Abstract
BackgroundAdvances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). This complexity and high interconnectedness call for automated techniques that can identify smaller targeted subnetworks specific to a given research context (e.g. a disease scenario).ResultsOur method, named ChainRank, finds relevant subnetworks by identifying and scoring chains of interactions that link specific network components. Scores can be generated from integrating multiple general and context specific measures (e.g. experimental molecular data from expression to proteomics and metabolomics, literature evidence, network topology). The performance of the novel ChainRank method was evaluated on recreating selected signalling pathways from a human protein interaction network. Specifically, we recreated skeletal muscle specific signaling networks in healthy and chronic obstructive pulmonary disease (COPD) contexts. The analysis showed that ChainRank can identify main mediators of context specific molecular signalling. An improvement of up to factor 2.5 was shown in the precision of finding proteins of the recreated pathways compared to random simulation.ConclusionsChainRank provides a framework, which can integrate several user-defined scores and evaluate their combined effect on ranking interaction chains linking input data sets. It can be used to contextualise networks, identify signaling and regulatory path amongst targeted genes or to analyse synthetic lethality in the context of anticancer therapy. ChainRank is implemented in R programming language and freely available at https://github.com/atenyi/ChainRank.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0864-x) contains supplementary material, which is available to authorized users.
Highlights
Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data
We introduced three different strategies to combine the scores: (i) Combined scores are calculated as the weighted product of the normalized element scores mapped to a node, using the formula ck = ∑jnwjskj, where ck is the combined score of the kth node, n is the number of scores, sj is the jth element score normalized to the range [0,1] and wj is the weight corresponding to the jth score, (ii) the filtering strategy pre-filters the chains using a threshold for the score s1, and it re-ranks the filtered chains with score s2 and (iii) the intersection strategy keeps only those chains that are under a specified threshold for all the selected scores
We specified the domain of interest to muscle dysfunctions in chronic obstructive pulmonary disease (COPD) because of its specificity to a distinctive tissue, its clinical relevance as well as the wealth of literature mining and experimental data available for our analysis [20]
Summary
Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. These networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). Canonical pathways are widely used tools to represent signal transduction and molecular networks They generally rely on literature-based information, mostly derived from hypothesis-driven experiments collected in exceedingly diverse contexts, encompassing a large variety of experimental conditions (e.g. different species, cell-types/tissues, diseases) and/or in-vitro models. It is currently apparent that the classical pathway approach is too simplistic to properly describe complex cellular events [7,8,9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
