ArticleJournal of computational biology : a journal of computational molecular cell biology2013
Top-down network analysis to drive bottom-up modeling of physiological processes.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- A Framework for Benchmarking Pathway Reconstruction Algorithms.bioRxiv : the preprint server for biology · 2026Article
- Discovering anticancer drug target combinations via network-informed signaling-based approach.Communications medicine · 2025Article
- Anticancer Target Combinations: Network-Informed Signaling-Based Approach to Discovery.bioRxiv : the preprint server for biology · 2024Article
- Computational Modeling and Reverse Engineering to Reveal Dominant Regulatory Interactions Controlling Osteochondral Differentiation: Potential for Regenerative Medicine.Frontiers in bioengineering and biotechnology · 2018Review
- GraphSpace: stimulating interdisciplinary collaborations in network biology.Bioinformatics (Oxford, England) · 2017Article
- Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles.BMC bioinformatics · 2017Article
- Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways.Molecular & cellular proteomics : MCP · 2016Article
- Signaling hypergraphs.Trends in biotechnology · 2014Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Top-down analyses in systems biology can automatically find correlations among genes and proteins in large-scale datasets. However, it is often difficult to design experiments from these results. In contrast, bottom-up approaches painstakingly craft detailed models that can be simulated computationally to suggest wet lab experiments. However, developing the models is a manual process that can take many years. These approaches have largely been developed independently. We present LINKER, an efficient and automated data-driven method that can analyze molecular interactomes to propose extensions to models that can be simulated. LINKER combines teleporting random walks and k-shortest path computations to discover connections from a source protein to a set of proteins collectively involved in a particular cellular process. We evaluate the efficacy of LINKER by applying it to a well-known dynamic model of the cell division cycle in Saccharomyces cerevisiae. Compared to other state-of-the-art methods, subnetworks computed by LINKER are heavily enriched in Gene Ontology (GO) terms relevant to the cell cycle. Finally, we highlight how networks computed by LINKER elucidate the role of a protein kinase (Cdc5) in the mitotic exit network of a dynamic model of the cell cycle.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.