SUMMARY: Biomine Explorer is a web application that enables interactive exploration of large heterogeneous biological networks constructed from selected publicly available biological knowledge sources. It is built on top of Biomine, a system which integrates cross-references from several biological databases into a large heterogeneous probabilistic network. Biomine Explorer offers user-friendly interfaces for search, visualization, exploration and manipulation as well as public and private storage of discovered subnetworks with permanent links suitable for inclusion into scientific publications. A JSON-based web API for network search queries is also available for advanced users. AVAILABILITY AND IMPLEMENTATION: Biomine Explorer is implemented as a web application, which is publicly available at https://biomine.ijs.si. Registration is not required but registered users can benefit from additional features such as private network repositories.

The paper presents an approach to the holistic analysis of transcriptomic data which integrates two state-of-the-art methodologies into a coherent framework. The aim of the proposed approach is to give insight into the discovered patterns, help explaining the observed phenomena, enable the creation of new research hypotheses and assist in design of new experiments. We have integrated a methodology for semantic analysis of transcriptomic data, a system for automated extraction of biological relations from the literature, and a number of supporting components. The approach is demonstrated and evaluated on a publicly available dataset from a clinical trial in acute lymphoblastic leukaemia and a document corpus of full-text articles from the PubMed Open Access Subset.

ClowdFlows is an open cloud based platform for composition, execution, and sharing of interactive data mining workflows. In this paper we extend the ClowdFlows platform with the ability to mine real-time data streams. This functionality was implemented by creating a specialized type of workflow component and a stream mining daemon that delegates the execution of workflows in real-time. In this way, we have transformed a batch data processing platform into a real-time stream mining platform with an intuitive user interface. The real-time analytics aspect of the platform is demonstrated in a Twitter sentiment analysis use case where the sentiment of tweets about whistleblower Edward Snowden was monitored for approximately one month.

Subgroup discovery methods find interesting subsets of objects of a given class. Motivated by an application in bioinformatics, we first define a generalized subgroup discovery problem. In this setting, a subgroup is interesting if its members are characteristic for their class, even if the classes are not identical. Then we further refine this setting for the case where subsets of objects, for example, subsets of objects that represent different time points or different phenotypes, are contrasted. We show that this allows finding subgroups of objects that could not be found with classical subgroup discovery. To find such subgroups, we propose an approach that consists of two subgroup discovery steps and an intermediate, contrast set definition step. This approach is applicable in various application areas. An example is biology, where interesting subgroups of genes are searched by using gene expression data. We address the problem of finding enriched gene sets that are specific for virus infected samples for a specific time point or a specific phenotype. We report on experimental results on a time series data set for virus infected Solanum tuberosum (potato) plants. The results on S. tuberosum’s response to virus infection revealed new research hypotheses for plant biologists.

Novel data-mining tasks in e-science involve mining of distributed, highly heterogeneous data and knowledge sources. However, standard data mining platforms, such as Weka and Orange, involve only their own data mining algorithms in the process of knowledge discovery from local data sources. In contrast, next generation data mining technologies should enable processing of distributed data sources, the use of data mining algorithms implemented as web services, as well as the use of formal descriptions of data sources and knowledge discovery tools in the form of ontologies, enabling automated composition of complex knowledge discovery workflows for a given data mining task. This paper proposes a novel Service-oriented Knowledge Discovery framework and its implementation in a service-oriented data mining environment Orange4WS (Orange for Web Services), based on the existing Orange data mining toolbox and its visual programming environment, which enables manual composition of data mining workflows. The new service-oriented data mining environment Orange4WS includes the following new features: simple use of web services as remote components that can be included into a data mining workflow; simple incorporation of relational data mining algorithms; a knowledge discovery ontology to describe workflow components (data, knowledge and data mining services) in an abstract and machine-interpretable way, and its use by a planner that enables automated composition of data mining workflows. These new features are showcased in three real-world scenarios.

BACKGROUND: In experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments can be significantly increased by offering advanced data mining services as workflow components. Such services can support, for instance, knowledge discovery from diverse distributed data and knowledge sources (such as GO, KEGG, PubMed, and experimental databases). Specifically, cutting-edge data analysis approaches, such as semantic data mining, link discovery, and visualization, have not yet been made available to researchers investigating complex biological datasets. RESULTS: We present a new methodology, SegMine, for semantic analysis of microarray data by exploiting general biological knowledge, and a new workflow environment, Orange4WS, with integrated support for web services in which the SegMine methodology is implemented. The SegMine methodology consists of two main steps. First, the semantic subgroup discovery algorithm is used to construct elaborate rules that identify enriched gene sets. Then, a link discovery service is used for the creation and visualization of new biological hypotheses. The utility of SegMine, implemented as a set of workflows in Orange4WS, is demonstrated in two microarray data analysis applications. In the analysis of senescence in human stem cells, the use of SegMine resulted in three novel research hypotheses that could improve understanding of the underlying mechanisms of senescence and identification of candidate marker genes. CONCLUSIONS: Compared to the available data analysis systems, SegMine offers improved hypothesis generation and data interpretation for bioinformatics in an easy-to-use integrated workflow environment.