scholarly journals Sublinear Algorithms for Penalized Logistic Regression in Massive Datasets

2012 ◽  
pp. 553-568 ◽  
Author(s):  
Haoruo Peng ◽  
Zhengyu Wang ◽  
Edward Y. Chang ◽  
Shuchang Zhou ◽  
Zhihua Zhang
Keyword(s):  
Logistic Regression ◽  
Sublinear Algorithms ◽  
Massive Datasets ◽  
Penalized Logistic Regression

scholarly journals Predictors of suicidal thoughts and behavior in children: results from penalized logistic regression analyses in the ABCD study

2021 ◽  
Author(s):  
Laura S. van Velzen ◽  
Yara J. Toenders ◽  
Aina Avila-Parcet ◽  
Richard Dinga ◽  
Jill A. Rabinowitz ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Mental Health Treatment ◽  
Future Research ◽  
Control Group ◽  
Operating Characteristics ◽  
Genetic Characteristics ◽  
Healthy Control ◽  
History Of ◽  
Social Environmental ◽  
Penalized Logistic Regression

AbstractDespite numerous efforts to predict suicide risk in children, the ability to reliably identify youth that will engage in suicide thoughts or behaviors (STB) has remained remarkably unsuccessful. To further knowledge in this area, we apply a novel machine learning approach and examine whether children with STB could be differentiated from children without STB based on a combination of sociodemographic, physical health, social environmental, clinical psychiatric, cognitive, biological and genetic characteristics. The study sample included 5,885 unrelated children (50% female, 67% white) between 9 and 11 years old from the Adolescent Brain Cognitive Development (ABCD) study. Both parents and youth reported on children’s STB and based on these reports, we divided children into three subgroups: 1. children with current or past STB, 2. children with psychiatric disorder but no STB (clinical controls) and 3. healthy control children. We performed binomial penalized logistic regression analysis to distinguish between groups. The analyses were performed separately for child-reported STB and parent-reported STB. Results showed that we were able to distinguish the STB group from healthy controls and clinical controls (area under the receiver operating characteristics curve (AUROC) range: 0.79-0.81 and 0.70-0.78 respectively). However, we could not distinguish children with suicidal ideation from those who attempted suicide (AUROC range 0.49-0.59). Factors that differentiated the STB group from the clinical control group included family conflict, prodromal psychosis symptoms, impulsivity, depression severity and a history of mental health treatment. Future research is needed to determine if these variables prospectively predict subsequent suicidal behavior.

scholarly journals Investigating Unique Genes of Five Molecular Subtypes of Breast Cancer Using Penalized Logistic Regression

2021 ◽  
Author(s):  
sadegh raoufi ◽  
Saeideh Jafarinejad Farsangi ◽  
Tania Dehesh ◽  
Morteza Hadizadeh
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Web Application ◽  
Biological Process ◽  
Molecular Subtypes ◽  
Negative Regulation ◽  
Adaptive Lasso ◽  
Cell Processes ◽  
Penalized Logistic Regression ◽  
Unique Genes

Abstract Background: Breast cancer is the first cancer and fifth cause of death in women around the world. Exploring unique genes for cancers has become interesting. The aim of this study was to explore unique gens of five molecular subtypes of breast cancer in women using penalized logistic regression models.Methods: In this study, microarray data of five independent GEO datasets was combined. This combination includes genetic information of 324 women with breast cancer and 12 healthy women. Lasso logistic regression and adaptive lasso logistic regression were used to extract unique genes. Biological process of extracted gens was evaluated in open-source GOnet web-application. R software version 3.6.0 with glmnet package was used for fitting the models. Results: Totally, 119 genes were extracted among fifteen pairwise comparisons. 17 genes (%14) had overlap between comparative groups. Among 27 genes contributed in positive regulation of cell processes, one gene belonged exclusively to this biological process. Among 46 genes contributed in negative regulation of cell processes, 6 genes belonged exclusively. Among 50 genes that were significant in regulation of metabolism, 4 genes belonged exclusively. Among 32 genes that related to response of stress, 4 genes belonged exclusively. Conclusions: The most genes selected by lasso logistic regression and adaptive Lasso logistic regression, were diagnosed in negative regulation of cell processes.

scholarly journals Identification of Grouped Rare and Common Variants via Penalized Logistic Regression

2013 ◽  
Vol 37 (6) ◽  
pp. 592-602 ◽  
Author(s):  
Kristin L. Ayers ◽  
Heather J. Cordell
Keyword(s):  
Logistic Regression ◽  
Common Variants ◽  
Penalized Logistic Regression

Speaker verification with non-audible murmur segments by combining global alignment kernel and penalized logistic regression machine

2008 ◽  
Author(s):  
Hideki Okamoto ◽  
Tomoko Matsui ◽  
Hiromichi Kawanami ◽  
Hiroshi Saruwatari ◽  
Kiyohiro Shikano
Keyword(s):  
Logistic Regression ◽  
Speaker Verification ◽  
Global Alignment ◽  
Penalized Logistic Regression

scholarly journals Comparing two samples by penalized logistic regression

2008 ◽  
Vol 2 (0) ◽  
pp. 564-580 ◽  
Author(s):  
Konstantinos Fokianos
Keyword(s):  
Logistic Regression ◽  
Two Samples ◽  
Penalized Logistic Regression

scholarly journals Penalized logistic regression with low prevalence exposures beyond high dimensional settings

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0217057 ◽  
Author(s):  
Sam Doerken ◽  
Marta Avalos ◽  
Emmanuel Lagarde ◽  
Martin Schumacher
Keyword(s):  
Logistic Regression ◽  
High Dimensional ◽  
Penalized Logistic Regression ◽  
Low Prevalence
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