Discovering study-specific gene regulatory networks

This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets

A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

Decision support system for the selection of an ITE or a BTE hearing aid

The purpose of this research is to mine a large set of heterogeneous audiology data to create a decision support system (DSS) to choose between two hearing aid types (ITE and BTE aid). This research is based on the data analysis of audiology data using various statistical and data mining techniques. It uses the data of a large NHS (National Health Services, UK) facility. It uses 180,000 records (covering more than 23,000 different patients) from a hearing aid clinic. The developed system uses an unconventional method to predict hearing aid type for a patient and it can be used as a second opinion by audiologists for complex cases. After modifying the system to take account of the feedback from a professional audiologist, the success rates obtained were in the ranges 63 to 66 percent. In this research an automatic system was developed to choose between an ITE or a BTE hearing aid type with an explanation facility that can be used as a second opinion by audiologist in cases where the choice of an ITE or a BTE hearing aid is not clear cut. This analysis of audiology data and DSS will provide supplementary information for audiology experts and hearing aid dispensers. This type of system may also be of interest to manufacturers of hearing technologies in using as a ready means for their telephone customer services staff to check data, discovering data in audiology records will also be good for general awareness about the suitability of hearing aid type

Unifying the essential concepts of biological networks: biological insights and philosophical foundations

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies

Mining for Social Serendipity

A common social problem at an event in which people do not personally know all of the other participants is the natural tendency for cliques to form and for discussions to mainly happen between people who already know each other. This limits the possibility for people to make interesting new acquaintances and acts as a retarding force in the creation of new links in the social web. Encouraging users to socialize with people they don't know by revealing to them hidden surprising links could help to improve the diversity of interactions at an event. The goal of this paper is to propose a method for detecting "surprising" relationships between people attending an event. By "surprising" relationship we mean those relationships that are not known a priori, and that imply shared information not directly related with the local context of the event (location, interests, contacts) at which the meeting takes place. To demonstrate and test our concept we used the Flickr community. We focused on a community of users associated with a social event (a computer science conference) and represented in Flickr by means of a photo pool devoted to the event. We use Flickr metadata (tags) to mine for user similarity not related to the context of the event, as represented in the corresponding Flickr group. For example, we look for two group members who have been in the same highly specific place (identified by means of geo-tagged photos), but are not friends of each other and share no other common interests or, social neighborhood