Automatic pathway building in biological association networks

Yuryev, Anton; Mulyukov, Zufar; Kotelnikova, Ekaterina; Maslov, Sergei; Egorov, Sergei; Nikitin, Alexander P.; Daraselia, Nikolai; Mazo, Ilya

doi:10.1186/1471-2105-7-171

Cited by 78 publications

(33 citation statements)

References 12 publications

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“…This is a similar approach to that adopted by Yuryev et al [6] in the context of automated signaling pathway construction and Rodríguez-Penagos et al [8] when evaluating the automated reconstruction of a bacterial regulatory network.…”

Section: Methodsmentioning

confidence: 99%

A text-mining system for extracting metabolic reactions from full-text articles

et al. 2012

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BackgroundIncreasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway — metabolic pathways — has been largely neglected.Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein–protein interactions.ResultsWhen evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the well-known protein-protein interaction extraction task.ConclusionsWe conclude that automated metabolic pathway construction is more tractable than has often been assumed, and that (as in the case of protein–protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed.

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Section: Methodsmentioning

confidence: 99%

A text-mining system for extracting metabolic reactions from full-text articles

et al. 2012

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“…For the extraction from published literature the software Pathway Studio V9 provides a gene relation database termed ResNet Mammalian, which has been compiled by automatic extraction of interactions from PubMed, as evaluated by [15]. As shown in the latter publication, gene relations derived from Pathway Studio V9 can be considered of high quality, since in general scientific literature is a reliable resource and the false positive rate is reported to be about 10 % .…”

Section: Methodsmentioning

confidence: 99%

Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0

et al. 2013

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BackgroundInference of gene-regulatory networks (GRNs) is important for understanding behaviour and potential treatment of biological systems. Knowledge about GRNs gained from transcriptome analysis can be increased by multiple experiments and/or multiple stimuli. Since GRNs are complex and dynamical, appropriate methods and algorithms are needed for constructing models describing these dynamics. Algorithms based on heuristic approaches reduce the effort in parameter identification and computation time.ResultsThe NetGenerator V2.0 algorithm, a heuristic for network inference, is proposed and described. It automatically generates a system of differential equations modelling structure and dynamics of the network based on time-resolved gene expression data. In contrast to a previous version, the inference considers multi-stimuli multi-experiment data and contains different methods for integrating prior knowledge. The resulting significant changes in the algorithmic procedures are explained in detail. NetGenerator is applied to relevant benchmark examples evaluating the inference for data from experiments with different stimuli. Also, the underlying GRN of chondrogenic differentiation, a real-world multi-stimulus problem, is inferred and analysed.ConclusionsNetGenerator is able to determine the structure and parameters of GRNs and their dynamics. The new features of the algorithm extend the range of possible experimental set-ups, results and biological interpretations. Based upon benchmarks, the algorithm provides good results in terms of specificity, sensitivity, efficiency and model fit.

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“…In order to better elucidate differences in HPV-positive HNSCC based upon the response to chemoradiation, the microarray data was investigated to identify highly regulated expression pathway sub-networks [8,10] using Ariadne Sub-Network Enrichment Analysis (SNEA). Expression pathway sub-network analysis consists of a single seed and proteins associated to this seed by either regulating expression of/by the seed or by binding to the promoter of/by the seed [11]. Three of the most highly regulated sub-networks between CRs and Post-Tx Fails were built around the interrelated seeds of E2F3, E2F4, and the general E2F family.…”

Section: Sub-network and Pathway Analysismentioning

confidence: 99%

Gene Expression Characterization of HPV Positive Head and Neck Cancer to Predict Response to Chemoradiation

Thibodeau

Geddes

Fortier

et al. 2014

Head and Neck Pathol

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Human papillomavirus (HPV) has been shown to have a causal role in the development of head and neck squamous cell carcinoma. While HPV-positive head and neck cancer is associated with a better response to treatment in the majority of patients, there is a subset who does not respond favorably to current therapy. Identification of these patients could prevent unnecessary morbidity and indicate the need for alternative therapeutic options. Tissue samples were obtained from 19 patients with HPV-positive head and neck squamous carcinoma treated with chemoradiation therapy. HPV status was confirmed by polymerase chain reaction analysis through detection of HPV16 E7 in both DNA and RNA. RNA was isolated from tissue samples and subjected to microarray gene expression analysis. In addition to identification of potential genetic biomarkers (including LCE3D, KRTDAP, HMOX1, KRT19, MDK, TSPAN1), differentially expressed genes associated with genomic stability, cell cycle, and DNA damage were detected between responders and non-responders. These results were further validated with publicly available gene expression studies. This pilot study suggests prospective biomarkers that predict response to therapy. The importance of genes involved with genomic stability is highlighted in both development and progression of head and neck squamous cell carcinoma but also recurrence. Potential development of an assay may prove beneficial to clinicians, assisting them to provide alternative care sooner thus lowering morbidity.

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Automatic pathway building in biological association networks

Cited by 78 publications

References 12 publications

A text-mining system for extracting metabolic reactions from full-text articles

A text-mining system for extracting metabolic reactions from full-text articles

Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0

Gene Expression Characterization of HPV Positive Head and Neck Cancer to Predict Response to Chemoradiation

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