Markov Chain Ontology Analysis (MCOA)

<p>Abstract</p> <p>Background</p> <p>Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data.</p> <p>Results</p> <p>In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods.</p> <p>Conclusion</p> <p>A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches.</p

Variations in state-level definitions: children with special health care needs.

Multiple agencies at the federal and state level provide for children with special health care needs (CSHCN), with variation in eligibility criteria. Epidemiological studies show that 3.8%-32% of children could be classified as children with special health care needs, depending on the definition and method of determination used. OBJECTIVES: To determine the extent of variation between definitions used and funding by Supplemental Security Income (SSI), Title V, and Medicaid for CSHCN. METHODS: Statistics on children receiving SSI and the amount of funding were obtained from the SSI website. This was compared to information on Title V children from the Maternal and Child Health Bureau (MCHB) website and eligibility definitions published by the Institute of Child Health Policy in Gainesville, Florida. Medicaid definitions were obtained through interviews with state Medicaid agencies and confirmed with state regulations. RESULTS: The population enrolled in SSI has varied with alterations in eligibility criteria. The number of children enrolled in SSI and the amount of funding per child in each state correlate with the state poverty rate (r=0.56, p<0.0001; r=0.44, p<0.001). Enrollment in Title V does not correlate with state poverty rates (r=0.16, p=0.25). Title V definitions vary widely among states, but there was no correlation between the number of children served or amount of funding per child and the type of definition used (Z=-0.12, p=0.91; Z=-0.59, p=0.55). State Medicaid agencies rarely define CSHCN. CONCLUSIONS: There is significant variation in definitions used by agencies serving CSHCN. Agencies need to be more explicit with eligibility criteria so the definitions are logical to those making referrals for services