ResponseNet v.3: revealing signaling and regulatory pathways connecting your proteins and genes across human tissues

Basha, Omer; Mauer, Omry; Simonovsky, Eyal; Shpringer, Rotem; Yeger-Lotem, Esti

doi:10.1093/nar/gkz421

Cited by 12 publications

(10 citation statements)

References 52 publications

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“…Our distance metric focuses only on topology and does not include any information about the biological context. ResponseNet (Basha et al, 2019) and PathLinker (Youssef et al, 2018) extensions consider tissue-specificity and protein localization context, respectively. A possible extension of pathway parameter advising would be to account for this information, such as adding a penalty for interactions that occur in different tissues or cellular compartments.…”

Section: Discussionmentioning

confidence: 99%

Automating parameter selection to avoid implausible biological pathway models

Magnano

Gitter

2019

Preprint

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A common way to integrate and analyze large amounts of biological omic data is through pathway finding: creating a subnetwork of a generic background network that represents some process or cellular state using condition-specific omic data. A challenge in pathway finding is that adjusting pathway creation algorithms' parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway finding, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising method to tune pathway finding algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway finding algorithms. We also demonstrate how pathway parameter advising can guide construction of an influenza host factor network. Pathway parameter advising is method-agnostic; it is applicable to any pathway finding algorithm with tunable parameters. Our pathway parameter advising software is available on GitHub at https://github.com/gitter-lab/pathway-parameter-advising.

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Section: Discussionmentioning

confidence: 99%

Automating parameter selection to avoid implausible biological pathway models

Magnano

Gitter

2019

Preprint

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“…ResponseNet (RN) [9,10]: This method formulates the pathway reconstruction problem as a network flow algorithm, and presents a linear program to find a subgraph that balances outgoing flow from the sources and incoming flow to the targets. RN requires a sparsity parameter γ ∈ R + that penalizes flow through multiple sources (by default γ = 20).…”

Section: Pathway Reconstruction Methodsmentioning

confidence: 99%

“…PathLinker [19] PL number of shortest paths k ResponseNet [9,10] RN sparsity parameter γ BowTieBuilder [26] BTB Prize Collecting PCSF terminal prize p Steiner Forest [7,8] edge reliability b dummy edge weight ω degree penalty g Random Walk with RWR teleportation probability α Restarts (insp. by [11]) flux threshold τ Shortest Paths SP Table 1.…”

Section: Methods Namementioning

confidence: 99%

“…Another area of signaling pathway research develops new methods that can predict new players in signaling pathways of interest. Many of these approaches generate new predictions by integrating protein-protein interaction data with gene [6][7][8][9][10][11] or protein [12][13][14][15] expression. Other approaches work to remove biologically implausible predictions [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Augmenting Signaling Pathway Reconstructions

Rubel

Ritz

2020

Preprint

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Signaling pathways drive cellular response, and understanding such pathways is fundamental to molecular systems biology. A mounting volume of experimental protein interaction data has motivated the development of algorithms to computationally reconstruct signaling pathways. However, existing methods suffer from low recall in recovering protein interactions in ground truth pathways, limiting our confidence in any new predictions for experimental validation. We present the Pathway Reconstruction AUGmenter (PRAUG), a higher-order function for producing high-quality pathway reconstruction algorithms. PRAUG modifies any existing pathway reconstruction method, resulting in augmented algorithms that outperform their un-augmented counterparts for six different algorithms across twenty-nine diverse signaling pathways. The algorithms produced by PRAUG collectively reveal potential new proteins and interactions involved in the Wnt and Notch signaling pathways. PRAUG offers a valuable framework for signaling pathway prediction and discovery.

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“…Another area of signaling pathway research develops new methods that can predict new players in signaling pathways of interest. Many of these approaches generate new predictions by integrating protein-protein interaction data with gene [7,12,21,21,29,30,33] or protein [4,15,19,24] expression. Other approaches work to remove biologically implausible predictions [20,34].…”

mentioning

confidence: 99%