Abstract
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.
Sponsored by the US Army Research Office and the Defense Advanced Research Projects Agency; accomplished under Cooperative Agreement W911NF-14-2-0020.
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Notes
- 1.
We use the machine learning framework [19] for PCA and basic clustering.
- 2.
Alternatively, PCA can be applied along the gene/compound dimension which we have done in another part of our RTA workflow.
- 3.
Another affinity measure we have explored is based on correlated changes (using time series derivatives) but beyond the scope of this paper.
- 4.
We explored some other metrics based on the original (non-normalized) time series data that we omit for brevity.
- 5.
Currently we restrict analysis of transcriptomics data to protein coding genes.
- 6.
Disclaimer. Research was sponsored by the U.S. Army Research Office and the Defense Advanced Research Projects Agency and was accomplished under Cooperative Agreement Number W911NF-14-2-0020. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office, DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
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Vertes, A. et al. (2018). Inferring Mechanism of Action of an Unknown Compound from Time Series Omics Data. In: Češka, M., Šafránek, D. (eds) Computational Methods in Systems Biology. CMSB 2018. Lecture Notes in Computer Science(), vol 11095. Springer, Cham. https://doi.org/10.1007/978-3-319-99429-1_14
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