Object behavior mining from mitochondiral shape databases for degenerated nerve disease studies

To make a biological information system valuable for the life sciences community, data analytics have to provide explainable results that can demonstrate the linkages between measurable features and biological meaning of interest. Neither traditional classification nor information retrieval methods are sufficient for biomedical applications if such linkages cannot be established. An example area is the understanding of mitochondrial dynamics in Drosophila segmental nerves. Simple shape analysis or image classification of mitochondrial objects is a far cry from in-depth study of the temporal patterns and transitions of mitochondria which are essential organelles of eukaryotic cells where mitochondria undergo frequent shape changes as well as active movement. We present a computational approach to perform mitochondrial shape analysis, pattern mining, and dynamic behavior retrievals. The capability of the computational methods may provide new insights into scientific discoveries for degenerated nerve diseases.