Feature space transformation for semi-supervised learning for protein subcellular localization in fluorescence microscopy images

Authors:
Yu-Shi Lin;Yi-Hung Huang;Chung-Chih Lin;Chun-Nan Hsu
Affiliations:
Institute of Information Science, Academia Sinica and Department of Computer Science, National Taiwan University, Taipei, Taiwan;Institute of Information Science, Academia Sinica, Taipei, Taiwan;Faculty of Life Sci. & Inst. of Genomes, Nat'l Yang-Ming Univ., Taipei, Taiwan;Institute of Information Science, Academia Sinica, Taipei, Taiwan
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

Citing 2
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Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization

Bioinformatics
Interpretation of protein subcellular location patterns in 3D images across cell types and resolutions

BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development

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Abstract

As rapid acquisition of large collections of fluorescence microscopy cell images can be automated, large-scale subcellular localizations of GFP-tagged fusion proteins can be practically accomplished. Semi-supervised learning has the potential of using a large set of unlabeled images for the recognition of subcellular organelle patterns, but the performance still has room for improvement. This paper presents a feature space transformation method based on the spectral graph theory to improve semi-supervised learning. Experimental result shows that our feature space transformation method can improve the classification accuracy substantially.