Plant Pathway Databases

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease with a prevalence of 1 in 500 people and varying clinical presentations. Although there is much research on HCM, underlying molecular mechanisms are poorly understood, and research on the molecular mechanisms of its specific clinical presentations is scarce. Our aim was to explore the molecular mechanisms shared by HCM and its clinical presentations through the automated extraction of molecular mechanisms. Molecular mechanisms were congregated by a query of the INDRA database, which aggregates knowledge from pathway databases and combines it with molecular mechanisms extracted from abstracts and open-access full articles by multiple machine-reading systems. The molecular mechanisms were extracted from 230,072 articles on HCM and 19 HCM clinical presentations, and their intersections were found. Shared molecular mechanisms of HCM and its clinical presentations were represented as networks; the most important elements in the intersections’ networks were found, centrality scores for each element of each network calculated, networks with reduced level of noise generated, and cooperatively working elements detected in each intersection network. The identified shared molecular mechanisms represent possible mechanisms underlying different HCM clinical presentations. Applied methodology produced results consistent with the information in the scientific literature.

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Using metabolic pathway databases for functional annotation

Trends in Genetics ◽

10.1016/s0168-9525(98)01523-6 ◽

1998 ◽

Vol 14 (8) ◽

pp. 332-333 ◽

Cited By ~ 9

Author(s):

Michael Y Galperin ◽

Steven E Brenner

Keyword(s):

Metabolic Pathway ◽

Functional Annotation ◽

Pathway Databases

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Cytotoxic Effect of RecombinantMycobacterium tuberculosisCFP-10/ESAT-6 Protein on the Crucial Pathways of WI-38 Cells

Journal of Biomedicine and Biotechnology ◽

10.1155/2009/917084 ◽

2009 ◽

Vol 2009 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

Kun-Nan Tsai ◽

Err-Cheng Chan ◽

Tsung-Yeh Tsai ◽

Kuei-Tien Chen ◽

Chun-Yu Chen ◽

...

Keyword(s):

Focal Adhesion ◽

Microarray Data ◽

Cytotoxic Effect ◽

Time Course ◽

Essential Role ◽

Topological Properties ◽

Regulatory Effect ◽

Qrt Pcr ◽

Focal Adhesion Pathway ◽

Pathway Databases

To unravel the cytotoxic effect of the recombinant CFP-10/ESAT-6 protein (rCFES) on WI-38 cells, an integrative analysis approach, combining time-course microarray data and annotated pathway databases, was proposed with the emphasis on identifying the potentially crucial pathways. The potentially crucial pathways were selected based on a composite criterion characterizing the average significance and topological properties of important genes. The analysis results suggested that the regulatory effect of rCFES was at least involved in cell proliferation, cell motility, cell survival, and metabolisms of WI-38 cells. The survivability of WI-38 cells, in particular, was significantly decreased to 62% with 12.5 μMrCFES. Furthermore, the focal adhesion pathway was identified as the potentially most-crucial pathway and 58 of 65 important genes in this pathway were downregulated by rCFES treatment. Using qRT-PCR, we have confirmed the changes in the expression levels of LAMA4, PIK3R3, BIRC3, and NFKBIA, suggesting that these proteins may play an essential role in the cytotoxic process in the rCFES-treated WI-38 cells.

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Using set theory to reduce redundancy in pathway sets

10.1101/319731 ◽

2018 ◽

Author(s):

Ruth Stoney ◽

Jean-Mark Schwartz ◽

David L Robertson ◽

Goran Nenadic

Keyword(s):

Size Control ◽

Original Data ◽

Set Cover ◽

Biological Knowledge ◽

Original Form ◽

Data Set ◽

Gene Set ◽

Alternative Approach ◽

Gene Coverage ◽

Pathway Databases

1.Abstract1.01BackgroundThe consolidation of pathway databases, such as KEGG[1], Reactome[2]and ConsensusPathDB[3], has generated widespread biological interest, however the issue of pathway redundancy impedes the use of these consolidated datasets. Attempts to reduce this redundancy have focused on visualizing pathway overlap or merging pathways, but the resulting pathways may be of heterogeneous sizes and cover multiple biological functions. Efforts have also been made to deal with redundancy in pathway data by consolidating enriched pathways into a number of clusters or concepts. We present an alternative approach, which generates pathway subsets capable of covering all of genes presented within either pathway databases or enrichment results, generating substantial reductions in redundancy.1.02ResultsWe propose a method that uses set cover to reduce pathway redundancy, without merging pathways. The proposed approach considers three objectives: removal of pathway redundancy, controlling pathway size and coverage of the gene set. By applying set cover to the ConsensusPathDB dataset we were able to produce a reduced set of pathways, representing 100% of the genes in the original data set with 74% less redundancy, or 95% of the genes with 88% less redundancy. We also developed an algorithm to simplify enrichment data and applied it to a set of enriched osteoarthritis pathways, revealing that within the top ten pathways, five were redundant subsets of more enriched pathways. Applying set cover to the enrichment results removed these redundant pathways allowing more informative pathways to take their place.1.03ConclusionOur method provides an alternative approach for handling pathway redundancy, while ensuring that the pathways are of homogeneous size and gene coverage is maximised. Pathways are not altered from their original form, allowing biological knowledge regarding the data set to be directly applicable. We demonstrate the ability of the algorithms to prioritise redundancy reduction, pathway size control or gene set coverage. The application of set cover to pathway enrichment results produces an optimised summary of the pathways that best represent the differentially regulated gene set.

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Pathway Databases

Foundations of Systems Biology - Computational Biology ◽

10.1007/978-1-84882-023-4_2 ◽

2009 ◽

pp. 5-18 ◽

Cited By ~ 1

Author(s):

Masao Nagasaki ◽

Ayumu Saito ◽

Atsushi Doi ◽

Hiroshi Matsuno ◽

Satoru Miyano

Keyword(s):

Pathway Databases

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Biomolecular Pathway Databases

Methods in Molecular Biology - Data Mining Techniques for the Life Sciences ◽

10.1007/978-1-60327-241-4_8 ◽

2009 ◽

pp. 129-144 ◽

Cited By ~ 16

Author(s):

Hong Sain Ooi ◽

Georg Schneider ◽

Teng-Ting Lim ◽

Ying-Leong Chan ◽

Birgit Eisenhaber ◽

...

Keyword(s):

Pathway Databases

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Knowledge-Driven, Data-Assisted Integrative Pathway Analytics

Handbook of Research on Computational and Systems Biology ◽

10.4018/978-1-60960-491-2.ch010 ◽

2011 ◽

pp. 225-247

Author(s):

Padmalatha S. Reddy ◽

Stuart Murray ◽

Wei Liu

Keyword(s):

Data Analysis ◽

Drug Discovery ◽

Text Mining ◽

Network Architecture ◽

Data Analytics ◽

Efficient Manner ◽

Omics Technologies ◽

Functional Process ◽

Pathway Databases ◽

Biomarker Selection

Target and biomarker selection in drug discovery relies extensively on the use of various genomics platforms. These technologies generate large amounts of data that can be used to gain novel insights in biology. There is a strong need to mine these information-rich datasets in an effective and efficient manner. Pathway and network based approaches have become an increasingly important methodology to mine bioinformatics datasets derived from ‘omics’ technologies. These approaches also find use in exploring the unknown biology of a disease or functional process. This chapter provides an overview of pathway databases and network tools, network architecture, text mining and existing methods used in knowledge-driven data analysis. It shows examples of how these databases and tools can be used integratively to apply existing knowledge and network-based approach in data analytics.

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Predictive modelling using pathway scores: robustness and significance of pathway collections

BMC Bioinformatics ◽

10.1186/s12859-019-3163-0 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Marcelo P. Segura-Lepe ◽

Hector C. Keun ◽

Timothy M. D. Ebbels

Keyword(s):

Gene Expression ◽

Prediction Models ◽

Disease Status ◽

Predictive Modelling ◽

Data Set ◽

Prediction Rules ◽

Similar Accuracy ◽

Robust To Noise ◽

Pathway Databases ◽

Gene Expression Levels

Abstract Background Transcriptomic data is often used to build statistical models which are predictive of a given phenotype, such as disease status. Genes work together in pathways and it is widely thought that pathway representations will be more robust to noise in the gene expression levels. We aimed to test this hypothesis by constructing models based on either genes alone, or based on sample specific scores for each pathway, thus transforming the data to a ‘pathway space’. We progressively degraded the raw data by addition of noise and examined the ability of the models to maintain predictivity. Results Models in the pathway space indeed had higher predictive robustness than models in the gene space. This result was independent of the workflow, parameters, classifier and data set used. Surprisingly, randomised pathway mappings produced models of similar accuracy and robustness to true mappings, suggesting that the success of pathway space models is not conferred by the specific definitions of the pathway. Instead, predictive models built on the true pathway mappings led to prediction rules with fewer influential pathways than those built on randomised pathways. The extent of this effect was used to differentiate pathway collections coming from a variety of widely used pathway databases. Conclusions Prediction models based on pathway scores are more robust to degradation of gene expression information than the equivalent models based on ungrouped genes. While models based on true pathway scores are not more robust or accurate than those based on randomised pathways, true pathways produced simpler prediction rules, emphasizing a smaller number of pathways.

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