Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Nantonac collaborative filtering: recommendation based on order responses
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The JigCell Model Builder and Run Manager
Bioinformatics
COPASI---a COmplex PAthway SImulator
Bioinformatics
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Computer
Journal of Artificial Intelligence Research
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Systems biology has made massive strides in recent years, with capabilities to model complex systems including cell division, stress response, energy metabolism, and signaling pathways. Concomitant with their improved modeling capabilities, however, such biochemical network models have also become notoriously complex for humans to comprehend. We propose network comprehension as a key problem for the KDD community, where the goal is to create explainable representations of complex biological networks. We formulate this problem as one of extracting temporal signatures from multi-variate time series data, where the signatures are composed of ordinal comparisons between time series components. We show how such signatures can be inferred by formulating the data mining problem as one of feature selection in rank-order space. We propose five new feature selection strategies for rank-order space and assess their selective superiorities. Experimental results on budding yeast cell cycle models demonstrate compelling results comparable to human interpretations of the cell cycle.