MOTIVATION: Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential. RESULTS: We have integrated 23 transcription datasets, spanning 1350 MB and 291 normal brain samples. To remove batch effects, we combined the Removal of Unwanted Variation (RUV) method with a novel pipeline for determining empirical negative control genes and a panel of metrics to evaluate normalization performance. The documented approach enabled the removal of a majority of batch effects, producing a large-scale, integrative dataset of MB and cerebellar expression data. The proposed strategy will be broadly applicable for accurate integration of data and incorporation of normal reference samples for studies of various diseases. We hope that the integrated dataset will improve current research in the field of MB by allowing more large-scale gene expression analyses. AVAILABILITY AND IMPLEMENTATION: The RUV-normalized expression data is available through the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) and can be accessed via the GSE series number GSE124814. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Cold acclimation involves a number of different cellular processes that together increase the freezing tolerance of an organism. The DREB1/CBFs are transcription factors (TFs) that are prominent in the regulation of cold responses in Arabidopsis thaliana, rice and many other crops. We investigated if the expression of DREB1/CBFs and co-expressed genes relies on combinatorial control by several TFs. Our results support this notion and indicate that methods for studying the regulation of complex cellular processes should include identification of combinations of motifs, in addition to searching for individual overrepresented binding sites.

Information fusion (IF) is a rapidly developing research area which concerns the study of methods to combine the analysis of different data sources in such a way that it increases our understanding of the system under study. The synergistic effects of using multiple data sources and repeatedly updating the model when new data is available, increases the reliability of the model and makes it better suited for e.g. decision support. However, information fusion is a challenging task and more research is needed on how to best integrate data of heterogeneous types and structures in a combined analysis. Initially, IF was mainly used in military contexts, but the algorithms developed are likely to be useful in many other domains. The JDL Data Fusion Model was developed to facilitate IF processes. Here, we investigate its applicability for bioinformatics problems in general and we present an example where it is applied in a study of stem cell differentiation.

The long-term goal of the research presented in this paper is to incorporate linguistic text analysis into a system for evaluation of biological pathways. In this system, relations extracted from biomedical texts will be compared with pathways encoded in existing specialized databases. In this way, the biologist's conclusions regarding the plausibility and/or novelty of a certain relation between genes, proteins, etc., can be supported by fused information from biological databases and biological literature. We aim at overcoming the shortcomings of existing systems for information retrieval by proposing a method based on thorough linguistic analysis of a large text corpus. In this paper, we present a comparative analysis of two corpora: one consisting of biomedical texts from PubMed, the other one of general English prose. The results stress the importance of taking multiword entries into account when constructing a system for extracting biological relations from texts

This paper provides an overview of a collaborative research program in information fusion from databases, sensors and simulations. Information fusion entails the combination of data from multiple sources, to generate information that cannot be derived from the individual sources. This area is of strategic importance for industry and defense, as well as public administration areas such as health care, and needs to be pursued as an academic subject. A large number of industrial partners are supporting and participating in the development of the area. The paper describes the program’s general approach and main research areas, with a particular focus on the role of information fusion in systems development.

This work demonstrates how the efficiency of evolutionary algorithms in dynamic environments can be improved by use of life-time adaptation. Our results contradict the hypothesis that there would be a tradeoff between designing and tuning EAs for static and dynamic environments, in which improved efficiency in one type of environment would decrease the efficiency in the other. In contrast, we show that the inclusion of life-time adaptation can result in EAs that outperform traditional EAs in both static and dynamic environments. Since the performance of EAs with life-time adaptation in dynamic environments are currently poorly understood at best, we conduct an extensive evaluation of the performance of these EAs on combinatorial and continuous dynamic global optimization problems with well-defined characteristics. In doing so, we propose improved benchmark dynamic fitness functions for both the combinatorial and continuous domains, which we have termed random dynamics NK-landscapes and structured moving peaks landscapes, respectively.

We show how prior domain knowledge can be used in a system for mining databases of biological data. Our system performs automated discovery of diagnostic patterns from a database of protein sequences. Such patterns are used for classification of new sequences, and identification of biologically interesting positions in the proteins. The patterns have a simple syntax and can be translated into regular expressions, which can be used for rapid scanning of databases. Current pattern libraries are built semi-manually, since the correctness of the pattern depends on the incorporation of domain knowledge. Due to the dramatic growth of the databases it is desirable to automate this process. Our results show that the patterns derived by our fully automated system compete well with the semi-manually constructed patterns. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.