Accounting for Redundancy when Integrating Gene Interaction Databases

Elefsinioti, Antigoni; Ackermann, Marit; Beyer, Andreas

doi:10.1371/journal.pone.0007492

Cited by 5 publications

(4 citation statements)

References 40 publications

(49 reference statements)

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“…refs. (25) and (26)] and, importantly, that the integrated interaction map present in ConsensusPathDB has not saturated yet. This underlines the importance of further interaction data integration.…”

Section: Database Content Updatementioning

confidence: 99%

The ConsensusPathDB interaction database: 2013 update

Kamburov

Stelzl

Lehrach

et al. 2012

Nucleic Acids Research

736

688

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Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB (http://ConsensusPathDB.org) that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.

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Section: Database Content Updatementioning

confidence: 99%

The ConsensusPathDB interaction database: 2013 update

Kamburov

Stelzl

Lehrach

et al. 2012

Nucleic Acids Research

736

688

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, methods including information on the interactions among gene sets within the single collections have not yet appeared in the literature. Moreover, database redundancy and disagreement are not limited to collections of gene sets, e.g., [18,19]. The impact of the choice of databases has been tackled for specific applications on cancer development in Mubeen et al [20].…”

Section: Related Workmentioning

confidence: 99%

Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets

et al. 2023

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In Genetics, gene sets are grouped in collections concerning their biological function. This often leads to high-dimensional, overlapping, and redundant families of sets, thus precluding a straightforward interpretation of their biological meaning. In Data Mining, it is often argued that techniques to reduce the dimensionality of data could increase the maneuverability and consequently the interpretability of large data. In the past years, moreover, we witnessed an increasing consciousness of the importance of understanding data and interpretable models in the machine learning and bioinformatics communities. On the one hand, there exist techniques aiming to aggregate overlapping gene sets to create larger pathways. While these methods could partly solve the large size of the collections’ problem, modifying biological pathways is hardly justifiable in this biological context. On the other hand, the representation methods to increase interpretability of collections of gene sets that have been proposed so far have proved to be insufficient. Inspired by this Bioinformatics context, we propose a method to rank sets within a family of sets based on the distribution of the singletons and their size. We obtain sets’ importance scores by computing Shapley values; Making use of microarray games, we do not incur the typical exponential computational complexity. Moreover, we address the challenge of constructing redundancy-aware rankings where, in our case, redundancy is a quantity proportional to the size of intersections among the sets in the collections. We use the obtained rankings to reduce the dimension of the families, therefore showing lower redundancy among sets while still preserving a high coverage of their elements. We finally evaluate our approach for collections of gene sets and apply Gene Sets Enrichment Analysis techniques to the now smaller collections: As expected, the unsupervised nature of the proposed rankings allows for unremarkable differences in the number of significant gene sets for specific phenotypic traits. In contrast, the number of performed statistical tests can be drastically reduced. The proposed rankings show a practical utility in bioinformatics to increase interpretability of the collections of gene sets and a step forward to include redundancy-awareness into Shapley values computations.

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“…In order to integrate prior knowledge of measured interactions we combined the Random Forests scores with experimental lines of evidence using Bayesian integration (implemented in R) as described previously (29). This approach also accounts for correlation between individual lines of evidence.…”

Section: Interaction Predictionmentioning

confidence: 99%

Large-scale De Novo Prediction of Physical Protein-Protein Association

Elefsinioti

Saraç

Hegele

et al. 2011

Molecular & Cellular Proteomics

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Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future.Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genomewide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular.

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Accounting for Redundancy when Integrating Gene Interaction Databases

Cited by 5 publications

References 40 publications

The ConsensusPathDB interaction database: 2013 update

The ConsensusPathDB interaction database: 2013 update

Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets

Large-scale De Novo Prediction of Physical Protein-Protein Association

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