DUX4 Activates Germ line Genes, Retroelements, and Immune Mediators: Implications for Facioscapulohumeral Dystrophy

Mechanisms of disease, diagnostics and therap

Combining Semantic Relations and DNA Microarray Data for Novel Hypotheses Generation

Abstract. Although microarray experiments have great potential to support progress in biomedical research, results are not easy to interpret. Information about the functions and relations of relevant genes needs to be extracted from the vast biomedical literature. A potential solution is to use computerized text analysis methods. Our proposal enhances these methods with semantic relations. We describe an application that integrates such relations with microarray results and discuss its benefits in supporting enhanced access to the relevant literature for interpretation of results and novel hypotheses generation. The application is available a

Estimating parametric semi-Markov models from panel data using phase-type approximations

Inference for semi-Markov models under panel data presents considerable computational difficulties. In general the likelihood is intractable, but a tractable likelihood with the form of a hidden Markov model can be obtained if the sojourn times in each of the states are assumed to have phase-type distributions. However, using phase-type distributions directly may be undesirable as they require estimation of parameters which may be poorly identified. In this article, an approach to fitting semi-Markov models with standard parametric sojourn distributions is developed. The method involves establishing a family of Coxian phase-type distribution approximations to the parametric distribution and merging approximations for different states to obtain an approximate semi-Markov process with a tractable likelihood. Approximations are developed for Weibull and Gamma distributions and demonstrated on data relating to post-lung-transplantation patients

Feature Selection Study on Separate Multi-modal Datasets: Application on Cutaneous Melanoma

Part 1: Second Artificial Intelligence Applications in Biomedicine Workshop (AIAB 2012)International audienceIn this work, we study the behavior of a feature selection algorithm (backwards selection) using random forests, by fusing multi-modal data from different subjects. Two separate datasets related to cutaneous melanoma, obtained from image (dermoscopy) and non-image (microarray) sources are used. Imputations are applied in order to acquire a unified dataset, prior the effect of machine learning algorithms. The results suggest that application of the normal random imputation method acts as an additional variation factor, helping towards stability of potential recommended biomarkers. In addition, microarray-derived features were favorably selected as best predictors compared to image-derived features

Differential Expression Analysis of Complex RNA-seq Experiments Using edgeR ∗

This article reviews the statistical theory underlying the edgeR software package for differential expression of RNA-seq data. Negative binomial models are used to capture the quadratic mean-variance relationship that can be observed in RNA-seq data. Conditional likelihood methods are used to avoid bias when estimating the level of variation. Empirical Bayes methods are used to allow gene-specific variation estimates even when the number of replicate samples is very small. Generalized linear models are used to accommodate arbitrarily complex designs. A key feature of the edgeR package is the use of weighted likelihood methods to implement a flexible empirical Bayes approach in the absence of easily tractable sampling distributions. The methodology is implemented in flexible software that is easy to use even for users who are not professional statisticians or bioinformaticians. The software is part of the Bioconductor project. This article describes some recently implemented features. Loess-style weighting is used to improve the weighted likelihood approach, and an analogy with quasilikelihood is used to estimate the optimal weight to be given to the empirical Bayes prior. The article includes a fully worked case study with complete code.