scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 212-213
Keyword(s):  
Logistic Regression ◽  
Private Information ◽  
Statistical Models ◽  
Heart Sound ◽  
Information Leakage ◽  
Scientific Data ◽  
Training Data ◽  
Genotype Data ◽  
Scientific Data Management ◽  
Heart Sound Segmentation

Agarwal V, Podchiyska T, Banda JM, Goel V, Leung TI, Minty EP, Sweeney TE, Gyang E, Shah NH. Learning statistical models of phenotypes using noisy labeled training data. J Am Med Inform Assoc 2016;23(6):1166-73 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocw028 Harmanci A, Gerstein M. Quantification of private information leakage from phenotype-genotype data: linking attacks. Nat Methods 2016;13(3):251-6 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4834871/ Pfiffner PB, Pinyol I, Natter MD, Mandl KD. C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR. PloS One 2016;11(3):e0152722 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816293/ Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJ, Groth P, Goble C, Grethe JS, Heringa J, ‘t Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016;3:160018 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175/ Springer DB, Tarassenko L, Clifford GD. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 2016 Apr;63(4):822-32

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. e19-e20
Keyword(s):  
Logistic Regression ◽  
Private Information ◽  
Statistical Models ◽  
Heart Sound ◽  
Information Leakage ◽  
Scientific Data ◽  
Training Data ◽  
Genotype Data ◽  
Scientific Data Management ◽  
Heart Sound Segmentation

Agarwal V, Podchiyska T, Banda JM, Goel V, Leung TI, Minty EP, Sweeney TE, Gyang E, Shah NH. Learning statistical models of phenotypes using noisy labeled training data. J Am Med Inform Assoc 2016;23(6):1166-73 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocw028 Harmanci A, Gerstein M. Quantification of private information leakage from phenotype-genotype data: linking attacks. Nat Methods 2016;13(3):251-6 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4834871/ Pfiffner PB, Pinyol I, Natter MD, Mandl KD. C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR. PloS One 2016;11(3):e0152722 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816293/ Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJ, Groth P, Goble C, Grethe JS, Heringa J, ‘t Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016;3:160018 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175/ Springer DB, Tarassenko L, Clifford GD. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 2016 Apr;63(4):822-32

scholarly journals 630. A 5-mRNA host response whole-blood classifier trained using patients with non-COVID-19 viral infections accurately predicts severity of COVID-19

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S375-S376
Author(s):  
ljubomir Buturovic ◽  
Purvesh Khatri ◽  
Benjamin Tang ◽  
Kevin Lai ◽  
Win Sen Kuan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Respiratory Failure ◽  
Viral Infections ◽  
Lamp Assay ◽  
Training Data ◽  
Diagnostic Tools ◽  
Severe Respiratory Failure ◽  
Proof Of Concept ◽  
Risk Of Death ◽  
Qpcr Platform

Abstract Background While major progress has been made to establish diagnostic tools for the diagnosis of SARS-CoV-2 infection, determining the severity of COVID-19 remains an unmet medical need. With limited hospital resources, gauging severity would allow for some patients to safely recover in home quarantine while ensuring sicker patients get needed care. We discovered a 5 host mRNA-based classifier for the severity of influenza and other acute viral infections and validated the classifier in COVID-19 patients from Greece. Methods We used training data (N=705) from 21 retrospective clinical studies of influenza and other viral illnesses. Five host mRNAs from a preselected panel were applied to train a logistic regression classifier for predicting 30-day mortality in influenza and other viral illnesses. We then applied this classifier, with fixed weights, to an independent cohort of subjects with confirmed COVID-19 from Athens, Greece (N=71) using NanoString nCounter. Finally, we developed a proof-of-concept rapid, isothermal qRT-LAMP assay for the 5-mRNA host signature using the QuantStudio 6 qPCR platform. Results In 71 patients with COVID-19, the 5 mRNA classifier had an AUROC of 0.88 (95% CI 0.80-0.97) for identifying patients with severe respiratory failure and/or 30-day mortality (Figure 1). Applying a preset cutoff based on training data, the 5-mRNA classifier had 100% sensitivity and 46% specificity for identifying mortality, and 88% sensitivity and 68% specificity for identifying severe respiratory failure. Finally, our proof-of-concept qRT-LAMP assay showed high correlation with the reference NanoString 5-mRNA classifier (r=0.95). Figure 1. Validation of the 5-mRNA classifier in the COVID-19 cohort. (A) Expression of the 5 genes used in the logistic regression model in patients with (red) and without (blue) mortality. (B) The 5-mRNA classifier accurately distinguishes non-severe and severe patients with COVID-19 as well as those at risk of death. Conclusion Our 5-mRNA classifier demonstrated very high accuracy for the prediction of COVID-19 severity and could assist in the rapid, point-of-impact assessment of patients with confirmed COVID-19 to determine level of care thereby improving patient management and healthcare burden. Disclosures ljubomir Buturovic, PhD, Inflammatix Inc. (Employee, Shareholder) Purvesh Khatri, PhD, Inflammatix Inc. (Shareholder) Oliver Liesenfeld, MD, Inflammatix Inc. (Employee, Shareholder) James Wacker, n/a, Inflammatix Inc. (Employee, Shareholder) Uros Midic, PhD, Inflammatix Inc. (Employee, Shareholder) Roland Luethy, PhD, Inflammatix Inc. (Employee, Shareholder) David C. Rawling, PhD, Inflammatix Inc. (Employee, Shareholder) Timothy Sweeney, MD, Inflammatix, Inc. (Employee)

DECISION MAKING BY VARIABLE CONTRIBUTION IN DISCRIMINANT, LOGIT, AND REGRESSION ANALYSES

2004 ◽  
Vol 03 (02) ◽  
pp. 265-279 ◽  
Author(s):  
STAN LIPOVETSKY ◽  
MICHAEL CONKLIN
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Statistical Models ◽  
Regression Analyses ◽  
Discriminant Functions ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Variable Contribution ◽  
Linear Aggregate

Comparative contribution of predictors in multivariate statistical models is widely used for decision making on the importance of the variables for the aims of analysis and prediction. However, the analysis can be made difficult because of the predictors' multicollinearity that distorts estimates for coefficients in the linear aggregate. To solve the problem of the robust evaluation of the predictors' contribution, we apply the Shapley Value regression analysis that provides consistent results in the presence of multicollinearity both for regression and discriminant functions. We also show how the linear discriminant function can be constructed as a multiple regression, and how the logistic regression can be approximated by linear regression that helps to obtain the variables contribution in the linear aggregate.

ASYMMETRIES IN UNIVERSAL GRAMMAR The Role of Method and Statistics

2000 ◽  
Vol 22 (2) ◽  
pp. 209-228 ◽  
Author(s):  
John C. Paolillo
Keyword(s):  
Logistic Regression ◽  
Statistical Analysis ◽  
Statistical Models ◽  
Logistic Regression Model ◽  
Universal Grammar ◽  
Nonnative Speakers ◽  
Potential Explanation ◽  
Differential Learning ◽  
Grammaticality Judgments

Felix (1988) claimed to demonstrate that UG-based knowledge of grammaticality causes nonnative speakers (NNSs) to have more accurate grammaticality judgments on sentences that are ungrammatical according to UG than on those that are grammatical. Birdsong (1994) criticized the methodology employed, noting that it ignores “response bias” (a propensity to judge sentences as ungrammatical) as a potential explanation. Felix and Zobl (1994) dismissed this criticism as merely methodological. In this paper, Birdsong's criticism is upheld by considering a statistical model of the data. At the same time, a more complete logistic regression model allows a fuller statistical analysis, revealing tentative support for the asymmetry claim, as well as differential learning states for different constructions and a tendency toward transfer avoidance. These theoretically significant effects were unnoticed in the earlier discussion of this research. For SLA research on grammaticality judgments to proceed fruitfully, appropriate statistical models need to be considered in designing the research.

Polymorphisms 677C > T and 1298A > C in the MTHFR gene and homocysteine levels in patients with non-alcoholic fatty liver disease as a component of the ontological model

2021 ◽  
Author(s):  
N. A. Nosko ◽  
O. M. Rud
Keyword(s):  
Logistic Regression ◽  
Liver Disease ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Mthfr Gene ◽  
Scientific Data ◽  
Multiple Logistic Regression ◽  
Alcoholic Fatty Liver ◽  
Significant Difference ◽  
Alcoholic Fatty Liver Disease

Objective — to systematize literature data on the presence of 677C > T and 1298A > C polymorphisms in the MTHFR gene and homocysteine levels in patients with non‑alcoholic fatty liver disease (NAFLD); to calculate the frequencies 677C > T and 1298A > C polymorphisms combinations in the MTHFR gene and their impact on NAFLD development; to compare homocysteine levels in patients with and without NAFLD. Materials and methods. The analysis has been performed for the results of investigation of 49 patients, from them 17 subjects with NAFLD and 32 without it. Clinical, laboratory, statistical and ontological methods were used in the study. The MTHFR 677C > T and MTHFR 1298A > C polymorphisms in the MTHFR gene were investigated with the use of real time polymerase chain reaction (RT‑PCR) technique. Homocysteine levels were determined with chemiluminescent immunoassay with reference values 3.7 — 13.9 µmol/L. Multiple logistic regression method was used to evaluate the effects 677C > T and 1298A > C polymorphisms in the MTHFR gene on NAFLD development. Results. The variant of combination of 667С/С/1298А/А polymorphisms in the MTHFR gene (absence of mutation) was reveled in 6 (12 %) persons, that showed a widespread prevalence of variants with the presence of mutations. The correlation between variants of 677C > T and 1298A > C polymorphism in the MTHFR gene has been established (r = 0.429; p < 0.05). The results of multiple logistic regression demonstrated absence of the significant effects of 677C > T and 1298A > C polymorphisms in the MTHFR gen on NAFLD development (p > 0.05). Comparison of the homocysteine levels in patients with and without NAFLD didn’t reveal significant difference (р > 0.05), as well as comparison in the groups with combinations of 677C > T and 1298А > С polymorphisms in the MTHFR gen (р > 0.05). This can be explained by the fact that NAFLD group consisted of manly young patients without hypertension, type 2 diabetes mellitus and severe liver fibrosis. Conclusions. Ontological systematization of the scientific data on NAFLD revealed that 677C > T and 1298A > C polymorphisms in the MTHFR gen are pathogenetically associated with the significant increase in homocysteine levels as a marker of cardiovascular pathology. Giving the multifactorial nature of hyperhomocysteinemia and wide spread of 677C > T and 1298A > C polymorphisms in the MTHFR gen in population, it seems to be impractical to use genetic investigations for MTHFR gen polymorphism in NAFLD patients routinely, but only for the purpose of differential diagnosis of hyperhomocysteinemia.  

Classification of heart sound signals with BP neural network and logistic regression

2017 ◽  
Author(s):  
Lina Li ◽  
Xinpei Wang ◽  
Xiaping Du ◽  
Yuanyuan Liu ◽  
Changchun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Bp Neural Network ◽  
Heart Sound
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