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  • Inference of Protein-Protein Interaction Networks from Liquid-Chromatography Mass-Spectrometry Data by Approximate Bayesian Computation-Sequential Monte Carlo Sampling
Title: Inference of Protein-Protein Interaction Networks from Liquid-Chromatography Mass-Spectrometry Data by Approximate Bayesian Computation-Sequential Monte Carlo Sampling
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.  more » « less
Award ID(s):
1718924
PAR ID:
10199142
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
Page Range / eLocation ID:
1 to 6
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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