Mehmet Eren Ahsen

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

Publisher: Springer Science and Business Media LLC

Date: 24-10-2018

DOI: 10.1038/S41467-018-06735-8

Abstract: The response to respiratory viruses varies substantially between in iduals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses.

The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers

Publisher: Proceedings of the National Academy of Sciences

Date: 19-08-2021

DOI: 10.1073/PNAS.2100761118

Abstract: While it would be desirable that the output of binary classification algorithms be the probability that the classification is correct, most algorithms do not provide a method to calculate such a probability. We propose a probabilistic output for binary classifiers based on an unexpected mapping of the probability of correct classification to the probability of occupation of a fermion in a quantum system, known as the Fermi–Dirac distribution. This mapping allows us to compute the optimal threshold to separate predicted classes and to calculate statistical parameters necessary to estimate confidence intervals of performance metrics. Using this mapping we propose an ensemble learning algorithm. In short, the Fermi–Dirac distribution provides a calibrated probabilistic output for binary classification.

The University Of Texas At Dallas

Location: United States of America

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Middle East Technical University

Location: Turkey

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University Of Illinois Urbana-Champaign

Location: United States of America

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Bilkent Üniversitesi

Location: Turkey

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IBM Thomas J Watson Research Center

Location: United States of America

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Icahn School Of Medicine At Mount Sinai

Location: United States of America

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University Of Illinois

Location: United States of America

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