproduction rate prediction. These 16 features are ranked using Random Forest, Gradient Boosting, and XGBOOST feature selection algorithms. Seven features are selected from the 16 features according to the ranks assigned by most of the above mentioned feature-selection algorithms. Predictions by historical statistical models are based solely on the predicted feature and the assumption that future instances resemble past occurrences. However, techniques, such as Random Forest, XGBOOST, Gradient Boosting, KNN, and SVR considered the influence of other significant features for predicting the result. The performance of reproduction rate prediction is measured by mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R-Squared, relative absolute error (RAE), and root relative squared error (RRSE) metrics. The performances of algorithms with and without feature selection are similar, but a remarkable difference is seen with hyperparameter tuning. The results suggest that the reproduction rate is highly dependent on many features, and the prediction should not be based solely upon past values. In the case without hyperparameter tuning, the minimum value of RAE is 0.117315935 with feature selection and 0.0968989 without feature selection, respectively. The KNN attains a low MAE value of 0.0008 and performs well without feature selection and with hyperparameter tuning. The results show that predictions performed using all features and hyperparameter tuning is more accurate than predictions performed using selected features.

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“…Depth from Focus (DFF) [14][15][16] method is an autofocusing method based on focusing search mechanism. DFF method uses a certain definition evaluation function to calculate the definition evaluation function values (evaluation values) of images with different defocusing degrees.…”

Section: Depth From Focusmentioning

confidence: 99%

Automatic Focusing Method of Microscopes Based on Image Processing

Zhang

Yao

2021

Mathematical Problems in Engineering

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Microscope vision analysis is applied in many fields. The traditional way is to use the human eye to observe and manually focus to obtain the image of the observed object. However, with the observation object becoming more and more subtle, the magnification of the microscope is required to be larger and larger. The method of manual focusing cannot guarantee the best focusing position of the microscope in use. Therefore, in this paper, we are studying the existing autofocusing technology and the autofocusing method of microscope based on image processing, which is different from the traditional manual focusing method. The autofocusing method of microscope based on image processing does not need the information such as the target position and the focal length of optical system, to directly focus the collected image. First of all, in order to solve the problem of large computation and difficult real time of traditional wavelet based image sharpness evaluation algorithm, this paper proposes an improved wavelet based image sharpness evaluation algorithm; secondly, in view of the situation that the window selected by traditional focusing window selection method is fixed, this paper adopts an adaptive focusing window selection method to increase the focusing window. Finally, this paper studies the extremum search strategy. In order to avoid the interference of the local extremum in the focusing curve, this paper proposes an improved hill-climbing algorithm to achieve the accuracy of focusing search. The simulation results show that the improved wavelet transform image definition evaluation algorithm can improve the definition evaluation performance, and the improved mountain climbing algorithm can reduce the impact of local extremum and improve the accuracy of the search algorithm. All in all, it can be concluded that the method based on image processing proposed in this paper has a good focusing effect, which can meet the needs of anti-interference and extreme value search of microscope autofocus.

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Variable selection in Logistic regression model with genetic algorithm

Cited by 31 publications

References 13 publications

miRNA Expression Profiles and Potential as Biomarkers in Nontuberculous Mycobacterial Pulmonary Disease

miRNA Expression Profiles and Potential as Biomarkers in Nontuberculous Mycobacterial Pulmonary Disease

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate

Automatic Focusing Method of Microscopes Based on Image Processing

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