ArticlePhysical biology2012
Modeling the latent dimensions of multivariate signaling datasets.
Article in Physical biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.Nature communications · 2023Article
- A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling.Nature communications · 2022Article
- Robust latent-variable interpretation of in vivo regression models by nested resampling.Scientific reports · 2019Article
- TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors.Science signaling · 2016Article
- Molecular variability elicits a tunable switch with discrete neuromodulatory response phenotypes.Journal of computational neuroscience · 2016Article
- Models of signalling networks - what cell biologists can gain from them and give to them.Journal of cell science · 2013Review
- An ERK-p38 subnetwork coordinates host cell apoptosis and necrosis during coxsackievirus B3 infection.Cell host & microbe · 2013Article
- Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics.PloS one · 2013Article
- Systems biology in physiology: the vasopressin signaling network in kidney.American journal of physiology. Cell physiology · 2012Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Cellular signal transduction is coordinated by modifications of many proteins within cells. Protein modifications are not independent, because some are connected through shared signaling cascades and others jointly converge upon common cellular functions. This coupling creates a hidden structure within a signaling network that can point to higher level organizing principles of interest to systems biology. One can identify important covariations within large-scale datasets by using mathematical models that extract latent dimensions-the key structural elements of a measurement set. In this paper, we introduce two principal component-based methods for identifying and interpreting latent dimensions. Principal component analysis provides a starting point for unbiased inspection of the major sources of variation within a dataset. Partial least-squares regression reorients these dimensions toward a specific hypothesis of interest. Both approaches have been used widely in studies of cell signaling, and they should be standard analytical tools once highly multivariate datasets become straightforward to accumulate.
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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 Socratic graph.