A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
New iterative approach (ISNCA) for constrained matrix factorization methods
Tekijät: Bar N, Jayavelu ND
Konferenssin vakiintunut nimi: 11th IFAC Symposium on Dynamics and Control of Process Systems including Biosystems
Kustantaja: ELSEVIER SCIENCE BV
Kustannuspaikka: AMSTERDAM
Julkaisuvuosi: 2016
Lehti: IFAC-PapersOnLine
Tietokannassa oleva lehden nimi: IFAC PAPERSONLINE
Lehden akronyymi: IFAC PAPERSONLINE
Vuosikerta: 49
Numero: 7
Aloitussivu: 472
Lopetussivu: 477
Sivujen määrä: 6
ISSN: 2405-8963
DOI: https://doi.org/10.1016/j.ifacol.2016.07.387
Tiivistelmä
Gene regulation networks are complex, often involve thousands of genes, regulators and the connections between them. To understand the complex interactions between these genes and regulators with time, large empirical data is used the so called time-series gene expression data. Many statistical took are used to analyze this data but they often impose restrictions that reduce the size of the network and make the solution less feasible from a biological perspective. We developed the iterative subnetwork component analysis (ISNCA), a method that decomposes the empirical data of two or more overlapping subnetworks with joint components at one iteration, and updates the solution at the next iteration by subtracting the contribution of each of the subnetworks. This predict - update method managed to relax the restrictions and solve larger networks. We generalized the method in this paper to include both regulators and genes in the joint, partition, and demonstrated its accuracy using a synthetic network with a known matrix decomposition. We also applied the ISNCA on large biological data taken front mice cells and obtained larger and more accurate solutions than achieved by previous methods. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Gene regulation networks are complex, often involve thousands of genes, regulators and the connections between them. To understand the complex interactions between these genes and regulators with time, large empirical data is used the so called time-series gene expression data. Many statistical took are used to analyze this data but they often impose restrictions that reduce the size of the network and make the solution less feasible from a biological perspective. We developed the iterative subnetwork component analysis (ISNCA), a method that decomposes the empirical data of two or more overlapping subnetworks with joint components at one iteration, and updates the solution at the next iteration by subtracting the contribution of each of the subnetworks. This predict - update method managed to relax the restrictions and solve larger networks. We generalized the method in this paper to include both regulators and genes in the joint, partition, and demonstrated its accuracy using a synthetic network with a known matrix decomposition. We also applied the ISNCA on large biological data taken front mice cells and obtained larger and more accurate solutions than achieved by previous methods. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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