Journal of Biometrics & Biostatistics

Task scheduling in parallel processing is a technique in which processes are assigned to different processors. Task scheduling in parallel processing use different types of algorithms and techniques which are used to reduce the number of delayed jobs. Now a days there are different kind of scheduling algorithms and techniques used to reduce the execution time of tasks. As task scheduling the NP-hard problem and no one can say the about the best algorithm proposed so in this paper we will review some of the task scheduling algorithms and other techniques.

Information and telecommunication technology (ICT) has penetrated deep into the human lives and is affecting human life style in different aspects. The rapid growth in ICT has embarked improvement in computing devices and computing techniques. Currently cloud computing is one of the most hyped innovation. It has several positive impacts like reduce cost, increase throughput, ease of use but it also have certain security issues that must be dealt with carefully. There are several techniques that can be used to overcome this major problem. In this paper will analyses biometric authentication in cloud computing, its various techniques and how they are helpful in reducing the security threats. It provides a comprehensive and structured overview of biometric authentication for enhancing cloud security.

The ALAD (Aminolevulinic Acid Dehydratase) gene polymorphism is linked with the accumulation of lead in the bone, blood and the other internal organs and it may predispose for many critical symptoms in the lead exposed persons. The aim of this study is to determine the primary, secondary and tertiary structure of lead. This enzyme is susceptible towards the toxic effect of lead. Primary structure prediction was done by Protparam tool, Compute PI/MW tool, Proscale tool. Secondary structure prediction was done by Self –optimize prediction method (SOPMA) tool, Porter tool. Tertiary structure prediction was done by protein structure prediction server. Domain was determined by Simple Modular Architecture Research (SMART) tool.

Pulse rate(PR) and respiratory rates(RR) are main symptoms of congestive heart failure(CHF) and the abnormal PR and RR are broad indicators of major physiological instabilities. The lower PR and RR are associated with a strong and healthier heart. CHF is a complex clinical syndrome that can result from any structural or functional cardiac disorder that impairs the ability of the ventricle to fill with or eject blood. The main objective of this study is, to investigate the joint evolution of PR and RR of CHF patients and identify the potential risk factors affecting the two end points. The latest data from Sep.2012 up to Aug.2013 have been taken from medical charts of 264 adult CHF patients to model separate and joint linear mixed effect for PR and RR. The baseline mean and standard deviation of PR and RR are 126.11 and 18.98 and 31.64 and 10.99 respectively. The association of the evolution for PR and RR was estimated to be (ρ=0.7054) which is statistically significant with 95% CI of (0.642, 0.769). PR and RR showed a decreasing pattern over time in both joint and separate models. Furthermore, a positive and significant association was observed between the two end points and all covariates except LVEF and time. Finally, to identify associated effect fitting joint model for paired endpoints is recommended.

Tuberculosis (TB) is a disease caused by bacteria that are spread through the air from person to person. If not treated properly, TB disease can be fatal. People infected with TB bacteria who are not sick may still need treatment to prevent TB disease from developing in the future. Tuberculosis (TB) is currently one of the greatest problems in public health. Mycobacterium tuberculosis infects about one third of the world's population, of whom more than 80% are living in developing countries. The incidence and prevalence of TB are very different in various parts of Iran and also throughout the world. Learn to recognize the symptoms of TB disease and find out if you are at risk.

Representing medical data and biological important part in experiments are concerned with Human life, the primary objective of this research is to use the statistical optimization method analysis for the data and knowledge of the important factors affecting the variables of the study (liver fat, liver size), where the variables are interconnected there is a need for statistical method to examines the degree of their relationship, we used bivariate logistic. To achieve the of the research on the field study will be done in Al-Sadr medical city in the province of Najaf by taking a sample of 150 people auditors diabetes and liver disease center, from the statistical analysis results we observed the degree of diagnosis model in both method are good, and also we monitored that impact factors in responses (liver fat, liver size) and some comment as multivariate logistic in the Future.

In this study cancer screening likelihood method was used to analyze the CT scan group in the National Lung Screening Trial (NLST) data. Three key parameters: screening sensitivity, transition probability density from disease free to preclinical state, and sojourn time in the preclinical state, were estimated using Bayesian approach and Markov Chain Monte Carlo simulations. The sensitivity for lung cancer screening using CT scan is high; it does not depend on a patient’s age, and is slightly higher in females than in males. The transition probability from the disease-free to the preclinical state has a peak around age 70 for both genders, which agrees with the fact that the highest lung cancer incidence rate appears between age 65 and 74. The posterior mean sojourn time is around 1.5 years for all groups, and that explains why screening only have a short time interval to catch lung cancer. Accurate estimation of the three key parameters is critical for other estimations such as lead time and over-diagnosis, because these quantities are functions of the three key parameters.

In a multiple regression analysis, it is usually difficult to interpret the estimator of the individual coefficients if the explanatory variables are highly inter-correlated. Such a problem is often referred to as the multicollinearity problem. There exist several ways to solve this problem. One such way is ridge regression. Two approaches of estimating the shrinkage ridge parameter k are proposed. Comparison is made with other ridge-type estimators. To investigate the performance of our proposed methods with the traditional ordinary least squares (OLS) and the other approaches for estimating the parameters of the ridge regression model, we calculate the mean squares error (MSE) using the simulation techniques. Results of the simulation study shows that the suggested ridge regression outperforms both the OLS estimator and the other ridge-type estimators in all of the different situations evaluated in this paper.

Microarray technology, which observes thousands of gene expressions at once, is one of the popular topics in recent decades. When it comes to the analysis of microarray data to identify differentially expressed (DE) genes, many methods have been proposed and modified for improvement. However, the most popular methods such as Significance Analysis of Microarrays (SAM), samroc, fold change, and rank product are far from perfect. In order to determine which method is most powerful, it comes down to the characteristics of the sample and distribution of the gene expressions. The most practiced method is usually SAM or samroc but when the data tends to be skewed, the power of these methods decreases. With the concept that the median becomes a better measure of central tendency than the mean when the data is skewed, the test statistics of the SAM and fold change methods are modified in this paper. This study shows that the median modified fold change method improves the power for many cases when identifying DE genes if the data follows a lognormal distribution.

In diagnostic medicine, the test that determines the true disease status without an error is referred to as the gold standard. Even when a gold standard exists, it is extremely difficult to verify each patient due to the issues of costeffectiveness and invasive nature of the procedures. In practice some of the patients with test results are not selected for verification of the disease status which results in verification bias for diagnostic tests. The ability of the diagnostic test to correctly identify the patients with and without the disease can be evaluated by measures such as sensitivity, specificity and predictive values. However, these measures can give biased estimates if we only consider the patients with test results who also underwent the gold standard procedure. The emphasis of this paper is to apply the log-linear model approach to compute the maximum likelihood estimates for sensitivity, specificity and predictive values. We also compare the estimates with Zhou’s results and apply this approach to analyze Hepatic Scintigraph data under the assumption of ignorable as well as non-ignorable missing data mechanisms. We demonstrated the efficiency of the estimators by using simulation studies.

The name of a medical specialty is a dynamic one because it changes according to chronological contextual needs. We analyzed 12 medical specialties nomenclatures for a 20 years period and we described the conceptual model of this change. The division, correspondence, renaming, fusion, creation, return and cancellation have been identified as operations, composing an “informational engine”. We found 754 links between 692 different medical specialties instances. The resulting application is an electronic dictionary for the medical specialties correspondences. The project should be extended to all the European countries, even to create an online database for the next European professional card.

Diagnostic biomarkers are used frequently in epidemiologic and clinical work. The ability of a diagnostic biomarker to discriminate between subjects who develop disease (cases) and subjects who do not (controls) is often measured by the area under the receiver operating characteristic curve (AUC). The diagnostic biomarkers are usually measured with error. Ignoring measurement error can cause biased estimation of AUC, which results in misleading interpretation of the efficacy of a diagnostic biomarker. Several methods have been proposed to correct AUC for measurement error, most of which required the normality assumption for the distributions of diagnostic biomarkers. In this article, we propose a new method to correct AUC for measurement error and derive approximate confidence limits for the corrected AUC. The proposed method does not require the normality assumption. Both real data analyses and simulation studies show good performance of the proposed measurement error correction method.

A nonlinear mixed-effects approach is developed for disease progression models that incorporate variation in age in a Bayesian framework. We further generalize the probability model for sensitivity to depend on age at diagnosis, time spent in the preclinical state and sojourn time. The developed models are then applied to the Johns Hopkins Lung Project data and the Health Insurance Plan for Greater New York data using Bayesian Markov chain Monte Carlo and are compared with the estimation method that does not consider random-effects from age. Using the developed models, we obtain not only age-specific individual-level distributions, but also population-level distributions of sensitivity, sojourn time and transition probability.