Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
A distributed database for bio-molecular images
ACM SIGMOD Record
ViVo: Visual Vocabulary Construction for Mining Biomedical Images
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Automated sub-cellular phenotype classification: an introduction and recent results
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Structured correspondence topic models for mining captioned figures in biological literature
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring the efficacy of caption search for bioscience journal search interfaces
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Automated analysis of human protein atlas immunofluorescence images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Principles of bioimage informatics: focus on machine learning of cell patterns
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since sub-cellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location.