Filtering Microarray Correlations by Statistical Literature Analysis Yields Potential Hypotheses for Lactation Research

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dc.creatorLing, Maurice HT
dc.creatorLefevre, Christophe
dc.creatorNicholas, Kevin R.
dc.date2009-01-02
dc.date.accessioned2026-07-07T12:23:46Z
dc.date.available2026-07-07T12:23:46Z
dc.descriptionOur results demonstrated that a previously reported protein name co-occurrence method (5-mention PubGene) which was not based on a hypothesis testing framework, it is generally statistically more significant than the 99th percentile of Poisson distribution-based method of calculating co-occurrence. It agrees with previous methods using natural language processing to extract protein-protein interaction from text as more than 96% of the interactions found by natural language processing methods to overlap with the results from 5-mention PubGene method. However, less than 2% of the gene co-expressions analyzed by microarray were found from direct co-occurrence or interaction information extraction from the literature. At the same time, combining microarray and literature analyses, we derive a novel set of 7 potential functional protein-protein interactions that had not been previously described in the literature.
dc.identifierhttps://arxiv.org/abs/0901.0213
dc.identifierhttp://arxiv.org/abs/0901.0213
dc.identifierLing, MHT, Lefevre, C, Nicholas, KR. 2008. Filtering Microarray Correlations by Statistical Literature Analysis Yields Potential Hypotheses for Lactation Research. The Python Papers 3(3): 4
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/214108
dc.subjectDigital Libraries
dc.subjectDatabases
dc.titleFiltering Microarray Correlations by Statistical Literature Analysis Yields Potential Hypotheses for Lactation Research
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