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| http://dx.doi.org/10.1002/ajh.24501 | DOI Listing |
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Combined stochastic modelling of pathogenic and spoilage microorganisms.
EFSA J
December 2024
Laboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment Aristotle University of Thessaloniki Thessaloniki Greece.
Quantitative microbiological risk assessment (QMRA) of pathogens in food safety is well established, but steps are being taken to expand this methodology to food spoilage. Parallels can be drawn between the steps involved in a QMRA for pathogens and its application to specific spoilage organisms (SSO). During hazard characterisation for pathogens, the appropriate dose-response model is used to link the hazard level to the health outcome by estimating the probability of illness, resulting from the ingestion of a certain dose of the hazard.
View Article and Find Full Text PDFBAMITA: Bayesian multiple imputation for tensor arrays.
Biostatistics
December 2024
Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, 2221 University Avenue SE, Minneapolis, MN 55414, United States.
Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects.
View Article and Find Full Text PDFSMURF: Statistical Modality Uniqueness and Redundancy Factorization.
Proc ACM Int Conf Multimodal Interact
November 2024
Carnegie Mellon University, Pittsburgh, PA, USA.
Multimodal late fusion is a well-performing fusion method that sums the outputs of separately processed modalities, so-called modality contributions, to create a prediction; for example, summing contributions from vision, acoustic, and language to predict affective states. In this paper, our primary goal is to improve the interpretability of what modalities contribute to the prediction in late fusion models. More specifically, we want to factorize modality contributions into what is consistently shared by at least two modalities (pairwise redundant contributions) and what the remaining modality-specific contributions are (unique contributions).
View Article and Find Full Text PDFBAMITA: Bayesian Multiple Imputation for Tensor Arrays.
ArXiv
October 2024
Division of Biostatistics and Health Data Science, University of Minnesota.
Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects.
View Article and Find Full Text PDFOptimizing multi-omics data imputation with NMF and GAN synergy.
Bioinformatics
November 2024
Department of Computer Science, University of Central Florida, Orlando, FL 32816, United States.
Motivation: Integrating multiple omics datasets can significantly advance our understanding of disease mechanisms, physiology, and treatment responses. However, a major challenge in multi-omics studies is the disparity in sample sizes across different datasets, which can introduce bias and reduce statistical power. To address this issue, we propose a novel framework, OmicsNMF, designed to impute missing omics data and enhance disease phenotype prediction.
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