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Independent Validation Set (independent + validation_set)
Selected AbstractsPredicting ready biodegradability in the Japanese ministry of international trade and industry testENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 10 2000Jay Tunkel Abstract Two new predictive models for assessing a chemical's biodegradability in the Japanese Ministry of International Trade and Industry (MITI) ready biodegradation test have been developed. The new methods use an approach similar to that in the existing BIOWIN© program, in which the probability of rapid biodegradation is estimated by means of multiple linear or nonlinear regression against counts of 36 chemical substructures (molecular fragments) plus molecular weight (mol wt). The data set used to develop the new models consisted of results (pass/no pass) from the MITI test for 884 discrete organic chemicals. This data set was first divided into randomly selected training and validation sets, and new coefficients were derived for the training set using the BIOWIN fragment library and mol wt as independent variables. Based on these results, the fragment library was then modified by deleting some fragments and adding or refining others, and the new set of independent variables (42 substructures and mol wt) was fit to the MITI data. The resulting linear and nonlinear regression models accurately classified 81% of the chemicals in an independent validation set. Like the established BIOWIN models, the MITI models are intended for use in chemical screening and in setting priorities for further review. [source] Assessing biotic integrity in a Mediterranean watershed: development and evaluation of a fish-based indexFISHERIES MANAGEMENT & ECOLOGY, Issue 4 2008M. F. MAGALHÃES Abstract, Biological indicators for Mediterranean rivers are poorly developed. This study evaluates the effectiveness of the Index of Biotic Integrity approach (IBI) with fish assemblages in the Guadiana catchment, a typical Mediterranean watershed in Southern Portugal. Reference sites were selected from a set of 95 sites, using a multivariate approach. Fifty-five candidate metrics were screened for range, responsiveness, precision and redundancy. Final metrics included: proportion of native fish, number of intolerant and intermediate species, number of invertivore native fish, number of phyto-lithophilic and polyphilic species, and catches of exotics. The IBI scores correlated with composite gradients of human impact and differed significantly between reference and non-reference sites. Application of the IBI to an independent validation set with 123 sites produced results congruent with the development set and repeatable assessments at 22 sites showed concordance in IBI scoring. This application highlights the effectiveness of the IBI approach even with fish assemblages of limited diversity and ecological specialisation as in Mediterranean streams. [source] Urinary biomarker profiling in transitional cell carcinomaINTERNATIONAL JOURNAL OF CANCER, Issue 11 2006Nicholas P. Munro Abstract Urinary biomarkers or profiles that allow noninvasive detection of recurrent transitional cell carcinoma (TCC) of the bladder are urgently needed. We obtained duplicate proteomic (SELDI) profiles from 227 subjects (118 TCC, 77 healthy controls and 32 controls with benign urological conditions) and used linear mixed effects models to identify peaks that are differentially expressed between TCC and controls and within TCC subgroups. A Random Forest classifier was trained on 130 profiles to develop an algorithm to predict the presence of TCC in a randomly selected initial test set (n = 54) and an independent validation set (n = 43) several months later. Twenty two peaks were differentially expressed between all TCC and controls (p < 10,7). However potential confounding effects of age, sex and analytical run were identified. In an age-matched sub-set, 23 peaks were differentially expressed between TCC and combined benign and healthy controls at the 0.005 significance level. Using the Random Forest classifier, TCC was predicted with 71.7% sensitivity and 62.5% specificity in the initial set and with 78.3% sensitivity and 65.0% specificity in the validation set after 6 months, compared with controls. Several peaks of importance were also identified in the linear mixed effects model. We conclude that SELDI profiling of urine samples can identify patients with TCC with comparable sensitivities and specificities to current tumor marker tests. This is the first time that reproducibility has been demonstrated on an independent test set analyzed several months later. Identification of the relevant peaks may facilitate multiplex marker assay development for detection of recurrent disease. © 2006 Wiley-Liss, Inc. [source] Molecular profiling of platinum resistant ovarian cancer,INTERNATIONAL JOURNAL OF CANCER, Issue 8 2006Jozien Helleman Abstract The aim of this study is to discover a gene set that can predict resistance to platinum-based chemotherapy in ovarian cancer. The study was performed on 96 primary ovarian adenocarcinoma specimens from 2 hospitals all treated with platinum-based chemotherapy. In our search for genes, 24 specimens of the discovery set (5 nonresponders and 19 responders) were profiled in duplicate with 18K cDNA microarrays. Confirmation was done using quantitative RT-PCR on 72 independent specimens (9 nonresponders and 63 responders). Sixty-nine genes were differentially expressed between the nonresponders (n = 5) and the responders (n = 19) in the discovery phase. An algorithm was constructed to identify predictive genes in this discovery set. This resulted in 9 genes (FN1, TOP2A, LBR, ASS, COL3A1, STK6, SGPP1, ITGAE, PCNA), which were confirmed with qRT-PCR. This gene set predicted platinum resistance in an independent validation set of 72 tumours with a sensitivity of 89% (95% CI: 0.68,1.09) and a specificity of 59% (95% CI: 0.47,0.71)(OR = 0.09, p = 0.026). Multivariable analysis including patient and tumour characteristics demonstrated that this set of 9 genes is independent for the prediction of resistance (p < 0.01). The findings of this study are the discovery of a gene signature that classifies the tumours, according to their response, and a 9-gene set that determines resistance in an independent validation set that outperforms patient and tumour characteristics. A larger independent multicentre study should further confirm whether this 9-gene set can identify the patients who will not respond to platinum-based chemotherapy and could benefit from other therapies. © 2005 Wiley-Liss, Inc. [source] Consistency of a two clinical site sample collection: A proteomics studyPROTEOMICS - CLINICAL APPLICATIONS, Issue 8-9 2010Cedric Wiesner Abstract Purpose: We investigated the ability to perform a clinical proteomic study using samples collected at different times from two independent clinical sites. Experimental Design: Label-free 2-D-LC-MS proteomic analysis was used to differentially quantify tens of thousands of peptides from human plasma. We have asked whether samples collected from two sites, when analyzed by this type of peptide profiling, reproducibly contain detectable peptide markers that are differentially expressed in the plasma of disease (advanced renal cancer) patients relative to healthy normals. Results: We have demonstrated that plasma proteins enriched in disease patients are indeed detected reproducibly in both clinical collections. Regression analysis, unsupervised hierarchical clustering and PCA detected no systematic bias in the data related to site of sample collection and processing. Using a genetic algorithm, support vector machine classification method, we were able to correctly classify disease samples at 88% sensitivity and 94% specificity using the second site as an independent validation set. Conclusions and clinical relevance: We conclude that multiple site collection, when analyzed by label-free 2-D-LC-MS, generates data that are sufficiently reproducible to guide reliable biomarker discovery. [source] Chemical structure-based predictive model for methanogenic anaerobic biodegradation potentialENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 9 2007William Meylan Abstract Many screening-level models exist for predicting aerobic biodegradation potential from chemical structure, but anaerobic biodegradation generally has been ignored by modelers. We used a fragment contribution approach to develop a model for predicting biodegradation potential under methanogenic anaerobic conditions. The new model has 37 fragments (substructures) and classifies a substance as either fast or slow, relative to the potential to be biodegraded in the "serum bottle" anaerobic biodegradation screening test (Organization for Economic Cooperation and Development Guideline 311). The model correctly classified 90, 77, and 91% of the chemicals in the training set (n = 169) and two independent validation sets (n = 35 and 23), respectively. Accuracy of predictions of fast and slow degradation was equal for training-set chemicals, but fast-degradation predictions were less accurate than slow-degradation predictions for the validation sets. Analysis of the signs of the fragment coefficients for this and the other (aerobic) Biowin© models suggests that in the context of simple group contribution models, the majority of positive and negative structural influences on ultimate degradation are the same for aerobic and methanogenic anaerobic biodegradation. [source] | ||||||||||