ArticleSummit on translational bioinformatics2009
Modeling and characterization of disease associated subnetworks in the human interactome using machine learning.
Article in Summit on translational bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The availability of large-scale, genome-wide data about the molecular interactome of entire organisms has made possible new types of integrative studies, making use of rapidly accumulating knowledge of gene-disease associations. Previous studies have established the presence of functional biomodules in the molecular interaction network of living organisms, a number of which have been associated with the pathogenesis and progression of human disease. While a number of studies have examined the networks and biomodules associated with disease, the properties that contribute to the particular susceptibility of these subnetworks to disruptions leading to disease phenotypes have not been extensively studied. We take a machine learning approach to the characterization of these disease subnetworks associated with complex and single-gene diseases, taking into account both the biological roles of their constituent genes and topological properties of the networks they form.
Identifiers
21347156PMC3041579W1798006219What OpenQuestion holds
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.