A centrality based multi-objective approach to disease gene association

Disease Gene Association finds genes that are involved in the presentation of a given genetic disease. We present a hybrid approach which implements a multi-objective genetic algorithm, where input consists of centrality measures based on various relational biological evidence types merged into a complex network. Multiple objective settings and parameters are studied including the development of a new exchange methodology, safe dealer-based crossover. Successful results with respect to breast cancer and Parkinson's disease compared to previous techniques and popular known databases are shown. In addition, the newly developed methodology is also successfully applied to Alzheimer's disease, further demonstrating its flexibility. Across all three case studies the strongest results were produced by the shortest path-based measures stress and betweenness, either in a single objective parameter setting or when used in conjunction in a multi-objective environment. The new crossover technique achieved the best results when applied to Alzheimer's disease.

[1]  Todd F. DeLuca,et al.  Genotator: A disease-agnostic tool for genetic annotation of disease , 2010, BMC Medical Genomics.

[2]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[3]  J. Hardy,et al.  A new pathogenic mutation in the APP gene (I716V) increases the relative proportion of A beta 42(43). , 1997, Human molecular genetics.

[4]  Jacob Benesty,et al.  Pearson Correlation Coefficient , 2009 .

[5]  N. P. Gopalan,et al.  A Comparative Study and Analysis of DNA Sequence Classifiers for Predicting Human Diseases , 2016, ICIA.

[6]  Carl Kingsford,et al.  The power of protein interaction networks for associating genes with diseases , 2010, Bioinform..

[7]  Paul Pavlidis,et al.  “Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks , 2012, PLoS Comput. Biol..

[8]  Giovanni Scardoni,et al.  Analyzing biological network parameters with CentiScaPe , 2009, Bioinform..

[9]  M. Oti,et al.  The modular nature of genetic diseases , 2006, Clinical genetics.

[10]  Van-Huy Pham,et al.  HGPEC: a Cytoscape app for prediction of novel disease-gene and disease-disease associations and evidence collection based on a random walk on heterogeneous network , 2017, BMC Systems Biology.

[11]  Michele Benzi,et al.  On the Limiting Behavior of Parameter-Dependent Network Centrality Measures , 2013, SIAM J. Matrix Anal. Appl..

[12]  Bruce Winney,et al.  Multiple rare variants in different genes account for multifactorial inherited susceptibility to colorectal adenomas. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Ahmedin Jemal,et al.  Trends in Breast Cancer by Race and Ethnicity , 2003, CA: a cancer journal for clinicians.

[14]  Sheridan K. Houghten,et al.  Disease-Gene Association Using a Genetic Algorithm , 2014, 2014 IEEE International Conference on Bioinformatics and Bioengineering.

[15]  Sheridan K. Houghten,et al.  Evolutionary computation for disease gene association , 2015, 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

[16]  Narsingh Deo,et al.  Efficient community identification in complex networks , 2012, Social Network Analysis and Mining.

[17]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[18]  R. Piro,et al.  Computational approaches to disease‐gene prediction: rationale, classification and successes , 2012, The FEBS journal.

[19]  Qin Li,et al.  Genes regulated in MPTP-treated macaques and human Parkinson's disease suggest a common signature in prefrontal cortex , 2010, Neurobiology of Disease.

[20]  Aidong Zhang,et al.  Bridging centrality: graph mining from element level to group level , 2008, KDD.

[21]  David W. Corne,et al.  Techniques for highly multiobjective optimisation: some nondominated points are better than others , 2007, GECCO '07.

[22]  Rena Li,et al.  Deficiency of Complement Defense Protein CD59 May Contribute to Neurodegeneration in Alzheimer's Disease , 2000, The Journal of Neuroscience.

[23]  W F Bodmer,et al.  Target genes of beta-catenin-T cell-factor/lymphoid-enhancer-factor signaling in human colorectal carcinomas. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Chao Wu,et al.  Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes , 2012, BMC Bioinformatics.

[25]  R. Lévy,et al.  Psychiatric Phenomena in Alzheimer's Disease. IV: Disorders of Behaviour , 1990, British Journal of Psychiatry.

[26]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Artem Lysenko,et al.  Arete – candidate gene prioritization using biological network topology with additional evidence types , 2017, BioData Mining.

[28]  Bassem A. Hassan,et al.  Gene prioritization through genomic data fusion , 2006, Nature Biotechnology.

[29]  Peter J. Bentley,et al.  Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms , 1998 .

[30]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[31]  Robert L. Nussbaum,et al.  Mutation in the α-Synuclein Gene Identified in Families with Parkinson's Disease , 1997 .

[32]  J. Hughes,et al.  Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases. , 1992, Journal of neurology, neurosurgery, and psychiatry.

[33]  Dayou Liu,et al.  Genetic Algorithm with a Local Search Strategy for Discovering Communities in Complex Networks , 2013, Int. J. Comput. Intell. Syst..

[34]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[35]  Alan F. Scott,et al.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders , 2004, Nucleic Acids Res..

[36]  Jing Chen,et al.  Disease candidate gene identification and prioritization using protein interaction networks , 2009, BMC Bioinformatics.

[37]  Gary D. Bader,et al.  The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function , 2010, Nucleic Acids Res..

[38]  Sheridan K. Houghten,et al.  A methodology for disease gene association using centrality measures , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[39]  Hyoung-Gon Lee,et al.  Amyloid-β precursor protein promotes cell proliferation and motility of advanced breast cancer , 2014, BMC Cancer.

[40]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[41]  Mason A. Porter,et al.  Eigenvector-Based Centrality Measures for Temporal Networks , 2015, Multiscale Model. Simul..

[42]  Clara Pizzuti,et al.  Community detection in social networks with genetic algorithms , 2008, GECCO '08.

[43]  Luciano da Fontoura Costa,et al.  A Complex Networks Approach for Data Clustering , 2011, ArXiv.

[44]  J. D. Mills,et al.  RNA-Seq analysis of the parietal cortex in Alzheimer's disease reveals alternatively spliced isoforms related to lipid metabolism , 2013, Neuroscience Letters.

[45]  Michael L. Creech,et al.  Integration of biological networks and gene expression data using Cytoscape , 2007, Nature Protocols.

[46]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[47]  Alexa T. McCray,et al.  Application of Information Technology: Design of Genetics Home Reference: A New NLM Consumer Health Resource , 2004, J. Am. Medical Informatics Assoc..

[48]  Michael Q. Zhang,et al.  Network-based global inference of human disease genes , 2008, Molecular systems biology.

[49]  Bin Wu,et al.  Multi-objective community detection in complex networks , 2012, Appl. Soft Comput..

[50]  Duc-Hau Le,et al.  GPEC: A Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection , 2012, Comput. Biol. Chem..