Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble

BMC Bioinformatics

Volumn 12, Issue , 2011, Pages

  Zhang, Bailing ^a   Pham, Tuan D ^b  

^a XI AN JIAOTONG LIVERPOOL UNIVERSITY   (China)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY OF CLASSIFICATIONS; CLASSIFICATION PERFORMANCE; COMPONENT NEURAL NETWORKS; FEATURE EXTRACTION METHODS; GREY LEVEL CO-OCCURRENCE MATRIXES; IMAGE PROCESSING APPLICATIONS; MULTIPLE LAYER PERCEPTRON; RANDOM SUBSPACE CLASSIFIERS;

FEATURE EXTRACTION; NEURAL NETWORKS; WAVELET TRANSFORMS;

IMAGE PROCESSING;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; CHO CELL; CRICETULUS; CYTOLOGY; DROSOPHILA MELANOGASTER; FLUORESCENCE MICROSCOPY; HAMSTER; HELA CELL; HUMAN; IMAGE PROCESSING; METHODOLOGY; WAVELET ANALYSIS;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; CHO CELLS; CRICETINAE; CRICETULUS; DROSOPHILA MELANOGASTER; HELA CELLS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MICROSCOPY, FLUORESCENCE; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; WAVELET ANALYSIS;

EID: 79955369093     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-128     Document Type: Article

References (50)

1. Perlman ZE, Slack MD, Feng Y, Mitchison TJ, Wu LF, Altschuler SJ. Multidimensional drug profiling by automated microscopy. Science 2004, 306:1194-1198. 10.1126/science.1100709, 15539606.
2. Yarrow JC, Feng Y, Perlman ZE, Kirchhausen T, Mitchison TJ. Phenotypic screening of small molecule libraries by high throughput cell imaging. Comb Chem High Throughput Screening 2003, 6:279-286.
3. Boutros M, Kiger AA, Armknecht A, Kerr K, Hild M, Koch R, Haas SA, Paro R, Perrimon N. Genome-wide RNAi analysis of growth and viability in drosophila cells. Science 303:832-835.
4. Peng H. Bioimage informatics: a new area of engineering biology. Bioinformatics 2008, 24(17):1827-36. 10.1093/bioinformatics/btn346, 2519164, 18603566.
5. Weirauch MT, Wong CK, Byrne AB, Stuart JM. Information-based methods for predicting gene function from systematic gene knock-downs. Bioinformatics 2008, 9:463. 2596148, 18959798.
6. Clemens JC, Worby CA, Simonson-Leff N, Muda M, Maehama T, Hemmings BA, Dixon JE. Use of double-stranded RNA interference in Drosophila cell lines to dissect signal transduction pathways. Proc Natl Acad Sci 2000, 97:6499-6503. 10.1073/pnas.110149597, 18635, 10823906.
7. Hannon GJ. RNA interference. Nature 2002, 418:244-251. 10.1038/418244a, 12110901.
8. Echeverri CJ, Perrimon N. High-throughput RNAi screening in cultured cells: a user's guide. Nat Rev Genet 2006, 7:373-384.
9. Agaisse H, Burrack L, Philips J, Rubin E, Perrimon N, Higgins DE. Genome-wide RNAi screen for host factors required for intracellular bacterial infection. Science 2005, 309:1248-1251. 10.1126/science.1116008, 16020693.
10. Carpenter AE, Sabatini DM. Systematic genome-wide screens of gene function. Nature Reviews Genetics 2004, 5(1):11-22.
11. Wang J, Zhou X, Bradley PL, Chang SF, Perrimon N, Wong STC. Cellular Phenotype Recognition for High-Content RNA Interference Genome-Wide Screening. J Biomol Screen 2008, 13:29-39.
12. Zhou J, Peng H. Automatic recognition and annotation of gene expression patterns of fly embryos. Bioinformatics 2007, 23(5):589-596. 10.1093/bioinformatics/btl680, 17237064.
13. Orlov N, Johnston J, Macura T, Shamir L, Goldberg I. Computer Vision for Microscopy Applications. Vision Systems: Segmentation and Pattern Recognition 2007, 546. I-Tech, Vienna, Austria, June 2007, Goro Obinata, Ashish Dutta.
14. Jones TR, Kang IH, Wheeler DB, Lindquist RA, Rapallo A, Sabatini DM, Golland P, Carpenter AE. CellProfiler Analysis: data exploration and analysis software for complex image-based screens. Bioinformatics 2008, 9(1):482. 2614436, 19014601.
15. Shamir L, Orlov N, Eckley DM, Macura T, Goldberg I. IICBU 2008 - a proposed benchmark suite for biological image analysis. Medical & Biological Engineering & Computing 2008, 46:943-947.
16. Orlov N, Shamir L, Macura T, Johnston J, Eckley DM, Goldberg I. WNDCHRM - An Open Source Utility for Biological Image Analysis. BMC - Source Code for Biology and Medicine 2008, 3:13.
17. Shamir L, Eckley DM, Delaney J. An Image Informatics Method for Automated Quantitative Analysis of Phenotype Visual Similarities. IEEE/NIH Life Science Systems and Application Workshop 2009, LiSSA2009:96-99.
18. Boland MV, Murphy RF. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 2001, 17:1213-1223. 10.1093/bioinformatics/17.12.1213, 11751230.
19. Boland MV, Markey M, Murphy RF. Automated Recognition of Patterns Characteristic of Subcellular Structures in Fluorescence Microscopy Images. Cytometry 1998, 33:366-375. 10.1002/(SICI)1097-0320(19981101)33:3<366::AID-CYTO12>3.0.CO;2-R, 9822349.
20. Huang K, Murphy RF. Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics 2004, 5:78. 10.1186/1471-2105-5-78, 449699, 15207009.
21. Hamilton NA, Pantelic RS, Hanson K, Teasdale RD. Fast automated cell phenotype image classification. Bioinformatics 2007, 8:110. 1847687, 17394669.
22. Nanni L, Lumini A, Lin Y, Hsu C, Lin C. Fusion of systems for automated cell phenotype image classification. Expert Systems with Applications 2010, 37:1556-1562.
23. Donoho D, Duncan M. Digital Curvelet Transform: Strategy, Implementation and Experiments. 1999, Stanford University.
24. Starck J, Candes E, Donoho D. The Curvelet transform for image denoising. IEEE Transactions Image Processing 2002, 11:670-684.
25. Candes E, Donoho D. Curvelets: multiresolution representation, and scaling laws. 2000, 4119:2000. Wavelet Applications in Signal and ImageProcessing VIII, Proceeding of the SPIE, Aldroubi A, Laine AF, Unser MA.
26. Candes E, Demanet L, Donoho D, Ying L. Fast discrete curvelet transforms. Multiscale Model Simul 2006, 5:861-899.
27. Ma J, Plonka G. The Curvelet Transform: A review of recent applications. IEEE Signal Processing Magazine 2010, 27(2):118-133.
28. Ni L, Leng H. Curvelet transform and its application in image retrieval. 3rd International Symposium on Multispectral Image Processing and Pattern Recognition, Proceedings of SPIE 2003, 5286:927-930.
29. Majumdar A, Ward R. Multiresolution methods in face recognition. Recent Advances in Face Recognition 2008, Kresimir Delac, Mislav Grgic, Marian Stewart Bartlett, ISBN: 978-953-7619-34-3:InTech, Croatia.
30. Mandal T, Wu QJ, Yuan Y. Curvelet based face recognition via dimension reduction. Signal Processing 2009, 89:2345-2353.
31. Sumana I, Islam M, Zhang D, Lu G. Content based image retrieval using curvelet transform. IEEE 10th Workshop on Multimedia Signal Processing, 11-61, Cairns, Australia 2008,
32. Geback T, Koumoutsakos P. Edge detection in microscopy images using curvelets. BMC Bioinformatics 2009, 10.
33. Haralick R. Statistical and Structural Approaches to Texture. Proceedings of the IEEE 1979, 67:786-804.
34. Chebira A, Barbotin Y, Jackson C, Merryman T, Srinivasa G, Murphy RF, Kovacevic J. A multiresolution approach to automated classification of protein subcellular location images. Bioinformatics 2007, 8:210.
35. Kuncheva LI. Combining Pattern Classifiers: Methods and Algorithms 2004, Wiley-Interscience.
36. Kuncheva LI, Rodriguez JJ, Plumpton CO, Linden DE, Johnston SJ. Random subspace ensembles for fMRI classification. IEEE Trans Med Imaging 2010, 29(2):531-42.
37. Ho TK. The random subspace method for constructing decision forests. IEEE Transactions on PAMI 1998, 20:832-844.
38. Daubechies I. The wavelet transform, time-frequency localisation and signal processing. IEEE Trans Inform Theory 1990, 36:961-1005.
39. Gabor D. Theory of communication. J IEEE 1946, 93:429-457.
40. Manjunath B, Ma W. Texture Features for Browsing and Retrieval of Image Data. IEEE Trans on Pattern Analysis and Machine Intelligence 1996, 18:837-842.
41. Vapnik V. The Nature of Statistical Learning Theory. 1995, Springer-Verlag.
42. Breiman L. Random Forests. Machine Learning 2001, 45:5-32.
43. Gangeh MJ, Kamel MS, Duin PW. Random Subspace Method in Text Categorization. International Conference on Pattern Recognition (ICPR) 2010, 3049-2052.
44. Loo LH, Wu L, Altschuler SJ. Image-based multivariate profiling of drug responses from single cells. Nature Methods 2007, 4(5):445-53.
45. Newberg J, Murphy RF. A framework for the automated analysis of subcellular patterns in human protein atlas images. J Proteome Res 2008, 7(6):2300-8. 10.1021/pr7007626, 18435555.
46. Gehler P, Nowozin S. On Feature Combination for Multiclass Object Classification. IEEE International Conference on Computer Vision (ICCV) 2009,
47. Soh L, Tsatsoulis C. Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-Occurrence Matrices. IEEE Transactions on Geoscience and Remote Sensing 1999, 37:780-795.
48. Clausi D. An analysis of co-occurrence texture statistics as a function of grey level quantization. Can J Remote Sensing 2002, 28:45-62.
49. Breiman L. Bagging predictors. Machine Learning 1996, 24:123-140.
50. Freund Y, Schapire RE. A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting. Journal of Computer and System Sciences 1997, 55:119-139.