Lung cancer has the highest mortality rate of all of the cancers in the world and asbestos-related lung cancer is one of the leading occupational cancers. The identification of asbestos-related molecular changes has long been a topic of increasing research interest. The aim of this study was to identify novel asbestos-related molecular correlates by integrating miRNA expression profiling with previously obtained profiling data (aCGH and mRNA expression) from the same patient material. miRNA profiling was performed on 26 tumor and corresponding normal lung tissue samples from highly asbestos-exposed and non-exposed patients, and on eight control lung tissue samples. Data analyses on miRNA expression, and integration of miRNA and previously obtained mRNA data were performed using Chipster. A separate analysis was used to integrate miRNA and previously obtained aCGH data. Both known and new lung cancer-associated miRNAs and target genes with inverse correlation were discovered. Furthermore, DNA copy number alterations (e.g., gain at 12p13.31) were correlated with the deregulated miRNAs. Specifically, thirteen novel asbestos-related miRNAs (over-expressed: miR-148b, miR-374a, miR-24-1*, Let-7d, Let-7e, miR-199b-5p, miR-331-3p, and miR-96 and under-expressed: miR-939, miR-671-5p, miR-605, miR-1224-5p and miR-202) and inversely correlated target genes (e.g., GADD45A, LTBP1, FOSB, NCALD, CACNA2D2, MTSS1, EPB41L3) were identified. In addition, over-expression of the well known squamous cell carcinoma-associated miR-205 was linked to down-regulation of the DOK4 gene. The miRNAs/genes presented here may represent interesting targets for further investigation and could eventually have potential diagnostic implications.

The complexity of ecosystems is staggering, with hundreds or thousands of species interacting in a number of ways from competition and predation to facilitation and mutualism. Understanding the networks that form the systems is of growing importance, e.g. to understand how species will respond to climate change, or to predict potential knock-on effects of a biological control agent. In recent years, a variety of summary statistics for characterising the global and local properties of such networks have been derived, which provide a measure for gauging the accuracy of a mathematical model for network formation processes. However, the critical underlying assumption is that the true network is known. This is not a straightforward task to accomplish, and typi- * Corresponding author. Tel: +44 (0) cally requires minute observations and detailed field work. More importantly, knowledge about species interactions is restricted to specific kinds of interactions. For instance, while the interactions between pollinators and their host plants are amenable to direct observation, other types of species interactions, like those mentioned above, are not, and might not even be clearly defined from the outset. To discover information about complex ecological systems efficiently, new tools for inferring the structure of networks from field data are needed. In the present study, we investigate the viability of various statistical and machine learning methods recently applied in molecular systems biology: graphical Gaussian models, L1-regularised regression with least absolute shrinkage and selection operator (LASSO), sparse Bayesian regression and Bayesian networks. We have assessed the performance of these methods on data simulated from food webs of known structure, where we combined a niche model with a stochastic population model in a 2-dimensional lattice.We assessed the network reconstruction accuracy in terms of the area under the receiver operator characteristics (ROC) curve, which was typically in the range between 0.75 and 0.9, corresponding to the recovery of about 60% of the true species interactions at a false prediction rate of 5%. We also applied the models to presence/absence data for 39 European warblers, and found that the inferred species interactions showed a weak yet significant correlation with phylogenetic similarity scores, which tended to weakly increase when including bio-climate covariates and allowing for spatial autocorrelation. Our findings demonstrate that relevant patterns in ecological networks can be identified from large-scale spatial data sets with machine learning methods, and that these methods have the potential to contribute novel important tools for gaining deeper insight into the structure and stability of ecosystems.

Motivation: As ArrayExpress and other repositories of genome-wide experiments are reaching a mature size, it is becoming more meaningful to search for related experiments, given a particular study. We introduce methods that allow for the search to be based upon measurement data, instead of the more customary annotation data. The goal is to retrieve experiments in which the same biological processes are activated. This can be due either to experiments targeting the same biological question, or to as yet unknown relationships.Results: We use a combination of existing and new probabilistic machine learning techniques to extract information about the biological processes differentially activated in each experiment, to retrieve earlier experiments where the same processes are activated and to visualize and interpret the retrieval results. Case studies on a subset of ArrayExpress show that, with a sufficient amount of data, our method indeed finds experiments relevant to particular biological questions. Results can be interpreted in terms of biological processes using the visualization techniques.Availability: The code is available from http://www.cis.hut.fi/projects/mi/software/ismb09.Contact: jose.caldas@tkk.fi