A Network-based Comparison between Molecular Apocrine Breast Cancer Tumor and Basal and Luminal Tumors by Joint Graphical Lasso.

Authors:
Maryam Shahdoust
Maryam Shahdoust
Arak University of Medical Sciences
Iran
Hossein Mahjub
Hossein Mahjub
School of Public Health
Iran
Hamid Pezeshk
Hamid Pezeshk
University of Tehran
Iran
Mehdi Sadeghi
Mehdi Sadeghi
National Institute of Genetic Engineering and Biotechnology

IEEE/ACM Trans Comput Biol Bioinform 2019 Apr 15. Epub 2019 Apr 15.

Joint graphical lasso(JGL) approach is a Gaussian graphical model to estimate multiple graphical models corresponding to distinct but related groups. Molecular apocrine (MA) breast cancer tumor has similar characteristics to luminal and basal subtypes. Due to the relationship between MA tumor and two other subtypes, this paper investigates the similarities and differences between the MA genes association network and the ones corresponding to other tumors by taking advantageous of JGL properties. Two distinct JGL graphical models are applied to two sub-datasets including the gene expression information of the MA and the luminal tumors and also the MA and the basal tumors. Then, topological comparisons between the networks such as finding the shared edges are applied. In addition, several support vector machine (SVM) classification models are performed to assess the discriminating power of some critical nodes in the networks, like hub nodes, to discriminate the tumors sample. Applying the JGL approach prepares an appropriate tool to observe the networks of the MA tumor and other subtypes in one map. The results obtained by comparing the networks could be helpful to generate new insight about MA tumor for future studies.

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Source
http://dx.doi.org/10.1109/TCBB.2019.2911074DOI Listing
April 2019

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