Abstract
The purpose of this work is to compare Knowledge Driven Variable Selection (KDVS), a novel method for biomarkers, processes and functions identification with the most frequently used pipeline in the analysis of high–throughput data (Standard pipeline). While in the Standard pipeline the biological knowledge is used after the variable selection and classification phase, in KDVS it is used a priori to structure the data matrix. We analyze the same gene expression dataset using \({\ell _1\ell _2}_{FS}\), a regularization method for variable selection and classification, choosing Gene Ontology (GO) as source of biological knowledge. We compare the lists identified by the pipelines with state–of–the–art benchmark lists of genes and GO terms known to be related with Parkinson’s disease (PD). The results indicate that KDVS performs significantly better than the Standard pipeline.
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References
Avramopoulos, D.: Genetics of alzheimer’s disease: recent advances. Genome Med. 1(3), 34 (2009)
Mrzljak, L., Munoz-Sanjuan, I.: Therapeutic strategies for huntingtons disease. Current Topics in Behavioral Neurosciences, pp. 1–41. Springer, Heidelberg (2013)
Zycinski, G., Barla, A., Verri, A.: Svs: Data and knowledge integration in computational biology. In: Conference Proceedings of the IEEE Engineering in Medicine and Biology Society 2011, pp. 6474–6478 (2012)
Zycinski, G., Barla, A., Squillario, M., Sanavia, T., Di Camillo, B., Verri, A.: Knowledge driven variable selection (kdvs) - a new approach to enrichment analysis of gene signatures obtained from high-throughput data. Source Code Biol. Med. 8(1), 2 (2013)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Da Huang, W., Sherman, B.T., Lempicki, R.A.: Systematic and integrative analysis of large gene lists using david bioinformatics resources. Nat. Protoc. 4(1), 44–57 (2009)
Zhang, B., Kirov, S., Snoddy, J.: Webgestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 33, W741–W748 (2005)
Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G.: Gene ontology: tool for the unification of biology. the gene ontology consortium. Nat. Genet. 25(1), 25–29 (2000)
Kanehisa, M., Goto, S.: Kegg: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 27–30 (2000)
Barla, A., Mosci, S., Rosasco, L., Verri, A.: A method for robust variable selection with signicance assessment. In: Proceedings of ESANN 2008 (2008)
Squillario, M., Barla, A.: A computational procedure for functional characterization of potential marker genes from molecular data: Alzheimer’s as a case study. BMC Med. Genomics 4, 55 (2011)
Fardin, P., Barla, A., Mosci, S., Rosasco, L., Verri, A., Varesio, L.: The l1–l2 regularization framework unmasks the hypoxia signature hidden in the transcriptome of a set of heterogeneous neuroblastoma cell lines. BMC Genom. 10, 474 (2009)
Edgar, R., Domrachev, M., Lash, A.E.: Gene expression omnibus: Ncbi gene expression and hybridization array data repository. Nucleic Acids Res. 30(1), 207–210 (2002)
Scherzer, C.R., Eklund, A.C., Morse, L.J., Liao, Z., Locascio, J.J., Fefer, D., Schwarzschild, M.A., Schlossmacher, M.G., Hauser, M.A., Vance, J.M., Sudarsky, L.R., Standaert, D.G., Growdon, J.H., Jensen, R.V., Gullans, S.R.: Molecular markers of early parkinson’s disease based on gene expression in blood. Proc. Natl. Acad. Sci. U. S. A. 104(3), 955–960 (2007)
Irizarry, R.A., Bolstad, B.M., Collin, F., Cope, L.M., Hobbs, B., Speed, T.P.: Summaries of affymetrix genechip probe level data. Nucleic Acids Res. 31(4), e15 (2003)
De Mol, C., De Vito, E., Rosasco, L.: Elastic net regularization in learning theory. J. Complex. 25, 201–230 (2009)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. Ser. B 67, 301–320 (2005)
Ambroise, C., McLachan, G.: Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. U. S. A. 99(10), 6562–6566 (2002)
Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A.J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J.Y., Zhang, J.: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5(10), R80 (2004)
Leung, Y.F., Cavalieri, D.: Fundamentals of cdna microarray data analysis. Trends Genet. 19(11), 649–659 (2003)
Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., Mesirov, J.P.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102(43), 15545–15550 (2005)
Yu, W., Wulf, A., Liu, T., Khoury, M.J., Gwinn, M.: Gene prospector: an evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases. BMC Bioinf. 9, 528 (2008)
Jomova, D.V.K., Lawson, M., Valko, M.: Metals, oxidative stress and neurodegenerative disorders. Mol Cell Biochem. 345, 91–104 (2010)
Gundersen, V.: Protein aggregation in parkinson’s disease. Acta. Neurol. Scand. Suppl. 122, 82–87 (2010)
Ronchi, C., Cartelli, D., Rodighiero, S., Maggioni, M.G., Cappelletti, G., Giavini, E.: J. Neurochemestry 115(1), 247–258 (2010)
Rohn, T.T., Catlin, L.W.: Immunolocalization of influenza a virus and markers of inflammation in the human parkinson’s disease brain. PLoS One 6(5), e20495 (2011)
Clayton, D.F., George, J.M.: The synucleins: a family of proteins involved in synaptic function, plasticity, neurodegeneration and disease. Trends Neurosci. 21(6), 249–254 (1998)
Tezenas du Montcel, S., Charles, P., Goizet, C., Marelli, C., Ribai, P., Vincitorio, C., Anheim, M., Guyant-Marchal, L., Le Bayon, A., Vandenberghe, N., Tchikviladz, M., Devos, D., Le Ber, I., N’Guyen, K., Cazeneuve, C., Tallaksen, C., Brice, A., Durr, A.: Factors influencing disease progression in autosomal dominant cerebellar ataxia and spastic paraplegia. Arch. Neurol. 69(4), 500–508 (2012)
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Squillario, M., Zycinski, G., Barla, A., Verri, A. (2014). Identification of Pathway Signatures in Parkinson’s Disease with Gene Ontology and Sparse Regularization. In: Formenti, E., Tagliaferri, R., Wit, E. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in Computer Science(), vol 8452. Springer, Cham. https://doi.org/10.1007/978-3-319-09042-9_19
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