Near‐infrared optical spectroscopy for pancreas shrinkage estimation with multi synchrosqueezing transform and multivariate regression model

2021 ◽  
Author(s):  
Indra Priyadharshini S. ◽  
Vigilson Prem M. ◽  
Suresh K.
Keyword(s):  
Regression Model ◽  
Optical Spectroscopy ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Shrinkage Estimation ◽  
Multivariate Regression Model ◽  
Synchrosqueezing Transform

scholarly journals L2,1-norm regularized multivariate regression model with applications to genomic prediction

2021 ◽  
Author(s):  
Alain J Mbebi ◽  
Hao Tong ◽  
Zoran Nikoloski
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Multivariate Regression ◽  
Statistical Testing ◽  
Supplementary Information ◽  
Nucleotide Polymorphisms ◽  
Multiple Traits ◽  
Multivariate Regression Model ◽  
Proposed Model ◽  
Iterative Optimization Algorithm

AbstractMotivationGenomic selection (GS) is currently deemed the most effective approach to speed up breeding of agricultural varieties. It has been recognized that consideration of multiple traits in GS can improve accuracy of prediction for traits of low heritability. However, since GS forgoes statistical testing with the idea of improving predictions, it does not facilitate mechanistic understanding of the contribution of particular single nucleotide polymorphisms (SNP).ResultsHere, we propose a L2,1-norm regularized multivariate regression model and devise a fast and efficient iterative optimization algorithm, called L2,1-joint, applicable in multi-trait GS. The usage of the L2,1-norm facilitates variable selection in a penalized multivariate regression that considers the relation between individuals, when the number of SNPs is much larger than the number of individuals. The capacity for variable selection allows us to define master regulators that can be used in a multi-trait GS setting to dissect the genetic architecture of the analyzed traits. Our comparative analyses demonstrate that the proposed model is a favorable candidate compared to existing state-of-the-art approaches. Prediction and variable selection with datasets from Brassica napus, wheat and Arabidopsis thaliana diversity panels are conducted to further showcase the performance of the proposed model.Availability and implementation: The model is implemented using R programming language and the code is freely available from https://github.com/alainmbebi/L21-norm-GS.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Rectal Staphylococcus aureus Carriage and Recurrence After Endoscopic Sinus Surgery for Chronic Rhinosinusitis With Nasal Polyps: A Prospective Cohort Study

2021 ◽  
pp. 014556132110197
Author(s):  
Yue Peng ◽  
Zhao Liu ◽  
Zhijian Yu ◽  
Aiwu Lu ◽  
Tao Zhang
Keyword(s):  
Staphylococcus Aureus ◽  
Cohort Study ◽  
Regression Model ◽  
Chronic Rhinosinusitis ◽  
Endoscopic Sinus Surgery ◽  
Prospective Cohort ◽  
Multivariate Regression ◽  
Nasal Polyps ◽  
Sinus Surgery ◽  
Multivariate Regression Model

Objective: Chronic rhinosinusitis with nasal polyps (CRSwNPs) remains a major challenge due to its high recurrence rate after endoscopic sinus surgery (ESS). We aimed to investigate the risk factors of recurrence among patients who underwent ESS for Chronic rhinosinusitis (CRS). Methods: Prospective cohort study including 391 cases in a single institution receiving ESS were included for analysis from 2014 and 2017. Baseline characteristics including rectal Staphylococcus aureus ( S aureus) carriage in patients receiving ESS for CRSwNPs. The primary outcome was the recurrence of CRSwNPs. Multivariate regression model was established to identify independently predictive factors for recurrence. Results: Overall, 142 (36.3%) cases with recurrence within 2 years after ESS were observed in this study. After variable selection, multivariate regression model consisted of 4 variables including asthma (odds ratio [OR] = 3.41; P < .001), nonsteroidal anti-inflammatory drug allergy (OR = 2.27; P = .005), previous ESS (OR = 3.64; P < .001), and preoperative carriage of S aureus in rectum (OR = 2.34; P = .001). Conclusions: Based on our results, surgeons could predict certain groups of patients who are at high risk for recurrence after ESS. Rectal carriage of S aureus is more statistically related to the recurrence of CRSwNP after ESS compared with skin and nasal carriage.

Comparing predictive performance of general regression neural network (GRNN) and hedonic regression model for factors affecting housing prices in “Pune-India”

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhijat Arun Abhyankar ◽  
Harish Kumar Singla
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Multivariate Regression ◽  
Housing Prices ◽  
Predictive Performance ◽  
General Regression Neural Network ◽  
Multivariate Regression Model ◽  
Data Set ◽  
Content Type ◽  
General Regression

Purpose The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.” Design/methodology/approach Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016). Findings While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%). Research limitations/implications The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices. Practical implications The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence. Originality/value To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

scholarly journals A Novel Nomogram for Individualized Gonadotropin Starting Dose in GnRH Antagonist Protocol

2021 ◽  
Vol 12 ◽  
Author(s):  
Yubin Li ◽  
Yuwei Duan ◽  
Xi Yuan ◽  
Bing Cai ◽  
Yanwen Xu ◽  
...  
Keyword(s):  
Regression Model ◽  
Ovarian Stimulation ◽  
Multivariate Regression ◽  
Gnrh Antagonist ◽  
Bootstrap Resampling ◽  
Multivariate Regression Model ◽  
Starting Dose ◽  
Gnrh Antagonist Protocol ◽  
Antagonist Protocol

Controlled ovarian stimulation (COS) is one of the most vital parts of in vitro fertilization-embryo transfer (IVF-ET). At present, no matter what kinds of COS protocols are used, clinicians have to face the challenge of selection of gonadotropin starting dose. Although several nomograms have been developed to calculate the appropriate gonadotropin starting dose in gonadotropin releasing hormone (GnRH) agonist protocol, no nomogram was suitable for GnRH antagonist protocol. This study aimed to develop a predictive nomogram for individualized gonadotropin starting dose in GnRH antagonist protocol. Single-center prospective cohort study was conducted, with 198 women aged 20-45 years underwent IVF/intracytoplasmic sperm injection (ICSI)-ET cycles. Blood samples were collected on the second day of the menstrual cycle. All women received ovarian stimulation using GnRH antagonist protocol. Univariate and multivariate analysis were performed to identify predictive factors of ovarian sensitivity (OS). A nomogram for gonadotropin starting dose was developed based on the multivariate regression model. Validation was performed using concordance statistics and bootstrap resampling. A multivariate regression model based on serum anti-Müllerian hormone (AMH) level, antral follicle count (AFC), and body mass index (BMI) was developed and accounted for 59% of the variability of OS. An easy-to-use predictive nomogram for gonadotropin starting dose was established with excellent accuracy. The concordance index (C-index) of the nomogram was 0.833 (95% CI, 0.829-0.837). Internal validation using bootstrap resampling further showed the good performance of the nomogram. In conclusion, gonadotropin starting dose in antagonist protocol can be predicted precisely by a novel nomogram.

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