Augmenting Signaling Pathway Reconstructions

Rubel, Tobias; Ritz, Anna

doi:10.1101/2020.06.16.155853

Cited by 4 publications

(8 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reconstruction algorithms are also adapted to overcome this inherent bias toward the nodes and edges in interactomes. For example, heat diffusion and random walk, together with the edge flux calculation, use node degrees for normalization, while PCSF penalizes highly connected proteins ( Creixell et al, 2015 ; Tuncbag et al, 2016a ; Rubel and Ritz, 2020 ). In this way, false-positive edges belonging to hub nodes are excluded from the final subnetwork.…”

Section: Resultsmentioning

confidence: 99%

“…In a recent study, the performance of flux algorithms was shown to exceed the performance of PCSF with default parameters ( Rubel and Ritz, 2020 ). However, the selected set of parameters significantly affects the performance of reconstruction algorithms, especially in PCSF.…”

Section: Discussionmentioning

confidence: 99%

“…Personalized PageRank with flux (PRF) and heat kernel diffusion with flux (HDF) are calculated over deg(v) , which is defined as the number of interactions in G , and node scores 0≤ p(v) ≤1 , which come from PPR or HD. In our study, unlike TieDie and HotNet with heat diffusion algorithms and flux on a random walk with restart, the threshold value is employed to eliminate uncritical nodes ( Vandin et al, 2011 ; Creighton et al, 2013 ; Rubel and Ritz, 2020 ). The related nodes with p(v i )≥1/n where n is the number of nodes in the interactome are considered for subnetwork reconstruction.…”

Section: Methodsmentioning

confidence: 99%

“…Edges are selected by summing flux scores from the highest to the lowest until the targeted flux amount, τxF . The edges having low flux scores are excluded from reconstructed subnetworks ( Rubel and Ritz, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Arici

Tunçbağ

2021

Front. Mol. Biosci.

View full text Add to dashboard Cite

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Arici

Tunçbağ

2021

Front. Mol. Biosci.

View full text Add to dashboard Cite

show abstract

“…Signaling pathways such as Interleukin-9 have been used to assess pathway reconstruction algorithms (e.g. [45,46]) and the group has also used graph-based methods to identify candidate regulators of a non-muscle myosin in Drosophila involved in cellular constriction (e.g. [47]).…”

Section: Graphsmentioning

confidence: 99%

Graphery: Interactive Tutorials for Biological Network Algorithms

Zeng,

Zhang,

Preising

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Networks provide a meaningful way to represent and analyze complex biological information, but the methodological details of network-based tools are often described for a technical audience. GRAPHERY is a hands-on tutorial webserver designed to help biological researchers understand the fundamental concepts behind commonly-used graph algorithms. Each tutorial describes a graph concept along with executable Python code that visualizes the concept in a code view and a graph view. GRAPHERY tutorials help researchers understand graph statistics (such as degree distribution and network modularity) and classic graph algorithms (such as shortest paths and random walks). Users navigate each tutorial using their choice of real-world biological networks, ranging in scale from molecular interaction graphs to ecological networks. GRAPHERY also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Discipline-focused tutorials will be essential to help researchers interpret their biological data. GRAPHERY accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform.

show abstract

Growing DAGs: Optimization Functions for Pathway Reconstruction Algorithms

Ritz

2022

Preprint

Self Cite

View full text Add to dashboard Cite

A major challenge in molecular systems biology is to understand how proteins work to transmit external signals to changes in gene expression. Computationally reconstructing these signaling pathways from protein interaction networks can help understand what is missing from existing pathway databases. We formulate a new pathway reconstruction problem, one that iteratively grows directed acyclic graphs (DAGs) from a set of starting proteins in a protein interaction network. We present an algorithm that provably returns the optimal DAGs for two different cost functions, and show how the pathway reconstructions have different topological features for each cost function when applied to six diverse signaling pathways from the NetPath database. Growing DAGs is a promising step towards reconstructing pathways with specific network topologies.

show abstract

Augmenting Signaling Pathway Reconstructions

Cited by 4 publications

References 38 publications

Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Graphery: Interactive Tutorials for Biological Network Algorithms

Growing DAGs: Optimization Functions for Pathway Reconstruction Algorithms

Contact Info

Product

Resources

About