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Artificial Intelligence for Investigating Anti-cancer solutions 

We are on a mission to investigate and provide the most advanced Machine Learning and Web Intelligence technologies to find treatments or diagnostic tools to fight diseases like cancers. 


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Quantification and Calibration of Collective Morphological Differences in Cell Images

Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise. We propose a graph-based approach to cell image analysis. We define graph transition energy to quantify morphological differences between image sets. A spectral graph theoretic regularization is applied to transform the feature space based on training examples of extremely different images to calibrate the quantification.

Online Learning: Periodic Step Size Adaptation (PSA) (2007-)

Periodic Step size Adaptation (PSA) is an on-line learning algorithm that can reach near-optimal empirical performance in a single-pass through training data. PSA can be applied to train a wide variety of models, including CRF and SVM. Source codes are released on October 15, 2009. Description of PSA can be found in NIPS*2009 [PDF] and Machine Learning Journal [PDF] .

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