503 research outputs found
A combined iterative sure independence screening and Cox proportional hazard model for extracting and analyzing prognostic biomarkers of adenocarcinoma lung cancer
No full textThe selection of significant biomarkers is essential in researching cancer diagnosis and treatment. The independence screening method works substantively to select crucial features based on the conditional marginal selection method. But it may pretermit the concoction effect of some marginally less essential covariates. We aim to obtain a significant biomarker-specific prediction on overall survival to know their survival and death risk. In this work, an iterative sure independence screening (ISIS) scheme has been applied to extract features from the high-dimensional dataset of adenocarcinoma lung cancer. Conventional and Bayesian approaches of the Cox proportional hazard (CPH) model have been used for analyzing the data to provide interpretation and conclusions about survival estimates. The accelerated failure time model is also used as an alternative to the CPH model. A forest plot is employed to show the graphical representation of the meta-analysis of the study design. Utilizing ISIS, we selected up to 20 relevant features From the entire dataset of adenocarcinoma lung cancer; some of them are liable to produce a positive hazard ratio greater than 1, and some are less than 1. The P values associated with the selected biomarkers imply their statistical significance. Fourteen biomarkers have been identified with a hazard ratio of less than 1; the remaining 20 biomarkers are greater than 1. These 14 biomarkers produce less risk of death for patients with adenocarcinoma lung cancer, and the remaining six biomarkers result in a high risk of death from adenocarcinoma lung cancer.</p
Supplementary Material, Supplementary_Material – Expression signature of lysosomal-associated transmembrane protein 4B in hepatitis C virus-induced hepatocellular carcinoma
No full textSupplementary Material, Supplementary_Material for Expression signature of lysosomal-associated transmembrane protein 4B in hepatitis C virus-induced hepatocellular carcinoma by Gaurav Roy, Papai Roy, Atanu Bhattacharjee, Mudassar Shahid, Mohammad Misbah, Subash Gupta and Mohammad Husain in The International Journal of Biological Markers</p
Distance Correlation Coefficient: An Application with Bayesian Approach in Clinical Data Analysis
No full textThe distance correlation coefficient – based on the product-moment approach – is one method by which to explore the relationship between variables. The Bayesian approach is a powerful tool to determine statistical inferences with credible intervals. Prior information about the relationship between BP and Serum cholesterol was applied to formulate the distance correlation between the two variables. The conjugate prior is considered to formulate the posterior estimates of the distance correlations. The illustrated method is simple and is suitable for other experimental studies
Big Data Analytics in Oncology with R
No full textBig Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area
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