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Data-driven prediction of cancer cell fates with a nonlinear model of signaling pathways

Published: 20 September 2014 Publication History

Abstract

Signals from the environment of a cell are captured and transmitted by signaling proteins inside the cell. The cell will respond to these signal inputs by leading to one of several possible phenotypic outputs, e.g., cell survival or cell death. However, the underlying mechanisms of processing molecular information are unknown, thus data-driven models are needed to bridge signaling data with phenotypic measurements of cell fates. The traditional linear model has its limitations because it assumes that one cell fate is proportional to the activity of every signaling protein, which is unlikely to be true in the complex biological systems. Therefore, we propose a nonlinear model to predict the probability of cell fates based on activity levels of signaling proteins.
Cross validation is used to estimate the performance of our model. We compared our nonlinear model with the linear model, demonstrating that the present nonlinear model has superior performance on cell fates prediction. Moreover, by in silico examination of gene knock-down, the proposed model is able to reveal the drug effects which can provide great assistance to traditional approaches such as binding affinity analysis.

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  • (2017)A polynomial based model for cell fate prediction in human diseasesBMC Systems Biology10.1186/s12918-017-0502-511:S7Online publication date: 21-Dec-2017

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  1. Data-driven prediction of cancer cell fates with a nonlinear model of signaling pathways

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        cover image ACM Conferences
        BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
        September 2014
        851 pages
        ISBN:9781450328944
        DOI:10.1145/2649387
        • General Chairs:
        • Pierre Baldi,
        • Wei Wang
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        Publication History

        Published: 20 September 2014

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        Author Tags

        1. cancer
        2. cell fates
        3. nonlinear
        4. signaling proteins

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        BCB '14: ACM-BCB '14
        September 20 - 23, 2014
        California, Newport Beach

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        • (2017)A polynomial based model for cell fate prediction in human diseasesBMC Systems Biology10.1186/s12918-017-0502-511:S7Online publication date: 21-Dec-2017

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