MPEG: a video compression standard for multimedia applications
Communications of the ACM - Special issue on digital multimedia systems
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Algorithmic Clustering of Music Based on String Compression
Computer Music Journal
2005 Special Issue: The context-tree kernel for strings
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Clustering Fetal Heart Rate Tracings by Compression
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Information distance from a question to an answer
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IEEE Transactions on Pattern Analysis and Machine Intelligence
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
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Statistical Analysis and Data Mining
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IEEE Transactions on Information Theory
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The ability of stem cells holds great potential for drug discovery and cell replacement therapy. To realize this potential, effective high content screening for drug candidates is required. Analysis of images from high content screening typically requires DNA staining to identify cell nuclei to do cell segmentation before feature extraction and classification. However, DNA staining has negative effects on cell growth, and segmentation algorithms err when compound treatments cause nuclear or cell swelling/shrinkage. In this paper, we introduced a novel Information Distance Classification (IDC) method, requiring no segmentation or feature extraction; hence no DNA staining is needed. In classifying 480 candidate compounds that may be used to stimulate stem cell differentiation, the proposed IDC method was demonstrated to achieve a 3% higher F1 score than conventional analysis. As far as we know, this is the first work to apply information distance in high content screening.