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Creators/Authors contains: "Neto, Ulisses Braga"

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  1. ; ; (, 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP))
    null (Ed.)
    We propose a new algorithm for inference of protein-protein interaction (PPI) networks from noisy time series of Liquid- Chromatography Mass-Spectrometry (LC-MS) proteomic expression data based on Approximate Bayesian Computation - Sequential Monte Carlo sampling (ABC-SMC). The algorithm is an extension of our previous framework PALLAS. The proposed algorithm can be easily modified to handle other complex models of expression data, such as LC-MS data, for which the likelihood function is intractable. Results based on synthetic time series of cytokine LC-MS measurements cor- responding to a prototype immunomic network demonstrate that our algorithm is capable of inferring the network topology accurately. 
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