scholarly journals Leveraging GWAS Data Derived From a Large Cooperative Group Trial to Identify a SNP Cluster Associated With the Risk of Taxane-induced Peripheral Neuropathy

2021 ◽  
Author(s):  
Maryam Lustberg ◽  
Xuan Wu ◽  
Juan Luis Fernández-Martínez ◽  
Enrique J. de Andrés-Galiana ◽  
Santosh Philips ◽  
...  
Keyword(s):  
Peripheral Neuropathy ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Aggregate Data ◽  
Discriminatory Power ◽  
Gwas Data ◽  
Cooperative Group ◽  
Fisher’S Ratio ◽  
Leave One Out ◽  
Group Trial

Abstract BackgroundChemotherapy-induced peripheral neuropathy (CIPN) is a common toxicity of taxanes for which there is no effective intervention. Genomic CIPN risk determination has yielded promising, but inconsistent results. The present study assessed the utility of a collective SNP cluster identified using novel analytic to describe taxane-associated CIPN risk.MethodsWe analyzed GWAS data derived from ECOG-5103, first identifying SNPs that were most strongly associated with CIPN using Fisher’s ratio. We then ranked ordered those SNPs which discriminated CIPN-positive from CIPN-negative phenotypes based on their discriminatory power and developed the cluster of SNPs which provided the highest predictive accuracy using leave-one-out cross validation (LOOCV).ResultsUsing GWAS aggregate data, we identified a 267 SNP cluster which was associated with a CIPN+ phenotype with an accuracy of 96.1%. ConclusionsIdentification of a 267 SNP cluster could accurately predict CIPN risk. Validation using an independent patient cohort should be performed.

Mapping Arctic clam abundance using multiple datasets, models, and a spatially explicit accuracy assessment

2019 ◽  
Vol 76 (7) ◽  
pp. 2349-2361
Author(s):  
Benjamin Misiuk ◽  
Trevor Bell ◽  
Alec Aitken ◽  
Craig J Brown ◽  
Evan N Edinger
Keyword(s):  
Spatial Autocorrelation ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Accuracy Assessment ◽  
Small Scale ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Management Tools ◽  
Seabed Topography ◽  
Leave One Out

Abstract Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence–absence and abundance of Arctic soft-shell clam (Mya spp.) to support the management of a local small-scale fishery in Qikiqtarjuaq, Nunavut, Canada. These models were combined to predict Mya abundance, conditional on presence throughout the study area. Results suggested that water depth was the primary environmental factor limiting Mya habitat suitability, yet seabed topography and substrate characteristics influence their abundance within suitable habitat. Ten-fold cross-validation and spatial leave-one-out cross-validation (LOO CV) were used to assess the accuracy of combined predictions and to test whether this was inflated by the spatial autocorrelation of transect sample data. Results demonstrated that four different measures of predictive accuracy were substantially inflated due to spatial autocorrelation, and the spatial LOO CV results were therefore adopted as the best estimates of performance.

scholarly journals Supplement Use and Chemotherapy-Induced Peripheral Neuropathy in a Cooperative Group Trial (S0221): The DELCaP Study

2017 ◽  
Vol 109 (12) ◽  
Author(s):  
Gary R Zirpoli ◽  
Susan E McCann ◽  
Lara E Sucheston-Campbell ◽  
Dawn L Hershman ◽  
Gregory Ciupak ◽  
...  
Keyword(s):  
Peripheral Neuropathy ◽  
Cooperative Group ◽  
Supplement Use ◽  
Group Trial

scholarly journals Associations between self-reported diet during treatment and chemotherapy-induced peripheral neuropathy in a cooperative group trial (S0221)

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Jennifer M. Mongiovi ◽  
Gary R. Zirpoli ◽  
Rikki Cannioto ◽  
Lara E. Sucheston-Campbell ◽  
Dawn L. Hershman ◽  
...  
Keyword(s):  
Peripheral Neuropathy ◽  
Cooperative Group ◽  
Group Trial

scholarly journals Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models

2020 ◽  
Author(s):  
Paul-Christian Bürkner ◽  
Jonah Gabry ◽  
Aki Vehtari
Keyword(s):  
Spatial Statistics ◽  
Model Comparison ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Observation Model ◽  
Multivariate Normal ◽  
Temporal And Spatial ◽  
Leave One Out

AbstractCross-validation can be used to measure a model’s predictive accuracy for the purpose of model comparison, averaging, or selection. Standard leave-one-out cross-validation (LOO-CV) requires that the observation model can be factorized into simple terms, but a lot of important models in temporal and spatial statistics do not have this property or are inefficient or unstable when forced into a factorized form. We derive how to efficiently compute and validate both exact and approximate LOO-CV for any Bayesian non-factorized model with a multivariate normal or Student-$$t$$ t distribution on the outcome values. We demonstrate the method using lagged simultaneously autoregressive (SAR) models as a case study.

scholarly journals Assessing elderly’s functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lisha Yu ◽  
Yang Zhao ◽  
Hailiang Wang ◽  
Tien-Lung Sun ◽  
Terrence E. Murphy ◽  
...  
Keyword(s):  
Predictive Models ◽  
Cross Validation ◽  
Motor Coordination ◽  
Predictive Accuracy ◽  
Short Form ◽  
Community Dwelling ◽  
Regularized Regression ◽  
Balance Assessment ◽  
Timed Up And Go ◽  
Functional Balance

Abstract Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score. Methods Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. Results Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. Conclusions The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.

scholarly journals QSPR study on the gas/particle partition coefficient of PCBs by using molecular distance-edge vector index

2014 ◽  
Vol 79 (8) ◽  
pp. 965-975 ◽  
Author(s):  
Long Jiao ◽  
Xiaofei Wang ◽  
LI. Hua ◽  
Yunxia Wang
Keyword(s):  
Partition Coefficient ◽  
Cross Validation ◽  
External Validation ◽  
Quantitative Relationship ◽  
Structure Property ◽  
Prediction Ability ◽  
Vector Index ◽  
Artificial Neural Network Ann ◽  
Leave One Out ◽  
Edge Vector

The quantitative structure property relationship (QSPR) for gas/particle partition coefficient, Kp, of polychlorinated biphenyls (PCBs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PCBs. The quantitative relationship between the MDEV index and log Kp was modeled by multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave one out cross validation and external validation were carried out to assess the prediction ability of the developed models. When the MLR method is used, the root mean square relative error (RMSRE) of prediction for leave one out cross validation and external validation is 4.72 and 8.62 respectively. When the ANN method is employed, the prediction RMSRE of leave one out cross validation and external validation is 3.87 and 7.47 respectively. It is demonstrated that the developed models are practicable for predicting the Kp of PCBs. The MDEV index is shown to be quantitatively related to the Kp of PCBs.

scholarly journals Study onYang-XuUsing Body Constitution Questionnaire and Blood Variables in Healthy Volunteers

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Hong-Jhang Chen ◽  
Yii-Jeng Lin ◽  
Pei-Chen Wu ◽  
Wei-Hsiang Hsu ◽  
Wan-Chung Hu ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Logistic Regression Model ◽  
Cross Validation ◽  
Blood Biomarkers ◽  
Metabolic Characteristics ◽  
Body Constitution ◽  
Leave One Out ◽  
The Relationship ◽  
Fold Cross Validation ◽  
Blood Variables

Traditional Chinese medicine (TCM) formulates treatment according to body constitution (BC) differentiation. Different constitutions have specific metabolic characteristics and different susceptibility to certain diseases. This study aimed to assess theYang-Xuconstitution using a body constitution questionnaire (BCQ) and clinical blood variables. A BCQ was employed to assess the clinical manifestation ofYang-Xu. The logistic regression model was conducted to explore the relationship between BC scores and biomarkers. Leave-one-out cross-validation (LOOCV) and K-fold cross-validation were performed to evaluate the accuracy of a predictive model in practice. Decision trees (DTs) were conducted to determine the possible relationships between blood biomarkers and BC scores. According to the BCQ analysis, 49% participants without any BC were classified as healthy subjects. Among them, 130 samples were selected for further analysis and divided into two groups. One group comprised healthy subjects without any BC (68%), while subjects of the other group, named as the sub-healthy group, had three BCs (32%). Six biomarkers, CRE, TSH, HB, MONO, RBC, and LH, were found to have the greatest impact on BCQ outcomes inYang-Xusubjects. This study indicated significant biochemical differences inYang-Xusubjects, which may provide a connection between blood variables and theYang-XuBC.

Pharmacokinetics of Propofol in Adult Patients Undergoing Coronary Revascularization

1996 ◽  
Vol 84 (6) ◽  
pp. 1288-1297 ◽  
Author(s):  
James M. Bailey ◽  
Christina T. Mora ◽  
Stephen L. Shafer ◽  
Keyword(s):  
Cardiac Surgery ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Coronary Revascularization ◽  
Artery Bypass ◽  
Bypass Graft ◽  
Adult Patients ◽  
Pharmacokinetic Parameters ◽  
Surgery Patient ◽  
Prospective Comparison

Background Propofol is increasingly used for cardiac anesthesia and for perioperative sedation. Because pharmacokinetic parameters vary among distinct patient populations, rational drug dosing in the cardiac surgery patient is dependent on characterization of the drug's pharmacokinetic parameters in patients actually undergoing cardiac procedures and cardiopulmonary bypass (CPB). In this study, the pharmacokinetics of propofol was characterized in adult patients undergoing coronary revascularization. Methods Anesthesia was induced and maintained by computer-controlled infusions of propofol and alfentanil, or sufentanil, in 41 adult patients undergoing coronary artery bypass graft surgery. Blood samples for determination of plasma propofol concentrations were collected during the predefined study periods and assayed by high-pressure liquid chromatography. Three-compartment model pharmacokinetic parameters were determined by nonlinear extended least-squares regression of pooled data from patients receiving propofol throughout the perioperative period. The effect of CPB on propofol pharmacokinetics was modeled by allowing the parameters to change with the institution and completion of extracorporeal circulation and selecting the optimal model on the basis of the logarithm of the likelihood. Predicted propofol concentrations were calculated by convolving the infusion rates with unit disposition functions using the estimated parameters. The predictive accuracy of the parameters was evaluated by cross-validation and by a prospective comparison of predicted and measured levels in a subset of patients. Results Optimal pharmacokinetic parameters were: central compartment volume = 6.0 l; second compartment volume = 49.5 l; third compartment volume = 429.3 l; Cl1 (elimination clearance) = 0.68 l/min; Cl2 (distribution clearance) = 1.97 l/min1; and Cl3 (distribution clearance) = 0.70 l/min. The effects of CPB were optimally modeled by step changes in V1 and Cl1 to values of 15.9 and 1.95, respectively, with the institution of CPB. Median absolute prediction error was 18% in the cross-validation assessment and 19% in the prospective evaluation. There was no evidence for nonlinear kinetics. Previously published propofol pharmacokinetic parameter sets poorly predicted the observed concentrations in cardiac surgical patients. Conclusions The pharmacokinetics of propofol in adult patients undergoing cardiac surgery with CPB are dissimilar from those reported for other adult patient populations. The effect of CPB was best modeled by an increase in V1 and Cl1. Predictive accuracy of the derived pharmacokinetic parameters was excellent as measured by cross-validation and a prospective test.

Evaluation of Safety Performance Functions Based on Experimental Design Using a Cross-Validation Method

2017 ◽  
Vol 2636 (1) ◽  
pp. 62-72 ◽  
Author(s):  
Jung-Han Wang ◽  
Mohamed A. Abdel-Aty ◽  
Jaeyoung Lee
Keyword(s):  
Goodness Of Fit ◽  
Cross Validation ◽  
Safety Performance ◽  
Major Road ◽  
Highway Safety Manual ◽  
Annual Average Daily Traffic ◽  
Minor Road ◽  
Safety Performance Functions ◽  
The One ◽  
Leave One Out

The Highway Safety Manual (HSM) Part C provides a series of safety performance functions (SPFs) for different roadway conditions. The SPFs suggested in the HSM are formulated on the basis of exposure variables: the logarithms of the annual average daily traffic (AADT) on the major road and on the minor road under the base condition. In this research, data from 7,802 intersections in Florida were collected and processed. These intersections were categorized into seven types based on area type (rural or urban), number of legs (three or four), and number of approaches controlled by stop signs. Twenty-two SPF formulations, including the one suggested by the HSM, were developed for each intersection type for examination of the goodness-of-fit measures of the SPFs. In addition, the goodness of fit of each model of the 22 SPFs in each category was examined with 10-fold leave-one-out cross-validation (LOOCV). With a comparison of the delta values generated with the LOOCV method, it is suggested that the SPF with the logarithm of the total entering vehicle volume and the ratio of the AADT on the minor road and the AADT on the major road are important. In addition, the SPFs with the AADT on the major road and the AADT on the minor road and their logarithmic transformations are also important. Therefore, it is suggested that the future HSM compare these two SPF formulations—as suggested in the current research, along with the original SPF formulation in the manual—and select the one with the best model fit on the basis of the delta value using LOOCV.

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