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Big Data, Networks Identify Cell Signaling Pathways in Lung Cancer


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3-D representation of lung cancer protein modifications

Three-dimensional representation of lung cancer protein modifications produced by a machine learning algorithm.

Credit: Science Signaling

University of Montana's Mark Grimes and his colleagues have identified networks inside lung cancer cells to advance knowledge and drug treatment. The research explains the functioning of a new class of drugs called HSP90 inhibitors, currently in clinical trials. The effort is the first large-scale study to analyze three different kinds of protein modifications simultaneously, using new data analysis methods that involve mathematical and computational approaches and networks.

Noting that many types of cancer are caused by problems with cell signaling mechanisms, Grimes says the study defines these signaling pathways with greater precision than previous methods and integrates pathways that use different protein modifications. After examining 45 lung cancer cell types, the researchers compared the cells' modified proteins to normal lung tissue.

The team used advanced pattern recognition techniques, including machine-learning algorithms, combining patterns in protein modifications with protein interaction networks to define cell signaling pathways.

The study illuminates which proteins to target with drugs to arrest different types of lung cancer, says Grimes.

From University of Montana
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Abstracts Copyright © 2018 Information Inc., Bethesda, Maryland, USA


 

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