ORCID Profile
0000-0001-9018-4680
Current Organisation
University of Oxford
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Publisher: Elsevier BV
Date: 02-1996
DOI: 10.1016/0166-6851(95)02569-3
Abstract: The coronavirus disease (COVID-19) has rapidly spread throughout the world and while pregnant women present the same adverse outcome rates, they are underrepresented in clinical research. We collected clinical data of 155 test-positive COVID-19 pregnant women at Stony Brook University Hospital. Many of these collected data are of multivariate categorical type, where the number of possible outcomes grows exponentially as the dimension of data increases. We modeled the data within the unsupervised Bayesian framework and mapped them into a lower dimensional space using latent Gaussian processes. The latent features in the lower dimensional space were further used for predicting if a pregnant woman would be admitted to a hospital due to COVID-19 or would remain with mild symptoms. We compared the prediction accuracy with the dummy/one-hot encoding of categorical data and found that the latent Gaussian process had better accuracy.
Publisher: Elsevier BV
Date: 07-2016
Publisher: Springer Science and Business Media LLC
Date: 27-06-2016
DOI: 10.1038/NG.3599
Publisher: Cold Spring Harbor Laboratory
Date: 24-09-2019
DOI: 10.1101/776781
Abstract: Imported cases present a considerable challenge to the elimination of malaria. Traditionally, patient travel history has been used to identify imported cases, but the long-latency liver stages confound this approach in Plasmodium vivax . Molecular tools to identify and map imported cases offer a more robust approach, that can be combined with drug resistance and other surveillance markers in high-throughput, population-based genotyping frameworks. Using a machine learning approach incorporating hierarchical FST (HFST) and decision tree (DT) analysis applied to 831 P. vivax genomes from 20 countries, we identified a 28-Single Nucleotide Polymorphism (SNP) barcode with high capacity to predict the country of origin. The Matthews correlation coefficient (MCC), which provides a measure of the quality of the classifications, ranging from −1 (total disagreement) to 1 (perfect prediction), exceeded 0.9 in 15 countries in cross-validation evaluations. When combined with an existing 37-SNP P. vivax barcode, the 65-SNP panel exhibits MCC scores exceeding 0.9 in 17 countries with up to 30% missing data. As a secondary objective, several genes were identified with moderate MCC scores (median MCC range from 0.54-0.68), amenable as markers for rapid testing using low-throughput genotyping approaches. A likelihood-based classifier framework was established, that supports analysis of missing data and polyclonal infections. To facilitate investigator-lead analyses, the likelihood framework is provided as a web-based, open-access platform (vivaxGEN-geo) to support the analysis and interpretation of data produced either at the 28-SNP core or full 65-SNP barcode. These tools can be used by malaria control programs to identify the main reservoirs of infection so that resources can be focused to where they are needed most.
Publisher: eLife Sciences Publications, Ltd
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 28-04-2013
DOI: 10.1038/NG.2624
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Alistair Miles.