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Cystic fibrosis (CF) is a monogenic disease due to mutations in the CFTR gene. Yet, variability in CF disease presentation is presumed to be affected by modifier genes, such as those recently demonstrated for the pulmonary aspect. Here, we conduct a modifier gene study for meconium ileus (MI), an intestinal obstruction that occurs in 16-20% of CF newborns,(More)
In cystic fibrosis (CF), CFTR dysfunction leads to salt and water imbalance across airway epithelia, depleted surface liquid layer, and impaired mucociliary clearance. This provides optimal conditions for chronic bacterial infections leading to excessive inflammation and progressive obstructive lung disease. We hypothesized that other epithelial channels(More)
Virtual Probe (VP), proposed for characterization of spatial variations and for test time reduction, can effectively reconstruct the spatial pattern of a test item for an entire wafer using measurement values from only a small fraction of dies on the wafer. However, VP calculates the spatial signature of each test item separately, one item at a time,(More)
Variants associated with meconium ileus in cystic fibrosis were identified in 3,763 affected individuals by genome-wide association study (GWAS). Five SNPs at two loci near SLC6A14 at Xq23-24 (minimum P = 1.28 × 10(-12) at rs3788766) and SLC26A9 at 1q32.1 (minimum P = 9.88 × 10(-9) at rs4077468) accounted for ~5% of phenotypic variability and were(More)
Cystic fibrosis is realizing the promise of personalized medicine. Recent advances in drug development that target the causal CFTR directly result in lung function improvement, but variability in response is demanding better prediction of outcomes to improve management decisions. The genetic modifier SLC26A9 contributes to disease severity in the CF(More)
The problem of inferring a user's intentions in Machine–Human Interaction has been the key research issue for providing personalized experiences and services. In this paper, we propose novel approaches on modeling and inferring user's actions in a computer. Two linguistic features – keyword and concept features – are extracted from the semantic context for(More)
—The discovery of patterns and correlations hidden in the test data could help reduce test time and cost. In this paper, we propose a methodology and supporting statistical regression tools that can exploit and utilize both spatial and inter-test-item correlations in the test data for test time and cost reduction. We first describe a statistical regression(More)
In this paper, we investigate the problem of optimizing complex multivariate performance measures to learn classifiers for pattern classification problems. For the first time, the multi-kernel learning is considered to construct a classifier to optimize a given nonlinear and non-smooth multivariate classifier performance measure. We estimate and optimize(More)
The identification of small molecules that target specific CFTR variants has ushered in a new era of treatment for cystic fibrosis (CF), yet optimal, individualized treatment of CF will require identification and targeting of disease modifiers. Here we use genome-wide association analysis to identify genetic modifiers of CF lung disease, the primary cause(More)
—It is known that statistical analysis of test data can help screen potential test escapes without additional physical measurements. Based on analysis of production test data, this paper focuses on feature engineering for statistical tests to screen test escapes. The features are engineered in two aspects: development of effective features and(More)