Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

BMC Bioinformatics

Volumn 5, Issue , 2004, Pages

  Huang, Kai ^a   Murphy, Robert F ^a,b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED CLASSIFICATION SYSTEMS; CLASSIFICATION ERROR RATE; ENDOMEMBRANE COMPARTMENTS; FLUORESCENCE MICROSCOPE IMAGES; FLUORESCENCE MICROSCOPES; NEURAL NETWORK CLASSIFIER; PROTEIN SUBCELLULAR LOCATION; SUBCELLULAR LOCATION FEATURES;

ADAPTIVE BOOSTING; CELL DEATH; CELL MEMBRANES; DISCRETE WAVELET TRANSFORMS; FLUORESCENCE; FLUORESCENCE MICROSCOPY; IMAGING TECHNIQUES; MICROSCOPES; MOLECULAR BIOLOGY; NEURAL NETWORKS; OPTIMIZATION; PROTEINS;

THREE DIMENSIONAL;

PROTEOME;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; CELL DEATH; CELL FRACTIONATION; ENDOPLASMIC RETICULUM; ENDOSOME; FLUORESCENCE MICROSCOPY; GOLGI COMPLEX; IMAGE ANALYSIS; LYSOSOME; MITOSIS; PROTEIN EXPRESSION; PROTEIN FUNCTION; PROTEIN LOCALIZATION; PROTEOMICS; THREE DIMENSIONAL IMAGING; VALIDATION PROCESS; BIOLOGY; CHEMISTRY; CLASSIFICATION; COMPARATIVE STUDY; ECONOMICS; EVALUATION STUDY; HELA CELL LINE; HUMAN; INTRACELLULAR SPACE; SENSITIVITY AND SPECIFICITY; TRENDS; TUMOR CELL LINE;

CELL LINE, TUMOR; COMPUTATIONAL BIOLOGY; HELA CELLS; HUMANS; IMAGING, THREE-DIMENSIONAL; INTRACELLULAR SPACE; MICROSCOPY, FLUORESCENCE; PROTEOMICS; SENSITIVITY AND SPECIFICITY;

EID: 13344280993     PISSN: 14712105     EISSN: None     Source Type: Journal    
DOI: 10.1186/1471-2105-5-78     Document Type: Article

References (39)

1. Norin M, Sundstrom M: Structural proteomics: developments in structure-to-function predictions. Trends Biotech 2002, 20:79-84.
2. Macbeath G: Protein microarrays and proteomics. Nature Genetics 2002, 32:526-532.
3. Nakai K: Protein sorting signals and prediction of subcellular localization. Adv Protein Chem 2000, 54: 77-344.
4. Hua S, Sun Z: Support vector machine approach for protein subcellular localization prediction. Bioinformatics 2001, 17: 21-728.
5. von Heijne G, Nielsen H, Engelbrecht J, Brunak S: Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng 1997, 10: -6.
6. Chou K, Cai Y: Using function domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem 2002, 277:45765-45769.
7. Mott R, Schultz J, Bork P, Ponting CP: Predicting protein cellular localization using a domain projection method. Genome Res 2002, 12:1168-1174.
8. Drawid A, Gerstein M: A Bayesian system integrating expression data with sequence patterns for localizing proteins: comprehensive application to the yeast genome. J Mol Biol 2000, 301:1059-1075.
9. 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. Danckaert A, Gonzalez-Couto E, Bollondi L, Thompson N, Hayes B: Automated Recognition of Intracellular Organelles in Confocal Microscope Images. Traffic 2002, 3:66-73.
11. Velliste M, Murphy RF: Automated Determination of Protein Subcellular Locations from 3D Fluorescence Microscope Images. 2002 IEEE International Symposium on Biomedical Imaging (ISBI-2002) Bethesda, MD, USA; 2002:867-870.
12. Price JH, Goodacre A, Hahn K, Hodgson L, Hunter EA, Krajewski S, Murphy RF, Rabinovich A, Reed JC, Heynen S: Advances in Molecular Labeling, High Throughput Imaging and Machine Intelligence Portend Powerful Functional Cellular Biochemistry Tools. J Cell Biochem Supp 2003:194-210.
13. Murphy RF, Boland MV, Velliste M: Towards a Systematics for Protein Subcellular Location: Quantitative Description of Protein Localization Patterns and Automated Analysis of Fluorescence Microscope Images. Eighth International Conference on Intelligent Systems for Molecular Biology Volume 8. San Diego; 2000:251-259.
14. Murphy RF, Velliste M, Porreca G: Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images. J VLSI Sig Proc 2003, 35:311-321.
15. Huang K, Velliste M, Murphy RF: Feature Reduction for Improved Recognition of Subcellular Location Patterns in Fluorescence Microscope Images. Proc SPIE 2003, 4962:307-318.
16. Cortes C, Vapnik V: Support vector networks. Machine Learning 1995, 20:1-25.
17. Vapnik V: Statistical Learning Theory. New York City, Wiley; 1998.
18. Kressel U: Pairwise Classification and Support Vector Machines. Advances in Kernel Methods - Support Vector Learning Volume 15 Edited by: Scholkopf B, Burges C and Smola A J. Cambridge, Massachusetts, MIT Press; 1999:255-268.
19. Platt J, Cristianini N, Shawe-Taylor J: Large Margin DAGs for Multiclass Classification. Adv Neural Inform Proc Systems 2000, 12:547-553.
20. Dietterich TG: Ensemble methods in machine learning. Lecture Notes in Computer Science Volume 1857. Springer-Verlag; 2000:1-15.
21. Hansen L, Salamon P: Neural network ensembles. IEEE Trans Pattern Analysis and Machine Intell 1990, 12:993-1001.
22. Freund Y, Shapire RE: Experiments with a new boosting algorithm. 13th International Conference on Machine Learning Morgan Kauffman; 1996:148-156.
23. Bauer E, Kohavi R: An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning 1999, 36:525-536.
24. Kuncheva LI, Whitaker CJ, Shipp CA, Duin RPW: Is independence good for combining classifiers? 15th International Conference on Pattern Recognition Barcelona, Spain; 2000:168-171.
25. Valentini G, Masulli F: Ensembles of learning machines. Neural Nets WIRN Vietri-02 Edited by: Marinaro M and Tagliaferri R. Heidelberg, Germany., Springer-Verlag; 2002:3-19.
26. Schapire RE: The Boosting Approach to Machine Learning: An Overview. MSRI workshop on Nonlinear estimation and Classification 2002.
27. Waterhouse SR: Classification and Regression using Mixtures of Experts. Department of Engineering, Jesus College Cambridge, U.K., University of Cambridge; 1997.
28. Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE: Adaptive mixtures of local experts. Neural Computation 1991, 3: 9-87.
29. Jordan MI, Jacobs RA: Hierarchical mixtures of experts and the EM algorithm. Neural Computation 1994, 6:181-214.
30. Kittler J, Messer K: Fusion of multiple experts in multimodal biometric personal identity verification systems. 2002 IEEE International Workshop on Neural Networks for Signal Processing (NNSP 12) 2002:3-12.
31. Markey MK, Boland MV, Murphy RF: Towards objective selection of representative microscope images. Biophys J 1999, 76:2230-2237.
32. Mitchell TM: Machine Learning. WCB/McGraw-Hill; 1997.
33. Daugman JD: Complete discrete 2-d Gabor transforms by neural networks for image analysis and compression. IEEE Trans Acoustics Speech Sig Proc 1988, 36:1169-1179.
34. Daubechies I: Orthonormal Bases of Compactly Supported Wavelets. Comm Pure Appl Math 1988, 41:909-996.
35. Ma WY, Manjunath BS: A comparison of wavelet transform features for texture image annotation. IEEE International Conference on Image Processing Volume 2. Washington D.C, U.S.A.; 1995:2256-2259.
36. Manjunath BS, Ma WY: Texture features for browsing and retrieval of image data. IEEE Trans Pattern Analysis and Machine Intelligence 1996, 8:837-842.
37. Chen X, Velliste M, Weinstein S, Jarvik JW, Murphy RF: Location proteomics - Building subcellular location trees from high resolution 3D fluorescence microscope images of randomly-tagged proteins. Proc SPIE 2003, 4962:298-306.
38. Collobert R, Bengio S, Mariéthoz J: Torch: a modular machine learning software library. IDIAP; 2002.
39. Jennrich RI: Stepwise discriminant analysis. Statistical Methods for Digital Computers Volume 3. Edited by: Enslein K, Ralston A and Wilf HS. New York, John Wiley & Sons; 1977:77-95.