Browsing by Author "Kshirsagar, Madhura M."

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    AZEBRA (Almost Zero Error Basepair-based Record Alert): A genomic clinical decision support system
    (2017) Kulanthaivel, Anand; Kshirsagar, Madhura M.; Alarifi, Mohammad; Oki, Mark N.; Jones, Josette F.
    The idea of the United States's Precision Medicine Initiative (PMI) was to allow providers (and patients) to leverage large amounts of information (including patient genomic data) in order to create actionable knowledge that increases patient well-being. To this end, we propose a system called AZEBRA; the acronym stands for Almost Zero Error Basepair-based Record Alerts. Zebra, in addition to being a well-known wild animal, is a common medical slang term for the clinician's fallacy of mistakenly corning to a rare and sometimes dire diagnosis (the rare zebra diagnosis) due to having missed more common causes of patient symptoms (the common horse diagnosis); conversely, patients with rare conditions would be better thought of as zebras and not horses. AZEBRA is intended to leverage the principles of genetically-enhanced precision medicine in order to alert clinicians to the presence of patients with five (four rare, one common) genetic pathologies that are ordinarily sources of unnecessary morbidity and mortality in clinical settings.
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    PAGER: constructing PAGs and new PAG-PAG relationships for network biology
    (Oxford University Press, 2015-06-15) Yue, Zongliang; Kshirsagar, Madhura M.; Nguyen, Thanh; Suphavilai, Chayaporn; Neylon, Michael T.; Zhu, Liugen; Ratliff, Timothy; Chen, Jake Yue; Department of Computer & Information Science, School of Science
    In this article, we described a new database framework to perform integrative "gene-set, network, and pathway analysis" (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene-gene relationships and two types of 3 174 323 PAG-PAG regulatory relationships-co-membership based and regulatory relationship based. To help users assess each PAG's biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG-PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/.