Artificial Intelligence in Medicine
Volume 43, Issue 2 , Pages 87-97 , June 2008

A reliable method for cell phenotype image classification

Received 10 September 2007 ,Revised 28 February 2008 ,Accepted 10 March 2008.

References 

  1. Murphy RF. Putting proteins on the map. Nat Biotechnol. 2006;24:1223–1224
  2. Lang P, Yeow K, Nichols A, Scheer A. Cellular imaging in drug discovery. Nat Rev Drug Discov. 2006;5:343–356
  3. Hamilton N, Pantelic R, Hanson K, Teasdale RD. Fast automated cell phenotype classification. BMC Bioinform. 2007;8:110
  4. Huh WK, Falvo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, et al. Global analysis of protein localization in budding yeast. Nature. 2003;425(6959):686–691
  5. Fink JL, Aturaliya RN, Davis MJ, Zhang F, Hanson K, Teasdale MS, et al. LOCATE: a protein subcellular localization database. Nucleic Acids Res. 2006;34:[database issue]
  6. Bannasch D, Mehrle A, Glatting KH, Pepperkok R, Poustka A, Wiemann S. LIFEdb: a database for functional genomics experiments integrating information from external sources, and serving as a sample tracking system. Nucleic Acids Res. 2004;32:505–508
  7. Chebira A, Barbotin Y, Jackson C, Merryman T, Srinivasa G, Murphy RF, et al. A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinform. 2007;8:210
  8. Huang K, Murphy R. Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinform. 2004;5:78
  9. Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998;20(8):832–844
  10. Alizadeh A, Ross DT, Perou CM, van de Rijn M. Towards a novel classification of human malignancies based on gene expression. J Pathol. 2001;195:41–52
  11. Huang K, Murphy RF. Automated classification of subcellular patterns in multicell images without segmentation into single cells. In: IEEE international symposium on biomedical imaging: nano to macro. Arlington, USA, April 15–18. 2004;p. 1139–1142
  12. Conrad C, Erfle H, Warnat P, Daigle N, Lorch T, Ellenberg J, et al. Automatic identification of subcellular phenotypes on human cell arrays. Genome Res. 2004;14(6):1130–1136
  13. Chen X, Murphy RF. Objective clustering of proteins based on subcellular location patterns. J Biomed Biotech. 2005;2:87–95
  14. Jain AK, Duin RP, Mao J. Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell. 2000;22(1):4–35
  15. Hamilton N, Pantelic R, Hanson K, Fink JL, Karunaratne S, Teasdale RD. Automated sub-cellular phenotype classification. In: Conferences in research and the practice in information technology. Hobart, Australia (73). 2006;p. 67–72
  16. Lin CC, Tsai Y-S, Lin Y-S, Chiu T-Y, Hsiung C-C, Lee M-I, et al. Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization. Bioinformatics. 2007;23(24):3374–3381
  17. Ojala T, Pietikainen M, Maeenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 2002;24(7):971–987
  18. Bishop CM. Neural networks for pattern recognition. Oxford: Oxford University Press; 1995;
  19. Shan S, Zhang W, Chen X, Su Y, Gao W. Ensemble of piecewise FDA based on spatial histograms of local (Gabor) binary patterns for face recognition. In: Proceedings of the 18th international conference on pattern recognition. Hong Kong. 2006;p. 606–609
  20. Hagan MT, Menhaj M. Training feed-forward networks with the Marquardt algorithm. IEEE Trans Neural Networ. 1999;5(6):989–993
  21. Haralick RM. Statistical and structural approaches to texture. Proc IEEE. 1979;67(5):768–804
  22. Hamamoto Y, Uchimura S, Tomita S. On the behavior of artificial neural network classifiers in high-dimensional spaces. IEEE Trans Pattern Anal Mach Intell. 1996;18:571–574
  23. Demsar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res. 2006;7:1–30
  24. Kuncheva LI, Whitaker CJ. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn. 2003;51:181–207
  25. Nanni L. Comparison among feature extraction methods for HIV-1 protease cleavage site prediction. Pattern Recogn. 2006;39(4):711–713

PII: S0933-3657(08)00032-8

doi: 10.1016/j.artmed.2008.03.005

Artificial Intelligence in Medicine
Volume 43, Issue 2 , Pages 87-97 , June 2008