Robust Model Selection for Classification of Microarrays

Recently, microarray-based cancer diagnosis systems have been increasingly investigated. However, cost reduction and reliability assurance of such diagnosis systems are still remaing problems in real clinical scenes. To reduce the cost, we need a supervised classifier involving the smallest number of genes, as long as the classifier is sufficiently reliable. To achieve a reliable classifier, we should assess candidate classifiers and select the best one. In the selection process of the best classifier, however, the assessment criterion must involve large variance because of limited number of samples and non-negligible observation noise. Therefore, even if a classifier with a very small number of genes exhibited the smallest leave-one-out cross-validation (LOO) error rate, it would not necessarily be reliable because classifiers based on a small number of genes tend to show large variance. We propose a robust model selection criterion, the min-max criterion, based on a resampling bootstrap simulation to assess the variance of estimation of classification error rates. We applied our assessment framework to four published real gene expression datasets and one synthetic dataset. We found that a state-of-the-art procedure, weighted voting classifiers with LOO criterion, had a non-negligible risk of selecting extremely poor classifiers and, on the other hand, that the new min-max criterion could eliminate that risk. These finding suggests that our criterion presents a safer procedure to design a practical cancer diagnosis system

Performance analysis of anaplasma antibody competitive ELISA using the ROC curve for screening of anaplasmosis in camel populations in Egypt

Anaplasmosis is a tick-born and potential zoonotic disease caused by Anaplasma (A.) phagocytophilum, A. ovis, A. platys and A. capra. Anaplasma marginale affecting bovines and camels causing significant economic losses. Camels as an integral part of the socio-economic lifestyle of nomads in semi-arid to arid ecosystems are prone to suffer from subclinical Anaplasma infections. This study aimed to determine the performance and adaptation of commercial competitive Anaplasma ELISA (cELISA) as a tool for screening the seroprevalence of anaplasmosis whitin the camel populations in Egypt. This study was based on the serological investigation of 437 camel sera collected between 2015 and 2016 during a Q fever prevalence study in Egypt using commercially available cELISA for the detection of antibodies specific for Anaplasma in bovine serum. The receiver operating characteristic (ROC) curve, an analysis method for optimizing cutoff values in cELISAs, was used to estimate the sensitivity and specificity using 76 true as serological positive (n = 7) and negative (n = 60) for Anaplasma antibodies. ROC curve analysis was done for 7 true positive and 60 true negative bovine samples and 7 true positive and 29 true negative camel samples serum. Real time PCR and/or conventional PCR was applied to confirm Anaplasma spp. specific-DNA in camel serum as an indication of a true positive and true negative for ROC analysis. Chi square analysis was performed to estimate the association between risk factors and anaplasmosis in camels. The cutoff value was determined as 0.42 (p value ≤ 0.001). Data simulation with randomly generated values revealed a cutoff value of 0.417 (p ≤ 0.001) with resulting 58.1% Se and 97.8% Sp. Seven true positive and 29 true negative camel serum samples was confirmed by PCR. Using the estimated cut off, the seroprevalence in the Nile Valley and Delta and the Eastern Desert domain was 47.4% and 46.4%, respectively. The potential risk factors as domains and origin of animals were less significantly associated with the prevalence of anaplasmosis (domains: χ(2) = 41.8, p value ≤ 0.001 and origin: χ(2) = 42.56, p value ≤ 0.001). Raising awareness especially for veterinarians and animal owners will significantly contribute to the best understanding of anaplasmosis in camels in Egypt. Alternative (in silico) validation techniques and preliminary prevalence studies are mandatory towards the control of neglected anaplasmosis in the camel population

Reproducibility of the bronchoconstrictive response to eucapnic voluntary hyperpnoea

Background: Eucapnic voluntary hyperpnoea (EVH) is considered an effective bronchoprovocation challenge for identifying exercise-induced bronchoconstriction (EIB). However, the reproducibility of the hyperpnoea-induced bronchoconstriction (HIB) response elicited by EVH remains unknown and was therefore the focus of this study. Methods: Two cohorts of 16 physically active males (each cohort comprised 8 controls and 8 with physician diagnosis of asthma) participated in two studies of the short- and long-term reproducibility of the bronchoconstrictive response to an EVH test with dry air. EVH was performed on days 0, 7, 14, and 21 (short-term study), and 0, 35, and 70 (long-term study). HIB was diagnosed by a ≥10% fall in forced expiratory volume in 1 s (FEV1) after EVH. Results: On day 0 of the short-term study, FEV1 fell by 2 ± 1% (P < 0.05) and 27 ± 18% (P < 0.01) from pre-to post-EVH in control and HIB-positive groups respectively. The post-EVH fall in FEV1 did not differ across the short-term study test days. In the HIB-positive group, the day-to-day coefficient of variation, reproducibility, and smallest meaningful change for the fall in FEV1 were 12%, 328 mL, and 164 mL, respectively. On day 0 of the long-term study, FEV1 fell by 2 ± 2% and 25 ± 18% (P < 0.01) after EVH in control and HIB-positive groups respectively. The post-EVH fall in FEV1 did not differ across the long-term study test days. In the HIB-positive group, the day-to-day coefficient of variation, reproducibility, and smallest meaningful change for the fall in FEV1 were 10%, 196 mL, and 98 mL respectively. Conclusion: The EVH test elicits a reproducible bronchoconstrictive response in physically active males with physician diagnosed asthma. These data thus support the clinical utility of the EVH test for EIB screening and monitoring

Directed networks as a novel way to describe and analyze cardiac excitation : directed graph mapping

Networks provide a powerful methodology with applications in a variety of biological, technological and social systems such as analysis of brain data, social networks, internet search engine algorithms, etc. To date, directed networks have not yet been applied to characterize the excitation of the human heart. In clinical practice, cardiac excitation is recorded by multiple discrete electrodes. During (normal) sinus rhythm or during cardiac arrhythmias, successive excitation connects neighboring electrodes, resulting in their own unique directed network. This in theory makes it a perfect fit for directed network analysis. In this study, we applied directed networks to the heart in order to describe and characterize cardiac arrhythmias. Proof-of-principle was established using in-silico and clinical data. We demonstrated that tools used in network theory analysis allow determination of the mechanism and location of certain cardiac arrhythmias. We show that the robustness of this approach can potentially exceed the existing state-of-the art methodology used in clinics. Furthermore, implementation of these techniques in daily practice can improve the accuracy and speed of cardiac arrhythmia analysis. It may also provide novel insights in arrhythmias that are still incompletely understood