A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion

Journal of Biomedical Informatics

Volumn 48, Issue , 2014, Pages 114-121

  Cai, Binghuang ^a   Jiang, Xia ^a  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

Biomedical prediction and classification; Cancer; Least absolute shrinkage and selection operator (LASSO); Matrix pseudo inversion; Neural networks; Single nucleotide polymorphism (SNP)

Indexed keywords

DIAGNOSIS; DISEASES; FORECASTING; ITERATIVE METHODS; LEARNING ALGORITHMS; MEDICAL APPLICATIONS; NEURAL NETWORKS; NUCLEOTIDES; SHRINKAGE; SUPPORT VECTOR MACHINES; ARTIFICIAL INTELLIGENCE; COMPUTER AIDED DIAGNOSIS; LEARNING SYSTEMS;

ARTIFICIAL NEURAL NETWORK METHODS; BIOMEDICAL APPLICATIONS; CANCER; DISEASE CLASSIFICATION; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATORS; MACHINE LEARNING METHODS; PSEUDO-INVERSION; SINGLE-NUCLEOTIDE POLYMORPHISMS;

MATRIX ALGEBRA;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; CONTROLLED STUDY; DISEASE CLASSIFICATION; MACHINE LEARNING; PRIORITY JOURNAL; SINGLE NUCLEOTIDE POLYMORPHISM; SUPPORT VECTOR MACHINE; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR METHOD; LOGISTIC REGRESSION ANALYSIS; MATRIX PSEUDO INVERSION; MATRIX PSEUDO INVERSION ARTIFICIAL NEURAL NETWORK METHOD; MEASUREMENT ACCURACY; POSITIVE FEEDBACK; PREDICTION; ARTIFICIAL INTELLIGENCE; BREAST NEOPLASMS; FEMALE; GENETICS; GENOMICS; HUMAN; MEDICAL INFORMATICS; NEOPLASMS; PROCEDURES; REGRESSION ANALYSIS; REPRODUCIBILITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BREAST NEOPLASMS; FEMALE; GENOMICS; HUMANS; MEDICAL INFORMATICS; MODELS, STATISTICAL; NEOPLASMS; NEURAL NETWORKS (COMPUTER); POLYMORPHISM, SINGLE NUCLEOTIDE; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SUPPORT VECTOR MACHINES;

EID: 84899483858     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2013.12.009     Document Type: Article

References (40)

1. Shortliffe E.H., Cimino J.J. Biomedical informatics: computer applications in health care and biomedicine 2006, Springer, New York.
2. Kim D., Shin H., Song Y.S., Kim J.H. Synergistic effect of different levels of genomic data for cancer clinical outcome prediction. J Biomed Inform 2012, 45:1191-1198.
3. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537.
4. Cesario A., Marcus F.B. Cancer systems biology, bioinformatics and medicine - research and clinical applications 2011, Springer, New York.
5. Zhou X., Liu K.Y., Wong S.T.C. Cancer classification and prediction using logistic regression with Bayesian gene selection. J Biomed Inform 2004, 37:249-259.
6. Long Q., Moreno C.S., Johnson B.A. Risk prediction for prostate cancer recurrence through regularized estimation with simultaneous adjustment for nonlinear clinical effects. Ann Appl Stat 2011, 5(3):2003-2023.
7. Wang Y., Makedon F.S., Ford J.C., Pearlman J. HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics 2005, 21(8):1530-1537.
8. Jiang X., Cooper G.F. A Bayesian spatio-temporal method for disease outbreak detection. J Am Med Inform Assoc 2010, 17(4):462-471.
9. Saritas I. Prediction of breast cancer using artificial neural networks. J Med Syst 2012, 36:2901-2907.
10. Hua J., Lowey J., Xiong Z., Dougherty E.R. Noise-injected neural networks show promise for use on small-sample expression data. BMC Bioinformatics 2006, 7:274.
11. Lancashire L.J., Powe D.G., Reis-Filho J.S., Rakha E., Lemetre C., Weigelt B., et al. A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks. Breast Cancer Res Treat 2010, 120:83-93.
12. Steriti R.J., Fiddy M.A. Regularized image reconstruction using SVD and a neural network method for matrix inversion. IEEE Trans Signal Process 1993, 41(10):3074-3077.
13. Zhang Y., Cai B. Artificial neural networks progress and debate during publishing process 2010, Publishing House of Electronics Industry, Beijing.
14. Zhang Y, Li W, Yi C, Chen K. A weights-directly-determined simple neural network for nonlinear system identification. In: Proceedings of IEEE international conference on fuzzy systems; 2008. p. 455-60.
15. Rumelhart D.E., McClelland J.L. Parallel distributed processing 1986, PDP Research Group, MIT Press, Cambridge (USA).
16. Belciug S., Gorunescu F. A hybrid neural network/genetic algorithm applied to breast cancer detection and recurrence. Expert Syst 2013, 30(3):243-254.
17. Huang G.B., Zhu Q.Y., Siew C.K. Extreme learning machine: theory and applications. Neurocomputing 2006, 70(1-3):489-501.
18. Hastie T., Tibshirani R., Friedman J. The elements of statistical learning: data mining, inference, and prediction 2009, Springer, New York. 2nd ed.
19. Hans C. Bayesian lasso regression. Biometrika 2009, 96(4):835-845.
20. Cybenko G. Approximations by superpositions of sigmoidal functions. Math Control Signals Syst 1989, 2(4):303-314.
21. Tamura S., Tateishi M. Capabilities of a four-layered feedforward neural network: four layers versus three. IEEE Trans Neural Networks 1997, 8(2):251-255.
22. Zhang Y, Li Z, Chen K, Cai B. Common nature of learning exemplified by BP and Hopfield neural networks for solving online a system of linear equations. In: Proceedings of IEEE international conference on networking, sensing and control; 2008. p. 832-6.
23. Serre D. Matrices: theory and applications 2002, Springer, New York.
24. Optimization toolbox user's guide. Version 3.0.3. Natick (MA): The MathWorks Inc.; 2005.
25. Cai B., Zhang Y. Different-level redundancy-resolution and its equivalent relationship analysis for robot manipulators using gradient-descent and Zhang et al.'s neural-dynamic methods. IEEE Trans Ind Electron 2012, 59(8):3146-3155.
26. Cai B., Zhang Y. Bi-criteria optimal control of redundant robot manipulators using LVI-based primal-dual neural network. Optim Control Appl Methods 2010, 31(3):213-229.
27. Efron B., Hastie T., Johnstone I., Tibshirani R. Least angle regression (with discussion). Ann Stat 2004, 32(2):407-499.
28. http://code.google.com/p/lasso4j/.
29. http://cran.r-project.org/web/packages/glmnet/index.html.
30. http://www.r-project.org/.
31. Velez D.R., White B.C., Motsinger A.A. A balanced accuracy function for epistasis modeling in imbalanced dataset using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306-315.
32. Mangasarian O.L., Setiono R., Wolberg W.H. Pattern recognition via linear programming: theory and application to medical diagnosis. Large-scale numerical optimization 1990, 22-30. SIAM Publications, Philadelphia. T.F. Coleman, Y. Li (Eds.).
33. Jiang X., Barmada M.M., Visweswaran S. Identifying genetic interactions in genome-wide data using Bayesian networks. Genet Epidemiol 2010, 34(6):575-581.
34. Jiang X., Barmada M.M., Cooper G.F., Becich M.J. A Bayesian method for evaluating and discovering disease loci associations. PLoS One 2011, 6(8):e22075.
35. Jiang X., Neapolitan R.E., Barmada M.M., Visweswaran S. Learning genetic epistasis using Bayesian network scoring criteria. BMC Bioinformatics 2011, 12(89).
36. http://www.java.com/en/.
37. http://math.nist.gov/javanumerics/jama/.
38. http://www.eclipse.org/.
39. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceeding of the international joint conference on artificial intelligence; 1995.
40. Fan R.E., Chang K.W., Hsieh C.J., Wang X.R., Lin C.J. LIBLINEAR: a library for large linear classification. J Mach Learn Res 2008, 9:1871-1874. Software available at