ArticleJournal of computational biology : a journal of computational molecular cell biology2013
Considering unknown unknowns: reconstruction of nonconfoundable causal relations in biological networks.
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.
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Who cites it
8 citing papers in PubMed.
- Context-Specific Nested Effects Models.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- Single cell network analysis with a mixture of Nested Effects Models.Bioinformatics (Oxford, England) · 2018Article
- Context-Specific Nested Effects Models.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2018Article
- Inferring modulators of genetic interactions with epistatic nested effects models.PLoS computational biology · 2017Article
- OncoNEM: inferring tumor evolution from single-cell sequencing data.Genome biology · 2016Article
- Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.Bioinformatics (Oxford, England) · 2016Article
- Article
- An NGS Workflow Blueprint for DNA Sequencing Data and Its Application in Individualized Molecular Oncology.Cancer informatics · 2015Review
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3 authors.
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Abstract
Our current understanding of cellular networks is rather incomplete. We over look important but so far unknown genes and mechanisms in the pathways. Moreover, we often only have a partial account of the molecular interactions and modifications of the known players. When analyzing the cell, we look through narrow windows leaving potentially important events in blind spots. Network reconstruction is naturally confined to what we have observed. Little is known on how the incompleteness of our observations confounds our interpretation of the available data. Here we ask which features of a network can be confounded by incomplete observations and which cannot. In the context of nested effects models, we show that in the presence of missing observations or hidden factors a reliable reconstruction of the full network is not feasible. Nevertheless, we can show that certain characteristics of signaling networks like the existence of cross-talk between certain branches of the network can be inferred in a nonconfoundable way. We derive a test for inferring such nonconfoundable characteristics of signaling networks. Next, we introduce a new data structure to represent partially reconstructed signaling networks. Finally, we evaluate our method both on simulated data and in the context of a study on early stem cell differentiation in mice.
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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.