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Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach

Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Indiana Alzheimer Disease Center and the Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
Department of Radiology, Stanford University School of Medicine, Stanford, CA 94025, USA
Genetics and Aging Research Unit and Department of Neurology, Massachusetts General Hospital and Harvard Medical School Stanford University School of Medicine, Stanford, CA 02129, USA
Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
Neurocognitive Disorders Program, Department of Psychiatry and the Duke Institute for Brain Sciences, Duke University Health System, Durham, NC 27710, USA
Author to whom correspondence should be addressed.
Data used in preparation of this article were obtained from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:
Processes 2017, 5(3), 47;
Received: 29 June 2017 / Revised: 5 August 2017 / Accepted: 13 August 2017 / Published: 17 August 2017
(This article belongs to the Special Issue Biological Networks)
PDF [1741 KB, uploaded 18 August 2017]


Alzheimer’s disease (AD) is a major public health threat; however, despite decades of research, the disease mechanisms are not completely understood, and there is a significant dearth of predictive biomarkers. The availability of systems biology approaches has opened new avenues for understanding disease mechanisms at a pathway level. However, to the best of our knowledge, no prior study has characterized the nature of pathway crosstalks in AD, or examined their utility as biomarkers for diagnosis or prognosis. In this paper, we build the first computational crosstalk model of AD incorporating genetics, antecedent knowledge, and biomarkers from a national study to create a generic pathway crosstalk reference map and to characterize the nature of genetic and protein pathway crosstalks in mild cognitive impairment (MCI) subjects. We perform initial studies of the utility of incorporating these crosstalks as biomarkers for assessing the risk of MCI progression to AD dementia. Our analysis identified Single Nucleotide Polymorphism-enriched pathways representing six of the seven Kyoto Encyclopedia of Genes and Genomes pathway categories. Integrating pathway crosstalks as a predictor improved the accuracy by 11.7% compared to standard clinical parameters and apolipoprotein E ε4 status alone. Our findings highlight the importance of moving beyond discrete biomarkers to studying interactions among complex biological pathways. View Full-Text
Keywords: pathway crosstalk; Alzheimer’s disease; biomarker; disease prediction pathway crosstalk; Alzheimer’s disease; biomarker; disease prediction

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Padmanabhan, K.; Nudelman, K.; Harenberg, S.; Bello, G.; Sohn, D.; Shpanskaya, K.; Tiwari Dikshit, P.; Yerramsetty, P.S.; Tanzi, R.E.; Saykin, A.J.; Petrella, J.R.; Doraiswamy, P.M.; Samatova, N.F.; Alzheimer’s Disease Neuroimaging Initiative. Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach. Processes 2017, 5, 47.

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