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1.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-325977

ABSTRACT

Traditional AI approaches in customized (personalized) contextual pricing applications assume that the data distribution at the time of online pricing is similar to that observed during training. However, this assumption may be violated in practice because of the dynamic nature of customer buying patterns, particularly due to unanticipated system shocks such as COVID-19. We study the changes in customer behavior for a major airline during the COVID-19 pandemic by framing it as a covariate shift and concept drift detection problem. We identify which customers changed their travel and purchase behavior and the attributes affecting that change using (i) Fast Generalized Subset Scanning and (ii) Causal Forests. In our experiments with simulated and real-world data, we present how these two techniques can be used through qualitative analysis.

2.
Elife ; 92020 05 28.
Article in English | MEDLINE | ID: covidwho-401507

ABSTRACT

The COVID-19 pandemic demands assimilation of all biomedical knowledge to decode mechanisms of pathogenesis. Despite the recent renaissance in neural networks, a platform for the real-time synthesis of the exponentially growing biomedical literature and deep omics insights is unavailable. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations from unstructured text, and triangulation with insights from single-cell RNA-sequencing, bulk RNA-seq and proteomics from diverse tissue types. A hypothesis-free profiling of ACE2 suggests tongue keratinocytes, olfactory epithelial cells, airway club cells and respiratory ciliated cells as potential reservoirs of the SARS-CoV-2 receptor. We find the gut as the putative hotspot of COVID-19, where a maturation correlated transcriptional signature is shared in small intestine enterocytes among coronavirus receptors (ACE2, DPP4, ANPEP). A holistic data science platform triangulating insights from structured and unstructured data holds potential for accelerating the generation of impactful biological insights and hypotheses.


Subject(s)
Coronavirus Infections/virology , Libraries, Medical , Pneumonia, Viral/virology , Receptors, Virus/metabolism , Animals , Betacoronavirus/genetics , Betacoronavirus/metabolism , COVID-19 , Coronavirus Infections/metabolism , Coronavirus Infections/pathology , Gene Expression Profiling , Humans , Knowledge Discovery , Mice , Pandemics , Pneumonia, Viral/metabolism , Pneumonia, Viral/pathology , Receptors, Coronavirus , Receptors, Virus/chemistry , Receptors, Virus/genetics , SARS-CoV-2
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