An integrative approach for biological data mining and visualisation

Gopalacharyulu, Peddinti; Lindfors, Erno; Miettinen, Jorma K.; Bounsaythip, Catherine; Orešič, Matej

doi:10.1504/ijdmb.2008.016756

Cited by 13 publications

(8 citation statements)

References 51 publications

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“…We constructed an integrated interaction network by combining protein-protein interactions, signal transduction maps and metabolic pathways in mouse as described previously [31], [32]. The integrated network nodes stand for proteins or metabolites, and edges stand for interactions between nodes.…”

Section: Methodsmentioning

confidence: 99%

Detection of Molecular Paths Associated with Insulitis and Type 1 Diabetes in Non-Obese Diabetic Mouse

et al. 2009

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Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells.

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Section: Methodsmentioning

confidence: 99%

Detection of Molecular Paths Associated with Insulitis and Type 1 Diabetes in Non-Obese Diabetic Mouse

et al. 2009

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“…Components of this triad are (of course) not independent; F determines the suitable choice of C; while C, in its turn, operates upon some particular subset of P with definite features. To ensure F is achieved, C engages only certain elements of P. Such triadbased structure is necessary because same physical structures can be subjected to different contexts to achieve different biological goals; for example, same proteins under different set of contexts may be involved in different biological processes, so that the goals of these processes (each different) are achieved (Gopalacharyulu et al 2008). Other application of this triad can be found in (Singh and Banerji 2011).…”

Section: Modelmentioning

confidence: 99%

“…Unlike some previous attempts, the framework proposed here do not tangentially touch upon context-dependence modelling (Standish 2001;Edmonds 1999;Yartseva et al 2007), but concentrates solely on it. On the other hand, it does not attempt to construct a computational structure that helps in retrieval of biological data from some repository in a context-dependent manner (Yu et al 2009;Boeckmann et al 2005), nor does it propose some (effective) visualization tool to observe context-dependent interactions between biological properties (Gopalacharyulu et al 2008).…”

Section: Introductionmentioning

confidence: 99%

An attempt to construct a (general) mathematical framework to model biological “context-dependence”

Banerji

2013

Syst Synth Biol

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Context-dependent nature of biological phenomena is well documented in every branch of biology. While there have been few previous attempts to (implicitly) model various (particular) facets of biological context-dependence, a formal and general mathematical construct to model the wide spectrum of context-dependence, eludes the students of biology. Such an objective model, from both 'bottom-up' as well as 'top-down' perspective, is proposed here to serve as the template to describe the various kinds of context-dependence that we encounter in different branches of biology. Interactions between biological contexts was found to be transitive but non-commutative. It is found that a hierarchical nature of dependence among the biological contexts models the emergent biological properties efficiently. Reasons for these findings are provided in a general model to describe biological reality. Scheme to algorithmically implement the hierarchic structure of organization of biological contexts was proposed with a construct named 'Context tree'. A 'Context tree' based analysis of context interactions among biophysical factors influencing protein structure was performed.

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“…By supporting the high-throughput processing of biological and clinical data, ontologies are a component of the datadriven approach to biomedical research, synergistic with the traditional hypothesis-driven approach [180]. Moreover, data mining often operates on datasets resulting from the integration of heterogeneous resources, also supported by ontologies [181].…”

Section: Knowledge Discoverymentioning

confidence: 99%

Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support

2008

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SummaryObjectives: To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support. Methods: Biomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles. Results: The ontologies under investigation in this survey include SNOMED CT, the Logical Observation Identifiers, Names, and Codes (LOINC), the Foundational Model of Anatomy, the Gene Ontology, RxNorm, the National Cancer Institute Thesaurus, the International Classification of Diseases, the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). The roles played by biomedical ontologies are classified into three major categories: knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies); data integration, exchange and semantic interoperability; and decision support and reasoning (data selection and aggregation, decision support, natural language processing applications, knowledge discovery). Conclusions: Ontologies play an important role in biomedical research through a variety of applications. While ontologies are used primarily as a source of vocabulary for standardization and integration purposes, many applications also use them as a source of computable knowledge. Barriers to the use of ontologies in biomedical applications are discussed.

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An integrative approach for biological data mining and visualisation

Cited by 13 publications

References 51 publications

Detection of Molecular Paths Associated with Insulitis and Type 1 Diabetes in Non-Obese Diabetic Mouse

Detection of Molecular Paths Associated with Insulitis and Type 1 Diabetes in Non-Obese Diabetic Mouse

An attempt to construct a (general) mathematical framework to model biological “context-dependence”

Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support

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