NetCore: a network propagation approach using node coreness

Barel, Gal; Herwig, Ralf

doi:10.1093/nar/gkaa639

Cited by 26 publications

(29 citation statements)

References 91 publications

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“…In other words, the first group consists of datasets where the vertex scores X v did not seem to add much value compared to using only the network G in identifying reference genes. Interestingly, this group includes schizophrenia, a disease which [94, 66] specifically highlight as a case where network propagation outperformed the scores-only baseline in AUROC. The second group consists of two diseases (Crohn’s disease, coronary artery disease) where the scores-only AUPRC was comparable to (or larger than) the network propagation AUPRC.…”

Section: Resultsmentioning

confidence: 99%

“…TieDIE [65] propagates two sets of vertex scores and aims to find high-scoring subnetworks for both sets of propagated scores. More recently, the NetCore algorithm [66] finds subnetworks whose vertices have large node “coreness” and large propagated scores. However, none of these approaches give an explicit definition of the subnetwork family, instead relying on heuristics to identify the altered subnetwork after performing network propagation.…”

Section: Introductionmentioning

confidence: 99%

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NetMix2: Unifying network propagation and altered subnetworks

Chitra

Park

Raphael

2022

Preprint

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A standard paradigm in computational biology is to use interaction networks to analyze high-throughput biological data. Two common approaches for leveraging interaction networks are: (1) network ranking, where one ranks vertices in the network according to both vertex scores and network topology; (2) altered subnetwork identification, where one identifies one or more subnetworks in an interaction network using both vertex scores and network topology. The dominant approach in network ranking is network propagation which smooths vertex scores over the network using a random walk or diffusion process, thus utilizing the global structure of the network. For altered subnetwork identification, existing algorithms either restrict solutions to subnetworks in subnetwork families with simple topological constraints, such as connected subnetworks, or utilize ad hoc heuristics that lack a rigorous statistical foundation. In this work, we unify the network propagation and altered subnetwork approaches. We derive a subnetwork family which we call the propagation family that approximates the subnetworks ranked highly by network propagation. We introduce NetMix2, a principled algorithm for identifying altered subnetworks from a wide range of subnetwork families, including the propagation family, thus combining the advantages of the network propagation and altered subnetwork approaches. We show that NetMix2 outperforms network propagation on data simulated using the propagation family. Furthermore, NetMix2 outperforms other methods at recovering known disease genes in pan-cancer somatic mutation data and in genome-wide association data from multiple human diseases. NetMix2 is publicly available at https://github.com/raphael-group/netmix2.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NetMix2: Unifying network propagation and altered subnetworks

Chitra

Park

Raphael

2022

Preprint

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“…Node degree bias in network propagation can be reduced by either better controlling the hubs in the propagation step or taking into account more robust metrics in the re-ranking process. For this purpose, we have developed the network propagation method NetCore ( 67 ). NetCore uses the node core as an alternative node property instead of node degree to conduct the propagation of the experimental weights, which has been found to be more robust against the influence of hubs.…”

Section: Integrating Ppis For Network-based Inferencesmentioning

confidence: 99%

ConsensusPathDB 2022: molecular interactions update as a resource for network biology

Kamburov

Herwig

2021

Nucleic Acids Research

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Molecular interactions are key drivers of biological function. Providing interaction resources to the research community is important since they allow functional interpretation and network-based analysis of molecular data. ConsensusPathDB (http://consensuspathdb.org) is a meta-database combining interactions of diverse types from 31 public resources for humans, 16 for mice and 14 for yeasts. Using ConsensusPathDB, researchers commonly evaluate lists of genes, proteins and metabolites against sets of molecular interactions defined by pathways, Gene Ontology and network neighborhoods and retrieve complex molecular neighborhoods formed by heterogeneous interaction types. Furthermore, the integrated protein–protein interaction network is used as a basis for propagation methods. Here, we present the 2022 update of ConsensusPathDB, highlighting content growth, additional functionality and improved database stability. For example, the number of human molecular interactions increased to 859 848 connecting 200 499 unique physical entities such as genes/proteins, metabolites and drugs. Furthermore, we integrated regulatory datasets in the form of transcription factor–, microRNA– and enhancer–gene target interactions, thus providing novel functionality in the context of overrepresentation and enrichment analyses. We specifically emphasize the use of the integrated protein–protein interaction network as a scaffold for network inferences, present topological characteristics of the network and discuss strengths and shortcomings of such approaches.

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“…Network propagation allows combining experimental data with molecular interaction networks, such that the topology of the network is used to propagate the data effects throughout the network, and by that amplifying and functionally interpreting the experimental data. This approach covers a wide range of data domains and has been applied, for example, to associate genetic variants with phenotypic disease traits ( Barel and Herwig, 2020 ; Leiserson et al., 2015 ).…”

Section: Introductionmentioning

confidence: 99%

A gradient tree boosting and network propagation derived pan-cancer survival network of the tumor microenvironment

Thedinga

Herwig

2022

iScience

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Predicting cancer survival from molecular data is an important aspect of biomedical research because it allows quantifying patient risks and thus individualizing therapy. We introduce XGBoost tree ensemble learning to predict survival from transcriptome data of 8,024 patients from 25 different cancer types and show highly competitive performance with state-of-the-art methods. To further improve plausibility of the machine learning approach we conducted two additional steps. In the first step, we applied pan-cancer training and showed that it substantially improves prognosis compared with cancer subtype-specific training. In the second step, we applied network propagation and inferred a pan-cancer survival network consisting of 103 genes. This network highlights cross-cohort features and is predictive for the tumor microenvironment and immune status of the patients. Our work demonstrates that pan-cancer learning combined with network propagation generalizes over multiple cancer types and identifies biologically plausible features that can serve as biomarkers for monitoring cancer survival.

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NetCore: a network propagation approach using node coreness

Cited by 26 publications

References 91 publications

NetMix2: Unifying network propagation and altered subnetworks

NetMix2: Unifying network propagation and altered subnetworks

ConsensusPathDB 2022: molecular interactions update as a resource for network biology

A gradient tree boosting and network propagation derived pan-cancer survival network of the tumor microenvironment

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