A diagnostic methodology for Alzheimer’s disease

Journal of Clinical Bioinformatics

Su-Shing Chen²  Christopher Denq¹  Wen-Chin Hsu³ 

[1] Van Nuys Senior High School, Van Nuys, CA, 91411, USA;Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA;Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA

关键词: Support vector machine;    Alzheimer’s disease;    Target networks;    Biomarkers;    Feature selection;   

Others  :  811499
DOI  :  10.1186/2043-9113-3-9

 received in 2013-02-28, accepted in 2013-04-19,  发布年份 2013

【 摘 要 】

Background

Like all other neurodegenerative diseases, Alzheimer’s disease (AD) remains a very challenging and difficult problem for diagnosis and therapy. For many years, only historical, behavioral and psychiatric measures have been available to AD cases. Recently, a definitive diagnostic framework, using biomarkers and imaging, has been proposed. In this paper, we propose a promising diagnostic methodology for the framework.

Methods

In a previous paper, we developed an efficient SVM (Support Vector Machine) based method, which we have now applied to discover important biomarkers and target networks which provide strategies for AD therapy.

Results

The methodology selects a number of blood-based biomarkers (fewer than 10% of initial numbers on three AD datasets from NCBI), and the results are statistically verified by cross-validation. The resulting SVM is a classifier of AD vs. normal subjects. We construct target networks of AD based on MI (mutual information). In addition, a hierarchical clustering is applied on the initial data and clustered genes are visualized in a heatmap. The proposed method also performs gender analysis by classifying subjects based on gender.

Conclusions

Unlike other traditional statistical analyses, our method uses a machine learning-based algorithm. Our method selects a small set of important biomarkers for AD, differentiates noisy (irrelevant) from relevant biomarkers and also provides the target networks of the selected biomarkers, which will be useful for diagnosis and therapeutic design. Finally, based on the gender analysis, we observe that gender could play a role in AD diagnosis.

【 授权许可】

   
2013 Hsu et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Fraller DB: State of the science: use of biomarkers and imaging in diagnosis and management of Alzheimer disease. J Neurosci Nurs 2013, 45:63-70.
• [2]Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Trojanowski JQ: Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 2010, 9:119-128.
• [3]Frisoni GB, Blennow K: Biomarkers for Alzheimer’s: the sequel of an original model. Lancet Neurol 2013, 12:126-128.
• [4]Laws Sm DJDMRN: Blood-based biomarkers in alzheimer disease: where are we now and where have we to go?—reply. JAMA Neurol 2013, 70:133-134.
• [5]Guo LH, Alexopoulos P, Wagenpfeil S, Kurz A, Perneczky R: Plasma proteomics for the identification of Alzheimer Disease. Alzheimer Dis Assoc Disord 2013, 00:000-000.
• [6]Zhang D, Wang Y, Zhou L, Yuan H, Shen D: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 2011, 55:856-867.
• [7]Fan Y, Batmanghelich N, Clark CM, Davatzikos C: Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 2008, 39:1731-1743.
• [8]Fan Y, Shen D, Gur RC, Gur RE, Davatzikos C: COMPARE: classification of morphological patterns using adaptive regional elements. IEEE T Med Imaging 2007, 26:93-105.
• [9]Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM: O3-06-01: Complex patterns of structural brain abnormality identified in MCI via high-dimensional pattern classification of MR images may be early markers of Alzheimer’s disease. Alzheimer’s Dement 2006, 2:S64.
• [10]Guyon I, Weston J, Barnhill S, Vapnik V: Gene selection for cancer classification using support vector machines. Mach Learn 2002, 46:389-422.
• [11]Hsu WC, Liu CC, Chang F, Chen SS: Cancer classification: mutual information, target network and strategies of therapy. J Clin Bioinforma 2012, 2:16. BioMed Central Full Text
• [12]Maes OC, Xu S, Yu B, Chertkow HM, Wang E, Schipper HM: Transcriptional profiling of Alzheimer blood mononuclear cells by microarray. Neurobiol Aging 2007, 28:1795-1809.
• [13]Maes OC, Schipper HM, Chertkow HM, Wang E: Methodology for discovery of Alzheimer’s disease blood-based biomarkers. J Gerontol A Biol Sci Med Sci 2009, 64:636-645.
• [14]Maes OC, Schipper HM, Chong G, Chertkow HM, Wang E: A GSTM3 polymorphism associated with an etiopathogenetic mechanism in Alzheimer disease. Neurobiol Aging 2010, 31:34-45.
• [15]Bi J, Bennett K, Embrechts M, Breneman C, Song M: Dimensionality reduction via sparse support vector machines. J Mach Learn Res 2003, 3:1229-1243.
• [16]Stoppiglia H, Dreyfus G, Dubois R, Oussar Y: Ranking a random feature for variable and feature selection. J Mach Learn Res 2008, 3:1399-1414.
• [17]Tuv E, Borisov A, Torkkola K: Feature Selection Using Ensemble Based Ranking Against Artificial Contrasts. In Neural Networks, 2006 IJCNN '06 International Joint Conference on; 0–0 0. Vancover, BC,Canada: IEEE; 2006:2181-2186.
• [18]Barabasi AL, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nat Rev Genet 2004, 5:101-113.
• [19]Shannon CE: A mathematical theory of communication. SIGMOBILE Mob Comput Commun Rev 2001, 5:3-55.
• [20]Beirlant J, Dudewicz EJ, Ouml LG, rfi, Meulen ECVD: Nonparametric entropy estimation: an overview. Int J Math Stat Sci 1997, 6:17-39.
• [21]Qiu P, Gentles AJ, Plevritis SK: Fast calculation of pairwise mutual information for gene regulatory network reconstruction. Comp Methods Programs Biomed 2009, 94:177-180.
• [22]Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera R, Califano A: ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinformatics 2006, 7:S7.
• [23]Wolf ME, Lam MS: A data locality optimizing algorithm. SIGPLAN Not 1991, 26:30-44.
• [24]Butte AJ, Kohane IS: Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 2000, 418-429.
• [25]Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A: Reverse engineering of regulatory networks in human B cells. Nat Genet 2005, 37:382-390.
• [26]Chang C-C, Lin C-J: LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011, 2:1-27.
• [27]Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001, 98:5116-5121.
• [28]Tibshirani R, Hastie T, Narasimhan B, Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 2002, 99:6567-6572.
• [29]Fan Y, Shen D, Davatzikos C: Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM. MICCAI 2005. In Lecture Notes in Computer Science, Volume 3749. Edited by Duncan J, Gerig G. Palm Springs, California, USA: Springer Berlin Heidelberg; 2005:1-8.