Minimal Hepatic Encephalopathy Classification Based on Discriminative Subgraph Reconstruction
TU Liyang1, DU Junqiang1, JIE Biao1,2, ZHANG Daoqiang1
1.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics,.Nanjing 211106.2.School of Mathematics and Computer Science, Anhui Normal University, Wuhu 241000
Abstract:Minimal hepatic encephalopathy (MHE) is related to the abnormality of subnetworks, but searching related subnetworks is still a challenging task. To solve this problem, a method based on discriminative subgraph reconstruction is proposed to search subnetworks related to MHE and the subnetworks are used for MHE classification. Firstly, frequent subgraphs are mined from the functional connectivity networks of MHE and non-MHE (NMHE), respectively. Next, the discriminative subgraphs are selected from the frequent subgraphs for the original networks reconstruction and the combination of discriminative networks is conducted to reconstruct the original networks. Finally, the graph kernel is applied to compute the similarity between pairwise reconstructed networks and the kernel SVM is adopted for MHE classification. On the dataset of 77 patients with hepatic cirrhosis, the high accuracy of the proposed algorithm is achieved and the effectiveness of the proposed method is demonstrated.
[1] FERENCI P, LOCKWOOD A, MULLEN K, et al. Hepatic Ence-phalopathy-Definition, Nomenclature, Diagnosis, and Quantification:Final Report of the Working Party at the 11th World Congresses of Gastroenterology, Vienna, 1998. Hepatology, 2002, 35(3): 716-721. [2] DHIMAN R K, CHAWLA Y K. Minimal Hepatic Encephalopathy. Indian Journal of Gastroenterology, 2009, 28(1): 5-16. [3] JAO T, SCHRTER M, CHEN C L, et al. Functional Brain Network Changes Associated with Clinical and Biochemical Measures of the Severity of Hepatic Encephalopathy. NeuroImage, 2015, 122: 332-344. [4] QI R F, ZHANG L J, XU Q, et al. Selective Impairments of Res-ting-State Networks in Minimal Hepatic Encephalopathy. PLoS One, 2012, 7(5). DOI: 10.1371/journal.pone.0037400. [5] CHEN H J, CHEN R, YANG M, et al. Identification of Minimal Hepatic Encephalopathy in Patients with Cirrhosis Based on White Matter Imaging and Bayesian Data Mining. American Journal of Neuroradiology, 2015, 36(3): 481-487. [6] SCHLKOPF B, SMOLA A J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, USA: MIT Press, 2001. [7] HUAN J, WANG W, PRINS J. Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism // Proc of the 13th IEEE International Conference on Data Mining. Washington, USA: IEEE, 2003: 549-552. [8] YAN X F, HAN J W. gSpan: Graph-Based Substructure Pattern Mining // Proc of the IEEE International Conference on Data Mi-ning. Washington, USA: IEEE, 2002: 721-724. [9] BORGELT C, BERTHOLD M R. Mining Molecular Fragments: Finding Relevant Substructures of Molecules // Proc of the IEEE International Conference on Data Mining. New York, USA: IEEE, 2002: 51-58. [10] GARTNER T, FLACH P, WROBEL S. On Graph Kernels: Hardness Results and Efficient Alternatives // Proc of the 16th Annual Conference on Learning Theory. Berlin, Germany: Springer, 2003: 129-143. [11] ALVAREZ M A, QI X J, YAN C H. A Shortest-Path Graph Kernel for Estimating Gene Product Semantic Similarity. Journal of Biomedical Semantics, 2011. DOI: 10.1186/2041-1480-2-3. [12] SHERVASHIDZE N, VISHWANATHAN S V N, PETRI T, et al. Efficient Graphlet Kernels for Large Graph Comparison. Journal of Machine Learning Research, 2009, 5: 488-495. [13] SHERVASHIDZE N, SCHWEITZER P, JAN VAN LEEUWEN E, et al. Weisfeiler-Lehman Graph Kernels. Journal of Machine Learning Research, 2011, 12: 2539-2561. [14] YAN C G, ZANG Y F. DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Frontiers in Systems Neuroscience, 2010. DOI: 10.3389/fnsys.2010.00013. [15] ZUO X N, DI MARTINO A, KELLY C, et al. The Oscillating Brain: Complex and Reliable. NeuroImage, 2010, 49(2): 1432-1445. [16] RUBINOV M, SPORNS O. Complex Network Measures of Brain Connectivity: Uses and Interpretations. NeuroImage, 2010, 52(3): 1059-1069.