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Inferring mechanism of action of an unknown compound from time series omics data. (English) Zbl 1397.92236

Češka, Milan (ed.) et al., Computational methods in systems biology. 16th international conference, CMSB 2018, Brno, Czech Republic, September 12–14, 2018. Proceedings. Cham: Springer (ISBN 978-3-319-99428-4/pbk; 978-3-319-99429-1/ebook). Lecture Notes in Computer Science 11095. Lecture Notes in Bioinformatics, 238-255 (2018).
Summary: Identifying the mechanism of action (MoA) of an unknown, possibly novel, substance (chemical, protein, or pathogen) is a significant challenge. Biologists typically spend years working out the MoA for known compounds. MoA determination is especially challenging if there is no prior knowledge and if there is an urgent need to understand the mechanism for rapid treatment and/or prevention of global health emergencies. In this paper, we describe a data analysis approach using Gaussian processes and machine learning techniques to infer components of the MoA of an unknown agent from time series transcriptomics, proteomics, and metabolomics data.
The work was performed as part of the DARPA Rapid Threat Assessment program, where the challenge was to identify the MoA of a potential threat agent in 30 days or less, using only project generated data, with no recourse to pre-existing databases or published literature.
For the entire collection see [Zbl 1397.92008].

MSC:

92C40 Biochemistry, molecular biology
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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