@inproceedings{d72700fe44c14d48be5fec892f62fa01,
title = "Identification of feasible pathway information for c-di-GMP binding proteins in cellulose production",
abstract = "In this paper, we utilize a machine learning approach to identify the significant pathways for c-di-GMP signaling proteins. The dataset involves gene counts from 12 pathways and 5 essential c-di-GMP binding domains for 1024 bacterial genomes. Two novel approaches, Least absolute shrinkage and selection operator (Lasso) and Random forests, have been applied for analyzing and modeling the dataset. Both approaches show that bacterial chemotaxis is the most essential pathway for c-di-GMP encoding domains. Though popular for feature selection, the strong regularization of Lasso method fails to associate any pathway to MshE domain. Results from the analysis may help to understand and emphasis to the supporting pathways involved in bacterial cellulose production. These findings demonstrate the need for a chassis to restrict the behavior or functionality by deactivating the selective pathways in cellulose production.",
keywords = "Cyclic di-guanosine monophosphate, Metabolic pathways, Random forests, Regularized logistic regression",
author = "Hassan, {Syeda Sakira} and Rahul Mangayil and Tommi Aho and Olli Yli-Harja and Matti Karp",
note = "jufoid=58152; Joint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC) ; Conference date: 01-01-1900",
year = "2018",
doi = "10.1007/978-981-10-5122-7_167",
language = "English",
isbn = "9789811051210",
series = "IFMBE Proceedings",
publisher = "Springer Verlag",
pages = "667--670",
booktitle = "EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017",
address = "Germany",
}