scholarly journals Multiple Linear Regression Analysis of lncRNA–Disease Association Prediction Based on Clinical Prognosis Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bo Wang ◽  
Jing Zhang
Keyword(s):  
Prostate Cancer ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Cancer Survival ◽  
Disease Association ◽  
The Body ◽  
Survival Prediction ◽  
Clinical Prognosis ◽  
Disease Associations ◽  
Auc Value

Long noncoding RNAs (lncRNAs) have an important role in various life processes of the body, especially cancer. The analysis of disease prognosis is ignored in current prediction on lncRNA–disease associations. In this study, a multiple linear regression model was constructed for lncRNA–disease association prediction based on clinical prognosis data (MlrLDAcp), which integrated the cancer data of clinical prognosis and the expression quantity of lncRNA transcript. MlrLDAcp could realize not only cancer survival prediction but also lncRNA–disease association prediction. Ultimately, 60 lncRNAs most closely related to prostate cancer survival were selected from 481 alternative lncRNAs. Then, the multiple linear regression relationship between the prognosis survival of 176 patients with prostate cancer and 60 lncRNAs was also given. Compared with previous studies, MlrLDAcp had a predominant survival predictive ability and could effectively predict lncRNA–disease associations. MlrLDAcp had an area under the curve (AUC) value of 0.875 for survival prediction and an AUC value of 0.872 for lncRNA–disease association prediction. It could be an effective biological method for biomedical research.

scholarly journals DNILMF-LDA: Prediction of lncRNA-Disease Associations by Dual-Network Integrated Logistic Matrix Factorization and Bayesian Optimization

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 608 ◽  
Author(s):  
Yan Li ◽  
Junyi Li ◽  
Naizheng Bian
Keyword(s):  
Matrix Factorization ◽  
Disease Diagnosis ◽  
Disease Association ◽  
Bayesian Optimization ◽  
Model Parameters ◽  
Target Interaction ◽  
Disease Associations ◽  
Auc Value ◽  
Similarity Networks ◽  
Fold Cross Validation

Identifying associations between lncRNAs and diseases can help understand disease-related lncRNAs and facilitate disease diagnosis and treatment. The dual-network integrated logistic matrix factorization (DNILMF) model has been used for drug–target interaction prediction, and good results have been achieved. We firstly applied DNILMF to lncRNA–disease association prediction (DNILMF-LDA). We combined different similarity kernel matrices of lncRNAs and diseases by using nonlinear fusion to extract the most important information in fused matrices. Then, lncRNA–disease association networks and similarity networks were built simultaneously. Finally, the Gaussian process mutual information (GP-MI) algorithm of Bayesian optimization was adopted to optimize the model parameters. The 10-fold cross-validation result showed that the area under receiving operating characteristic (ROC) curve (AUC) value of DNILMF-LDA was 0.9202, and the area under precision-recall (PR) curve (AUPR) was 0.5610. Compared with LRLSLDA, SIMCLDA, BiwalkLDA, and TPGLDA, the AUC value of our method increased by 38.81%, 13.07%, 8.35%, and 6.75%, respectively. The AUPR value of our method increased by 52.66%, 40.05%, 37.01%, and 44.25%. These results indicate that DNILMF-LDA is an effective method for predicting the associations between lncRNAs and diseases.

scholarly journals The Effect of Automotive Seat Cushion Stiffness Distribution on the Subjective Comfort

2019 ◽  
Vol 8 (2S6) ◽  
pp. 336-341
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
The Body ◽  
Strong Positive Correlation ◽  
Comfort Evaluation ◽  
Selection Of Variables ◽  
Stiffness Distribution

When body pressures are concentrated, sense of fatigue is increased. To confirm this, correlation analysis between the difference in stiffness of seat and comfort using multiple linear regression analysis has been conducted. For the selected three types of seats which are small-, mid-, and large-size seats, respectively, static tests were con-ducted to measure the distribution of the subject's body pressure on the cushion, through which local stiffness distribution were derived. Also, a subjective comfort evaluation was conducted, and analyzed. According to the present analysis results, the correlation coefficients between stiff-ness of hip area and comfort of hip area were observed to be 0.713 and 0.789, respectively, indicating a strong positive correlation. Thus, the comfort of seat perceived by the driver could be seen to have the largest linear correlation with the stiffness of hip area. Selection of variables for the multiple linear regression analysis was implemented by a backward removal method. Differences of stiffness by areas were selected as independent variables, and subjective comfort evaluation results were selected as dependent variables. According to multiple regression analysis, the comfort of the cushion increased when the left and right balance of the stiffness distribution was maintained even if the body pressure distribution of the hip area was concentrated on one side. According to the analysis results, the stiffness of hip area could be seen to have the greatest linear relationship with the overall satisfaction of comfort, in which comfort is planned to be confirmed by actual production of seats

scholarly journals The Effect of Smoking and Respiration of Carbon Monoxide among Active Smokers in Palembang, Indonesia

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Rico Januar Sitorus ◽  
Imelda Gernauli Purba ◽  
Merry Natalia ◽  
Kraichat Tantrakarnapa
Keyword(s):  
Carbon Monoxide ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
The Body ◽  
Cross Sectional ◽  
Independent Variables ◽  
Cross Sectional Design

Smoking leads to disease and disability as well as harm nearly every organ of the body. Furthermore, smoking of tobacco is known to cause pulmonary dysfunction and lead to complications, pain, or even death. This study aims to measure the risk factors for the respiration of carbon monoxide among smokers. A cross-sectional design was implemented by involving 156 smokers in Karyajaya Subdistrict, Palembang City. The dependent variable was carbon monoxide levels (ppm), while the independent variables were smoking frequency, duration, and the last period of smoking. The carbon monoxide levels (ppm) measured with a PiCO + Smokerlyzer® device from Bedfont Scientific Limited were the research tool and the independent variables of the questionnaire. The pearson Correlation and multiple linear regression were used for the analysis. The results of the multiple linear regression analysis revealed that smoking duration assessment is related to carbon monoxide respiration after controlling smoking frequency, the last period of smoking, and the distance of residence to sources of exposure. The smoker's respiration of carbon monoxide level was 72.5% influenced by the duration, frequency, last period of smoking, and distance of residence to sources of exposure. Reducing the frequency of smoking and stopping may prevent and control carbon monoxide respiration.

scholarly journals Influence Of Number Of Pregnancies In Peak Expiratory Flow And Body Composition Of Pregnant Women

10.3823/2474 ◽  
2017 ◽  
Vol 10 ◽  
Author(s):  
Andrea Carla Brandao da Costa Santos ◽  
Alex Sandro Rolland De Souza ◽  
Jousilene De Sales Tavares ◽  
Maria Elma De Souza Maciel Soares ◽  
Marcos Antonio De Araújo Leite Filho ◽  
...  
Keyword(s):  
Body Composition ◽  
Linear Regression ◽  
Pregnant Women ◽  
Multiple Linear Regression ◽  
Fat Mass ◽  
Peak Expiratory Flow ◽  
Maternal Age ◽  
The Body ◽  
Independent Variables ◽  
Current Weight

Objectives: to describe and compare the mean values of the body composition and the peak expiratory flow (PEF) in primigravidae and multigravidae and, to determine its correlation with obstetric, anthropometric and body composition variables. Method: it was performed a cross-sectional study of 120 healthy pregnant women at low risk, including 77 primigravidae and 43 multigravidae. The PEF was measured by spirometry and the body composition by multisegmental electrical impedance. The unpaired t test was used to compare the groups and the Pearson correlation test was used to determine the association between PEF and independent variables. A multiple linear regression was used to estimate the relationship between the dependent variable, the PEF and the independent variables. Results: the body composition variables in multigravidae women showed higher values compared to the primigravidae, being statistically significant, except for fat mass. In primigravidae, the PEF was correlated significantly with maternal age and height. In multigravidae, the PEF was correlated with maternal age, height, pre-pregnancy and current weight, total body water, extracellular water, fat mass, lean mass and fat-free mass. A Multiple linear regression analysis showed that, in primigravidae, height and maternal age were associated with PEF, being responsible for explaining 14.5% of its variability. The current weight and the maternal age explained 42.3% of peak flow variability in multigravidae. Conclusion: The PEF seemed to be influenced by the number of pregnancies. Changes were observed in relation to the body composition, as it was evidenced in correlation with the PEF in multigravidae women. Keywords: Pregnancy. Spirometry. Weight gain.

scholarly journals Expression and correlation of circulating miRNAs in healthy obese children

2021 ◽  
Author(s):  
Feifei Ma ◽  
Dingding Cao ◽  
Zhuo Liu ◽  
Yuanyuan Li ◽  
Shengrong Ouyang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Mirna Expression ◽  
Target Genes ◽  
Linear Regression Analysis ◽  
The Body ◽  
Circulating Mirnas ◽  
Obese Children ◽  
Susceptible Population ◽  
Taqman Qpcr

Abstract Background Obesity is an important risk factor of metabolic diseases. It is caused by the interaction of genetic, epigenetic, and environmental factors. This study aimed to identify specific circulating miRNAs for evaluating obesity in children. Methods Thirty children including 15 obese and 15 extremely thin were selected. The miRNA microarray was used to detect the expression of miRNAs in circulating plasma. The reliability of differential miRNA expression was verified using TaqMan qPCR. The correlation between miRNA and obesity was analyzed using multiple linear regression. Target genes for selected miRNAs were analyzed using informatics tools, and a functional network map was constructed. Results Thirty-six differential expression miRNAs were screened by gene chip, and seven up-regulated miRNAs were verified by TaqMan qPCR, including hsa-miR-126-3p, hsa-miR-15b-5p, hsa-miR-199a-3p, hsa-miR-20a-5p, hsa-miR-223-3p, hsa-miR-23a-3p, and hsa-miR-24-3p. Six miRNAs had significant statistical difference except hsa-miR-23a-3p. Multiple linear regression analysis showed that hsa-miR-15b-5p and hsa-miR-223-3p were associated with obesity [1.649 (4.974–16.084), -1.175 (-17.852~ -2.657)]. After adjusting for age and gender, these two miRNAs were still associated with obesity [1.400 (3.572–14.301), -0.973 (-15.634~ -1.303)]. Among them, hsa-miR-15b-5p and hsa-miR-223 had more predicted obesity-related target genes than others. In particular, hsa-miR-15b-5p had numerous target genes associated with the FoxO, insulin, Ras, and AMPK signaling pathways. Conclusions The miRNA expression profile in the body circulation of obese children differs from normal children. This result is attributed to the abnormal metabolism of obese children. hsa-miR-15b-5p and hsa-miR-223-3p could serve as a molecular marker for screening obese children and susceptible population of metabolic syndrome.

scholarly journals Prediction of Phenytoin Dosage in Epilepsy Using Multiple Linear Regression

1982 ◽  
Vol 19 (3) ◽  
pp. 141-144 ◽  
Author(s):  
S P Gray
Keyword(s):  
Body Weight ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Therapeutic Range ◽  
Drug Dosage ◽  
The Body ◽  
Clinical Value ◽  
Number Of Patients ◽  
The Relationship

Analysis of plasma phenytoin in a group of patients treated for epilepsy showed that only 36% had values in the therapeutic range. The relationship between plasma phenytoin, body weight, and daily dosage of the drug were explored, and the data were analysed by multiple regression. The resultant equation, relating all three factors, was used to optimise drug dosage, and the importance of using the body weight of the patient before starting a phenytoin regimen is emphasised. An increase in the number of patients with plasma phenytoin in the therapeutic range was achieved, and the clinical value of being in that range is shown.

scholarly journals Expression and Correlation of Circulating miRNAs in Healthy Obese Children

2021 ◽  
Author(s):  
Feifei Ma ◽  
Dingding Cao ◽  
Zhuo Liu ◽  
Yuanyuan Li ◽  
Shengrong Ouyang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Mirna Expression ◽  
Target Genes ◽  
Linear Regression Analysis ◽  
The Body ◽  
Circulating Mirnas ◽  
Obese Children ◽  
Susceptible Population ◽  
Taqman Qpcr

Abstract Objective: Obesity is an important risk factor of metabolic diseases. It is caused by the interaction of genetic, epigenetic, and environmental factors. This study aimed to identify specific circulating miRNAs for evaluating obesity in children. Methods: Thirty children including 15 obese and 15 extremely thin were selected. The miRNA microarray was used to detect the expression of miRNAs in circulating plasma. The reliability of differential miRNA expression was verified using TaqMan qPCR. The correlation between miRNA and obesity was analyzed using multiple linear regression. Target genes for selected miRNAs were analyzed using informatics tools, and a functional network map was constructed. Results: Thirty-six differential expression miRNAs were screened by gene chip, and seven up-regulated miRNAs were verified by TaqMan qPCR, including hsa-miR-126-3p, hsa-miR-15b-5p, hsa-miR-199a-3p, hsa-miR-20a-5p, hsa-miR-223-3p, hsa-miR-23a-3p, and hsa-miR-24-3p. Six miRNAs had significant statistical difference except hsa-miR-23a-3p. Multiple linear regression analysis showed that hsa-miR-15b-5p and hsa-miR-223-3p were associated with obesity [1.649 (4.974-16.084), -1.175 (-17.852~ -2.657)]. After adjusting for age and gender, these two miRNAs were still associated with obesity [1.400 (3.572-14.301), -0.973 (-15.634~ -1.303)]. Among them, hsa-miR-15b-5p and hsa-miR-223 had more predicted obesity-related target genes than others. In particular, hsa-miR-15b-5p had numerous target genes associated with the FoxO, insulin, Ras, and AMPK signaling pathways. Conclusion: The miRNA expression profile in the body circulation of obese children differs from normal children. This result is attributed to the abnormal metabolism of obese children. hsa-miR-15b-5p and hsa-miR-223-3p could serve as a molecular marker for screening obese children and susceptible population of metabolic syndrome.

scholarly journals Multiple Linear Regression Model for Estimating the Price of a Housing Unit

2017 ◽  
Vol 17 (2) ◽  
pp. 66-77
Author(s):  
P. Boye ◽  
D. Mireku-Gyimah ◽  
C. A. Okpoti
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
The Body ◽  
Housing Unit ◽  
Unit Price ◽  
Data Matrix ◽  
Housing Units

This paper uses the respective unit costs, over fifteen (15) years, of selected Housing Unit Major Components (HUMC): cement, iron rods, aluzinc roofing sheets, coral paint, wood and sand, to develop Multiple Linear Regression Model (MLRM) for determining Housing Unit Price (HUP) for one-bedroom and two-bedroom housing units. In the modeling, the Ordinary Least Squares (OLS) normality assumption which could introduce errors in the statistical analyses was dealt with by log transformation of the data, ensuring the data is normally distributed and there is no correlation between them. Minimisation of Sum of Squares Error method was used to derive the model coefficients. The resultant MLRM is:  Ŷi MLRM = (X'X)-1 X'Y(xi') where X is the sample data matrix. The specific model for one-bedroom housing unit is loge (HUPMLRM)1-Bed = 1.017 – 2.225 x 10-5 x CC + 2.512 x 10-6 x CS + 6.016 x 10-4 x CIR  +  1.985 x  10-4 x CR + 5.694 x 10-4 x CP -7.437 x 10-4 x CW and that for two-bedroom housing unit is loge (HUPMLRM)2-Bed = 5.760 – 7.501 x 10-7 x CC + 2.935 x 10-6 x CS + 1.898 x 10-3 x CIR  +  6.695 x 10-4 x CR - 9.157 x 10-3 x CP +6.136 x 10-3 x CW, where CC, CS, CIR, CR, CP and CW are costs of the total quantity of cement, sand, iron rods, roofing, paint and wood respectively. The MLRM was validated by using it to estimate the known HUP in the 15.5th year. From the results, the percentage absolute deviations of the estimated HUP from the known HUP are 1.27% and 2.02% for one-bedroom and two-bedroom housing units respectively, which are satisfactory. The novel approach presented in this paper is a valuable contribution to the body of knowledge in modeling. Keywords: Multiple Regression Analysis, Housing Unit Major Components, Housing Unit Price

scholarly journals DEMLP: DeepWalk Embedding in MLP for miRNA-Disease Association Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xun Wang ◽  
Fuyu Wang ◽  
Xinzeng Wang ◽  
Sibo Qiao ◽  
Yu Zhuang
Keyword(s):  
Multilayer Perceptron ◽  
High Efficiency ◽  
Interaction Network ◽  
Disease Association ◽  
Biological Processes ◽  
Disease Network ◽  
Graph Node ◽  
Disease Associations ◽  
Auc Value ◽  
Low Dimensional

miRNAs significantly affect multifarious biological processes involving human disease. Biological experiments always need enormous financial support and time cost. Taking expense and difficulty into consideration, to predict the potential miRNA-disease associations, a lot of high-efficiency computational methods by computer have been developed, based on a network generated by miRNA-disease association dataset. However, there exist many challenges. Firstly, the association between miRNAs and diseases is intricate. These methods should consider the influence of the neighborhoods of each node from the network. Secondly, how to measure whether there is an association between two nodes of the network is also an important problem. In our study, we innovatively integrate graph node embedding with a multilayer perceptron and propose a method DEMLP. To begin with, we construct a miRNA-disease network by miRNA-disease adjacency matrix (MDA). Then, low-dimensional embedding representation vectors of nodes are learned from the miRNA-disease network by DeepWalk. Finally, we use these low-dimensional embedding representation vectors as input to train the multilayer perceptron. Experiments show that our proposed method that only utilized the miRNA–disease association information can effectively predict miRNA-disease associations. To evaluate the effectiveness of DEMLP in a miRNA-disease network from HMDD v3.2, we apply fivefold crossvalidation in our study. The ROC-AUC computed result value of DEMLP is 0.943, and the PR-AUC value of DEMLP is 0.937. Compared with other state-of-the-art methods, our method shows good performance using only the miRNA-disease interaction network.

Body mass index of women in Bangladesh: comparing Multiple Linear Regression and Quantile Regression

2020 ◽  
pp. 1-19 ◽  
Author(s):  
Sorif Hossain ◽  
Raaj Kishore Biswas ◽  
Md Amir Hossain
Keyword(s):  
Body Mass Index ◽  
Linear Regression ◽  
Quantile Regression ◽  
Multiple Linear Regression ◽  
Body Mass ◽  
Demographic Factors ◽  
Educational Status ◽  
The Body ◽  
Reproductive Age ◽  
Socio Demographic Factors

Abstract This study explored the association between socio-demographic factors and the body mass index (BMI) of women of reproductive age (15–49 years) in Bangladesh. Data from the 2014 Bangladesh Demographic and Health Survey (BDHS-14) were analysed using Multiple Linear Regression (MLR) and Quantile Regression (QR) analyses. The study sample comprised 15,636 non-pregnant women aged 15–49. The mean BMI of the women was 22.35±4.12 kg/m2. Over half (56.75%) had a BMI in the normal range (18<BMI<25 kg/m2), and 18.50%, 20.00% and 4.75% were underweight (BMI≤18 kg/m2), overweight (25≤BMI<30 kg/m2) and obese (BMI≥30 kg/m2), respectively. The results of the MLR found that age, wealth index, urban/rural place of residence, geographical division, womenʼs educational status, husbandʼs educational status, womenʼs working status and total number of children ever born were significantly (p<0.001) associated with respondents’ mean BMI. The QR results showed different associations between socio-demographic factors and mean BMI, as well as a different conditional distribution of mean BMI. Overall, the results indicated that women with uneducated husbands, with little or no education and from less-affluent households from rural areas tended to be more underweight compared with women in other groups. The inter-relationship between the study womenʼs mean BMI and associated socio-demographic factors was assessed using QR analysis to identify the most vulnerable cohorts of women in Bangladesh.

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