How long will my mouse live? Machine learning approaches for prediction of mouse life span

Journals of Gerontology - Series A Biological Sciences and Medical Sciences

Volumn 63, Issue 9, 2008, Pages 895-906

  Swindell, William R ^a,b   Harper, James M ^a   Miller, Richard A ^a  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Aging; Classification; Longevity; Shrunken centroid; T cell subset; Weight

Indexed keywords

ANIMAL EXPERIMENT; ARTICLE; BODY WEIGHT; CONTROLLED STUDY; FEMALE; LEARNING ALGORITHM; LIFESPAN; LONGEVITY; MACHINE LEARNING; MALE; MATHEMATICAL MODEL; MOUSE; NONHUMAN; PHENOTYPE; PRIORITY JOURNAL; T LYMPHOCYTE;

MUS;

EID: 54249167191     PISSN: 10795006     EISSN: None     Source Type: Journal    
DOI: 10.1093/gerona/63.9.895     Document Type: Article

References (72)

1. Botwinick J, West R, Storandt M. Predicting death from behavioral test performance. J Gerontol. 1983;33:755-762.
2. Harrison DE, Archer JR. Biomarkers of aging: tissue markers. Future research needs, strategies, directions and priorities. Exp Gerontol. 1988;23:309-321.
3. Reynolds MA, Ingram DK, Talan M. Relationship of body temperature stability to mortality in aging mice. Mech Ageing Dev. 1985;30:143-152.
4. Ingram DK, Reynolds MA. Assessing the predictive validity of psychomotor tests as measures of biological age in mice. Exp Aging Res. 1986;12:155-162.
5. Miller RA, Chrisp C, Galecki A. CD4 memory T cell levels predict lifespan in genetically heterogenous mice. FASEB J. 1997;11:775-783.
6. Miller RA. Biomarkers of aging: prediction of longevity by using age-sensitive T-cell subset determinations in a middle-aged, genetically heterogeneous mouse population. J Gerontol Biol Sci. 2001;56A:B180-B186.
7. Miller RA, Chrisp C. T cell subset patterns that predict resistance to spontaneous lymphoma, mammary adenocarcinoma, and fibrosarcoma in mice. J Immunol. 2002;169:1619-1625.
8. Miller RA, Harper JM, Galecki A, Burke D. Big mice die young: early life body weight predicts longevity in genetically heterogeneous mice. Aging Cell. 2002;1:22-29.
9. Harper JM, Wolf N, Galecki AT, Pinkosky SL, Miller RA. Hormone levels and cataract scores as sex-specific, mid-life predictors of longevity in genetically heterogeneous mice. Mech Ageing Dev. 2003;124:801-810.
10. Anisimov VN, Arbeev KG, Popovich IG, Zabezhinksi MA, Arbeeva LS, Yashin AI. Is early life body weight a predictor of longevity and tumor risk in rats? Exp Gerontol. 2004;39:807-816.
11. Harper JM, Galecki AT, Burke DT, Miller RA. Body weight, hormones and T cell subsets as predictors of life span in genetically heterogeneous mice. Mech Ageing Dev. 2004;125:381-390.
12. Miller RA, Austad S. Why do big dogs die young? In: Masaro E, Austad S, eds. Handbook of the Biology of Aging. San Diego, CA: Academic Press; 2005:512-533.
13. Ramsey JJ, Colman RJ, Binkley NC, et al. Dietary restriction and aging in rhesus monkeys: the University of Wisconsin study. Exp Gerontol. 2000;35:1131-1149.
14. Mattison JA, Lane MA, Roth GS, Ingram DK. Caloric restriction in rhesus monkeys. Exp Gerontol. 2003;38:35-46.
15. Flurkey K, Currer JM, Harrison DE. The mouse in aging research. In: Fox JG, Barthold S, Davisson M, Newcomer CE, Quimby FW, Smith A, eds. The Mouse in Biomedical Research, Volume 3. Burlington, MA: Elsevier; 2007:637-672.
16. Vieira C, Pasyukova EG, Zeng ZB, Hackett JB, Lyman RF, Mackay TF. Genotype-environment interaction for quantitative trait loci affecting life span in Drosophila melanogaster. Genetics. 2000;154:213-227.
17. Vermeulen CJ, Bijlsma R. Changes in mortality patterns and temperature dependence of lifespan in Drosophila melanogaster caused by inbreeding. Heredity. 2004;92:275-281.
18. Swindell WR, Bouzat JL. Inbreeding depression and male survivorship in Drosophila: implications for senescence theory. Genetics. 2006;172:317-327.
19. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W. Applied Linear Regression Models. Chicago, IL: McGraw-Hill; 1996.
20. Duda RO, Hart PE, Stork DG. Pattern Classification. 2nd Ed. New York: John Wiley and Sons; 2001.
21. Liu CL, Nakashima K, Sako H, Fujisawa H. Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognit. 2003;36:2271-2285.
22. Teow LN, Loe KF. Robust vision-based features and classification schemes for off-line handwritten digit recognition. Pattern Recognit. 2002;35:2355-2364.
23. Andorf C, Dobbs D, Honavar V. Exploring inconsistencies in genomewide protein function annotations: a machine-learning approach. BMC Bioinformatics. 2007;8:284.
24. Li S, Shi F, Pu F, et al. Hippocampal shape analysis of Alzheimer disease based on machine learning methods. Am J Neuroradiol. 2007;28:1339-1345.
25. Zernov VV, Balakin KV, Ivaschenko AA, Savchuk NP, Pletnev IV. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J Chem Inf Comput Sci. 2003;43:2048-2056.
26. Fox T, Kriegl JM. Machine learning techniques for in silico modeling of drug metabolism. Curr Top Med Chem. 2006;6:1579-1591.
27. Miller RA, Austad S, Burke D, et al. Exotic mice as models for aging research: polemic and prospectus. Neurobiol Aging. 1999;20:217-231.
28. Troyanskaya O, Cantor M, Sherlock G, et al. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001;17:520-525.
29. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A. 2002;99:6567-6572.
30. Laeuter J, Glimm E, Kropf S. Multivariate tests based on left-spherically distributed linear scores. Ann Stat. 1998;26:1972-1988.
31. Byvatov E, Schneider G. Support vector machine applications in bioinformatics. Appl Bioinformatics. 2003;2:67-77.
32. Williams CKI, Barber D. Bayesian classification with Gaussian processes. IEEE Trans Pattern Anal Mach Intell. 1998;20:1342-1351.
33. Breiman L. Random forests. Mach Learn. 2001;45:5-32.
34. Platt JC. Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf C, Burges C, Smola A, eds. Advances in Kernal Methods-Support Vector Learning. Cambridge, MA: MIT Press; 1998:185-208.
35. Tutz G, Binder H. Localized classification. Statistics and Computing. 2005;15:155-166.
36. Lee EK, Cook D, Klinke S, Lumley T. Projection pursuit for exploratory supervised classification. J Comput Graph Stat. 2005;14:831-846.
37. le Cessie S, van Houwelingen JC. Ridge estimators in logistic regression. Appl Stat. 1992;41:191-201.
38. Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods. 2000;43:3-31.
39. Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat. 2006;15:651-674.
40. Patrick EA, Fischer FP. A generalized k-nearest neighbor rule. Information and Control 1970;16:128-152.
41. Quinlan R. C4.5: Programs for Machine Learning. San Diego, CA: Morgan Kaufmann; 1993.
42. Frank E, Witten IH. Generating accurate rule sets without global optimization. In: Shavlik J, ed. Machine Learning: Proceedings of the Fifteenth International Conference. San Francisco, CA: Morgan Kaufmann Publishers; 1998:144-151.
43. Cohen WW. Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning. Lake Tahoe, CA: Morgan Kaufmann; 1995:115-123.
44. Diaz-Uriarte R, Alvarez de Andrés S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics. 2006;7:3.
45. Bauer JH, Goupil S, Garber GB, Helfand SL. An accelerated assay for the identification of lifespan-extending interventions in Drosophila melanogaster. Proc Natl Acad Sci U S A. 2004;101:12980-12985.
46. Sharma P, Sahni NS, Tibshirani R, et al. Early detection of breast cancer based on gene-expression patterns in peripheral blood cells. Breast Cancer Res. 2005;7:R634-R644.
47. Liu CL, Sako H. Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition. Pattern Recognit. 2006;39:669-681.
48. Patronek GJ, Waters DJ, Glickman LT. Comparative longevity of pet dogs and humans: implications for gerontology research. J Gerontol Biol Sci. 1997;52A:B171-B178.
49. Speakman JR, van Acker A, Harper EJ. Age-related changes in the metabolism and body composition of three dog breeds and their relationship to life expectancy. Aging Cell. 2003;2:265-275.
50. Greer KA, Canterberry SC, Murphy KE. Statistical analysis regarding the effects of height and weight on life span of the domestic dog. Res Vet Sci. 2007;82:208-214.
51. Harper JM, Durkee R, Dysko C, Austad SN, Miller RA. Genetic modulation of hormone levels and life span in hybrids between laboratory and wild-derived mice. J Gerontol A Biol Sci Med Sci. 2006;61:1019-1029.
52. Sharpless NE. Ink4a/Arf links senescence and aging. Exp Gerontol. 2004;39:1751-1759.
53. Ressler S, Bartkova J, Niederegger H, et al. p16ink4a is a robust in vivo biomarker of cellular aging in human skin. Aging Cell. 2006;5:379-389.
54. Rea SL, Wu D, Cypser JR, Vaupel JW, Johnson TE. A stress-sensitive reporter predicts longevity in isogenic populations of Ceanorhabditis elegans. Nat Genet. 2005;37:894-898.
55. Flurkey K, Papaconstantinou J, Miller RA, Harrison DE. Lifespan extension and delayed immune and collagen aging in mutant mice with defects in growth hormone production. Proc Natl Acad Sci U S A. 2001;98:6736-6741.
56. Costa PT, McCrae RR. Concepts of functional or biological age: a critical view. In: Andres R, Bierman EL, Hazzard WR, eds. Principles of Geriatric Medicine. New York: McGraw Hill; 1985:30-37.
57. Adelman RC. Biomarkers of aging. Exp Gerontol. 1987;22:227-229.
58. Costa PT, McCrae RR. Measures and markers of biological aging: "a great clamoring. . .of fleeting significance." Arch Gerontol Geriat. 1988;7:211-214.
59. Miller RA. Biomarkers of aging. Sci Aging Knowledge Environ. 2001;1:pe2.
60. Harrison DE, Archer JR. Physiological assays for biological age in mice: relationship to collagen, renal function, and longevity. Exp Aging Res. 1983;9:245-251.
61. Ingram DK. Toward the behavioral assessment of biological aging in the laboratory mouse: concepts, terminology, and objectives. Exp Aging Res. 1983;9:225-238.
62. Ingram DK. Key questions in developing biomarkers of aging. Exp Gerontol. 1988;23:429-434.
63. Butler RN, Sprott R, Warner H, et al. Biomarkers of aging: from primitive organisms to humans. J Gerontol A Biol Sci Med Sci. 2004;59:560-567.
64. Bowden DM, Short RA, Williams DD. Constructing an instrument to measure the rate of aging in female pigtailed macaques (Macaca nemestrina). J Gerontol Biol Sci. 1990;45A:B59-B66.
65. Nakamura E, Miyao K. Further evaluation of the basic nature of the human biological aging process based on a factor analysis of age-related physiological variables. J Gerontol A Biol Sci Med Sci. 2003;58:196-204.
66. Nakamura E, Miyao K. A method for identifying biomarkers of aging and constructing an index of biological age in humans. J Gerontol A Biol Sci Med Sci. 2007;62:1096-1105.
67. Olshansky SJ, Carnes BA, Cassel C. In search of Methuselah: estimating the upper limits to human longevity. Science. 1990;250:634-640.
68. Gadat S, Younes L. A stochastic algorithm for feature selection in pattern recognition. J Mach Learn Res. 2007;8:509-547.
69. Arriaga RI, Vempala S. An algorithmic theory of learning: robust concepts and random projection. Mach Learn. 2006;63:161-182.
70. Zhang P, Bui TD, Suen CY. A novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recognit. 2007;40:3415-3429.
71. Li Y, de Ridder D, Duin RPW, Reinders MJT. Integration of prior knowledge of measurement noise in kernel density classification. Pattern Recognit. 2008;41:320-330.
72. Brockwell PJ, Davis RA. Introduction to Time Series and Forecasting. 2nd Ed. New York: Springer; 2002.