Month: May 2019

Fully Interpretable Deep Learning Model of Transcriptional Control

The universal expressibility assumption of Deep Neural Networks (DNN) is the key motivation behind recent work in the system biology community to employ DNNs to solve important problems in functional genomics and molecular genetics. Because of the black-box nature of DNN, such assumptions, while useful in practice, are unsatisfactory for scientific analysis. In this paper,…
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The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system

Recent advances in machine learning have allowed identification of molecular and cellular factors that underly successful antibody responses to influenza vaccines. Results of these studies have revealed the high level of complexity necessary to establish influenza immunity, and many different cellular and molecular components involved. However, identified correlates of protection, as measured by antibody responses…
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Multiplexed measurement of protein biomarkers in high-frequency longitudinal dried blood spot (DBS) samples: characterization of inflammatory responses

A detailed understanding of changes in blood protein biomarkers occuring in individuals over time would enable truly personalized approaches to health and disease monitoring. Such measurements could reveal smaller, earlier departures from normal baseline levels of biomarkers thus allowing better disease detection and treatment monitoring. Current practice, however, generally involves infrequent, sporadic biomarker testing, and…
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Nested Active Learning for Efficient Model Contextualization and Parameterization

The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free-parameters; given the wide range of variable combinations, along with the intractability of ab initio modeling techniques which could be used to constrain these combinations, an…
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