Machine learning methods predict locomotor response to MK-801 in mouse models of down syndrome

Journal of Neurogenetics

Volumn 25, Issue 1-2, 2011, Pages 40-51

  Nguyen, Cao D ^a   Costa, Alberto C S ^b,c   Cios, Krzysztof J ^a,d   Gardiner, Katheleen J ^c,e,f  

^a Virginia Commonwealth University   (United States)

^b Division of Toxicology   (United States)

^c Intellectual and Developmental Disabilities Research Center   (United States)

^d Department of Pediatrics   (United States)

^e POLISH ACADEMY OF SCIENCES   (Poland)

^f Children's Hospital Colorado   (United States)

Author keywords

behavior prediction; Down syndrome; Na ve Bayes; Ts1Cje; Ts65Dn

Indexed keywords

DIZOCILPINE;

ACCURACY; ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; CHROMOSOME 21; COMPUTER PREDICTION; CONTROLLED STUDY; DOWN SYNDROME; GENETIC VARIABILITY; GENOTYPE; LEARNING; LOCOMOTION; MACHINE LEARNING; MEMORY; MOUSE; NONHUMAN; PRIORITY JOURNAL;

ADAPTOR PROTEINS, VESICULAR TRANSPORT; ANIMALS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DISEASE MODELS, ANIMAL; DIZOCILPINE MALEATE; DOSE-RESPONSE RELATIONSHIP, DRUG; DOWN SYNDROME; GUANINE NUCLEOTIDE EXCHANGE FACTORS; HUMANS; LOCOMOTION; MICE; MICE, INBRED C57BL; MICE, TRANSGENIC; NERVE TISSUE PROTEINS; NEUROPROTECTIVE AGENTS; PREDICTIVE VALUE OF TESTS; PROTEIN-SERINE-THREONINE KINASES; PROTEIN-TYROSINE KINASES; STATISTICS AS TOPIC;

EID: 79956025814     PISSN: 01677063     EISSN: 15635260     Source Type: Journal    
DOI: 10.3109/01677063.2011.558606     Document Type: Article

References (39)

1. Adayev, T., Chen-Hwang, M. C., Murakami, N., Wang, R., & Hwang, Y.W. (2006). MNB/DYRK1A phosphorylation regulates the interactions of synaptojanin 1 with endocytic accessory proteins. Biochem Biophys Res Commun, 351 , 1060-1065. (Pubitemid 44715892)
2. Ahn, Y. M., Seo, M. S., Kim, S. H., Kim, Y., Juhnn, Y. S., & Kim, Y. S. (2006). The effects of MK-801 on the phosphorylation of Ser338-c-Raf-MEK-ERK pathway in the rat frontal cortex. Int J Neuropsychopharmacol 4 , 1-6
3. Ahn,Y. M., Seo, M. S., Kim, S. H., Kim, Y., Yoon, S. C., Juhnn, Y. S., & Kim, Y. S. (2005). Increased phosphorylation of Ser473-Akt, Ser9-GSK-3beta and Ser133-CREB in the rat frontal cortex after MK-801 intraperitoneal injection. Int J Neuropsychopharmacol 8 , 607-613. (Pubitemid 41400892)
4. Benavides-Piccione, R., Ballesteros-Yáñ ez, I., de Lagrán, M. M., Elston, G., Estivill, X., Fillat, C., Defelipe, J., & Dierssen, M. (2004). On dendrites in Down syndrome and DS murine models: A spiny way to learn. Prog Neurobiol 74 , 111-126 . (Pubitemid 39423861)
5. Canfield, M. A., Honein, M. A., Yuskiv, N., Xing, J., Mai, C. T., Collins, J. S., et al. (2006). National estimates and race/ethnic-specific variation of selected birth defects in the United States, 1999-2001. Birth Defects Res A Clin Mol Teratol 76 , 747-756.
6. Chapman, R. S., & Hesketh, L. J. (2001). Language, cognition, and short-term memory in individuals with Down syndrome. Downs Syndr Res Pract 7 , 1-7.
7. Chen, S., Cowan, C. N. F., & Grant, P. M. (1991). Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Networks 2 , 302-309.
8. Cortes, C., & Vapnik. V. (1995). Support-vector networks. Machine Learn 20, 273-297
9. Costa, A. C., Scott-McKean, J. J., & Stasko, M. R. (2008). Acute injections of the NMDA receptor antagonist memantine rescue performance deficits of the Ts65Dn mouse model of Down syndrome on a fear conditioning test. Neuropsycopharmacology 33 , 1624-1632. (Pubitemid 351684704)
10. Costa, A. C., Stasko, M. R., Schmidt, C., & Davisson, M. T. (2010). Behavioral validation of the Ts65Dn mouse model for Down syndrome of a genetic background free of the retinal degeneration mutation Pde6b(rd1). Behav Brain Res . 206 , 52-62.
11. Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Ann Stat, 7 , 1-26.
12. Espina, V., Wulfkuhle, J., & Liotta, L. A. (2009). Application of laser microdissection and reverse-phase protein microarrays to the molecular profi ling of cancer signal pathway networks in the tissue microenvironment. Clin Lab Med 29, 1-13.
13. Fernandez-Martinez, J., Vela, E. M., Tora-Ponsioen, M., Ocaña, O. H., Nieto, M. A., & Galceran, J. (2009). Attenuation of Notch signalling by the Down-syndrome-associated kinase DYRK1A. J Cell Sci 122 , 1574-1583.
14. Fleming, I. N., Batty, I. H., Prescott, A. R., Gray, A., Kular, G. S., Stewart, H., & Downes, C. P. (2004). Inositol phospholipids regulate the guanine-nucleotide-exchange factor Tiam1 by facilitating its binding to the plasma membrane and regulating GDP/GTP exchange on Rac1. Biochem J 382 , 857-865. (Pubitemid 39312900)
15. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Ann Stat, 38, 337-374.
16. Galceran, J., de Graaf, K., Tejedor, F. J., & Becker, W. (2003). The MNB/DYRK1A protein kinase: Genetic and biochemical properties. J Neural Transm Suppl 67 , 139-148. (Pubitemid 38339929)
17. Gardiner, K., & Costa, A. C. (2006). The genes of human chr21: Choosing candidates for relevance to intellectual disability. Am J Med Genet 142, 196-205.
18. Gardiner, K., Fortna, A., Bechtel, L., & Davisson, M. T. (2003). Mouse models of Down syndrome: How useful can they be? Comparison of the gene content of human chromosome 21 with orthologous mouse genomic regions. Gene 318 , 137-147. (Pubitemid 37338399)
19. Gardiner, K. J. (2010). Molecular basis of pharmacotherapies for cognition in Down syndrome. Trends in Pharmacol Sci 32 , 66-73 .
20. Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learn, 46 , 389-422. (Pubitemid 34129977)
21. John, G., Kohavi, R., & Pfleger, K. (1994). Irrelevant features and the subset selection problem. In Proceedings 11th International Conference on Machine Learning (pp. 121 - 129). New Brunswick, NJ: Morgan Kaufmann, Los Altos.
22. Koller, D., & Sahami, M. (1996). Toward optimal feature selection. In Proceedings 13th International Conference on Machine Learning (pp. 284 - 292). Bari, Italy, Morgan Kaufmann, San Mateo, CA.
23. Langley, P., Iba, W., & Thompson, K (1992). An analysis of Bayesian classifiers. In Proceedings of the Tenth National Conference on Artificial Intelligence (1992) San Jose: AAAI Press. pp. 223-228.
24. +)-dependent phosphatase. Nature 369 , 235-239. (Pubitemid 24154801)
25. Lockrow, J., Boger, H., Bimonte-Nelson, H., & Granholm, A. C. (2010). Effects of long-term memantine on memory and neuropathology in Ts65Dn mice, a model for Down syndrome. Behav Brain Res Apr 2.
26. [ Epub ahead of print] Maier, T., Güell, M., & Serrano L. (2009). Correlation of mRNA and protein in complex biological samples, FEBS Lett. 583 , 3966-3973.
27. McCallum, A., & Nigam, K. (1998). A comparison of event models for naive Bayes text classification. In AAAI-98 Workshop on Learning for Text Categorization (pp. 41- 48).
28. Miyakawa, T., Leiter, L. M., Gerber, D. J., Gainetdinov, R. R., Sotnikova, T. D., Zeng, H., Caron, M. G., & Tonegawa, S. (2003). Conditional calcineurin knockout mice exhibit multiple abnormal behaviors related to schizophrenia. Proc Natl Acad Sci USA 100 , 8987-8992. (Pubitemid 36899210)
29. Nadel, L. (2003). Down ' s syndrome: A genetic disorder in biobehavioral perspective. Genes Brain Behav , 2 , 156-166. (Pubitemid 36898802)
30. Nishimura, T., Yamaguchi, T., Tokunaga, A., Hara, A., Hamaguchi, T., Kato, K., Iwamatsu, A., Okano, H., & Kaibuchi, K. (2006). Role of numb in dendritic spine development with a Cdc42 GEF intersectin and EphB2. Mol Biol Cell 17 , 1273-1285. (Pubitemid 43376548)
31. Park, J., & Sandberg, I. W. (1991). Universal approximation using radial-basis-function networks. Neural Comput, 3 , 246-257.
32. Pritchard, M., Reeves, R. H., Dierssen, M., Patterson, D., & Gardiner, K. (2008). Down syndrome and the genes of human chromosome 21: Current knowledge and future potentials. Cyto Genet Genome Res 121 , 67-77.
33. Rueda, N., Llorens-Martín, M., Flórez J, Valdizán, E., Banerjee, P., Trejo, J. L., & Martínez-Cué , C. (2010). Memantine normalizes several phenotypic features in the Ts65Dn mouse model of Down syndrome. J Alzheimers Dis . 21 , 277-290.
34. Salehi, A., Faizi, M., Colas, D., Valletta, J., Laguna, J., Takimoto-Kimura, R., Kleschevnikov, A., Wagner, S. L., Aisen, P., Shamloo, M., & Mobley, W. C. (2009). Restoration of norepinephrine-modulated contextual memory in a mouse model of Down syndrome. Sci Transl Med, 1 , 7ra17.
35. Sananbenesi, F., Fischer, A., Schrick, C., Spiess, J., & Radulovic, J. (2002). Phosphorylation of hippocampal Erk-1/2, Elk-1, and p90-Rsk-1 during contextual fear conditioning: Interactions between Erk-1/2 and Elk-1. Mol Cell Neurosci 21 , 463-476. (Pubitemid 36062732)
36. Siddiqui, A., Lacroix, T., Scott-McKean, J., Stasko, M., Costa, A. C., & Gardiner, K. (2008). Molecular responses of the Ts65Dn and Ts1Cje mouse models of Down syndrome to MK-801. Genes Brain Behav 7 , 810-820.
37. Tolias, K. F., Bikoff, J. B., Kane, C. G., Tolias, C. S., Hu, L., & Greenberg, M. E. (2007). The Rac1 guanine nucleotide exchange factor Tiam1 mediates EphB receptor-dependent dendritic spine development. Proc Natl Acad Sci U S A 104 , 7265-7270 . (Pubitemid 47186043)
38. Wiseman, F. K., Alford, K. A., Tybulewicz, V. L., & Fisher, E. M. (2009). Down syndrome-recent progress and future prospects. Hum Mol Genet 18(R1) , R75-R83.
39. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed.) . San Francisco: Morgan Kaufmann.