document: Extracting temporal signatures for comprehending systems biology models 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 .
doc_bio_tags: O B I O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O