1. SegMine workflows for semantic microarray data analysis in Orange4WSVid Podpečan, Nada Lavrač, Igor Mozetič, Petra Kralj Novak, Igor Trajkovski, Laura Langohr, Kimmo Kulovesi, Hannu Toivonen, Marko Petek, Helena Motaln, Kristina Gruden, 2011, original scientific article Abstract: 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.
Published in DiRROS: 05.08.2024; Views: 118; Downloads: 86
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2. Analysis of glioblastoma patients' plasma revealed the presence of microRNAs with a prognostic impact on survival and those of viral originKlemen Zupančič, Helena Motaln, Miomir Knežević, Urška Verbovšek, Marjan Koršič, Tamara Lah Turnšek, Primož Rožman, Matjaž Jeras, Matjaž Hren, Kristina Gruden, Andrej Blejec, Matija Veber, Ana Herman, Andrej Porčnik, Vid Podpečan, 2015, original scientific article Abstract: Background
Glioblastoma multiforme (GBM) is among the most aggressive cancers with a poor prognosis in spite of a plethora of established diagnostic and prognostic biomarkers and treatment modalities. Therefore, the current goal is the detection of novel biomarkers, possibly detectable in the blood of GBM patients that may enable an early diagnosis and are potential therapeutic targets, leading to more efficient interventions.
Experimental Procedures
MicroRNA profiling of 734 human and human-associated viral miRNAs was performed on blood plasma samples from 16 healthy individuals and 16 patients with GBM, using the nCounter miRNA Expression Assay Kits.
Results
We identified 19 miRNAs with significantly different plasma levels in GBM patients, compared to the healthy individuals group with the difference limited by a factor of 2. Additionally, 11 viral miRNAs were found differentially expressed in plasma of GBM patients and 24 miRNA levels significantly correlated with the patients’ survival. Moreover, the overlap between the group of candidate miRNAs for diagnostic biomarkers and the group of miRNAs associated with survival, consisted of ten miRNAs, showing both diagnostic and prognostic potential. Among them, hsa miR 592 and hsa miR 514a 3p have not been previously described in GBM and represent novel candidates for selective biomarkers. The possible signalling, induced by the revealed miRNAs is discussed, including those of viral origin, and in particular those related to the impaired immune response in the progression of GBM.
Conclusion
The GBM burden is reflected in the alteration of the plasma miRNAs pattern, including viral miRNAs, representing the potential for future clinical application. Therefore proposed biomarker candidate miRNAs should be validated in a larger study of an independent cohort of patients
Keywords: microRNAs, glioblastoma multiforme, biomarkers, RNA extraction, viral disease diagnosis
Published in DiRROS: 26.07.2024; Views: 147; Downloads: 67
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3. Interactive exploration of heterogeneous biological networks with Biomine ExplorerVid Podpečan, Živa Ramšak, Kristina Gruden, Hannu Toivonen, Nada Lavrač, 2019, original scientific article Abstract: 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.
Keywords: biological networks, bioinformatic
Published in DiRROS: 23.07.2024; Views: 148; Downloads: 87
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4. Stress knowledge map : a knowledge graph resource for systems biology analysis of plant stress responsesCarissa Bleker, Živa Ramšak, Andras Bittner, Vid Podpečan, Maja Zagorščak, Bernhard Wurzinger, Špela Baebler, Marko Petek, Maja Križnik, Anže Županič, Kristina Gruden, 2024, original scientific article Abstract: Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical, signaling, and regulatory molecular interactions in plants: a highly curated model of plant stress signaling (PSS; 543 reactions) and a large comprehensive knowledge network (488 390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entry point for investigations of plant stress response and related growth trade-offs, as well as interactive explorations of current knowledge. PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation and design of validation experiments, or to gain new insights into experimental observations in plant biology.
Keywords: Stress knowledge map, knowledge graph, knowledge network, entry point, plant digital twin, plant stress responses, plant signaling, systems biology
Published in DiRROS: 11.06.2024; Views: 250; Downloads: 126
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