Lee Abstract: The dysregulation of microRNAs (miRNAs) alters expression level of pro-oncogenic or tumor suppressive mRNAs in breast cancer, and in the long run, causes multiple biological abnormalities. Identification of such interactions of miRNA-mRNA requires integrative analysis of miRNA-mRNA expression profile data. However, current approaches have limitations to consider the regulatory relationship between miRNAs and mRNAs and to implicate the relationship with phenotypic abnormality and cancer pathogenesis. We modeled causal relationships between genomic expression and clinical data using Bayesian Networks (BN), with the goal of discovering miRNA-mRNA interactions that are associated with cancer pathogenesis. The Multiple Beam Search (MBS) algorithm learned the relationship in the data using a scoring method and discovered that hsa-miR-21, hsa-miR-10b, hsa-miR-448, and hsa-miR-96 interact with oncogenes, such as, CCND2, ESR1, NOTCH1, TGFBR2 and TGFB1, that promote tumor metastasis, invasion, and cell proliferation. We also calculated Bayesian network posterior probability (BNPP) for the models discovered by the MBS algorithm to validate true models with high likelihoods of BN models. The MBS algorithm successfully learned miRNA and mRNA expression profile data using Bayesian Networks, and identified miRNA-mRNA interactions that probabilistically affect breast cancer pathogenesis. The MBS algorithm is a potentially useful tool for identifying interacting gene pairs implicated by the deregulation of expression.
Mtonga Abstract: Reducing errors in the clinical laboratory testing process presents a significant opportunity for reducing expenditure and improving health-care quality. This is particularly true in low-resource settings where laboratory errors are further exacerbated by poor infrastructure and a lack of a well-trained workforce. We describe a pilot implementation of several informatics interventions that address sources of laboratory errors and support the entire laboratory testing process at a referral hospital in a low-resource setting.