Research
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Towards Improving Interface Modularity in Legacy Java Software Through Automated Refactoring
Towards Improving Interface Modularity in Legacy Java Software Through Automated Refactoring
The skeletal implementation pattern is a software design pattern consisting of defining an abstract class that provides a partial interface implementation. However, since Java allows only single class inheritance, if implementers decide to extend a skeletal implementation, they will not be allowed to extend any … Continue reading
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Bio-inspired Computation Approach for Tumor Growth with Spatial Randomness Analysis of Kidney Cancer Xenograft Pathology Slides
Bio-inspired Computation Approach for Tumor Growth with Spatial Randomness Analysis of Kidney Cancer Xenograft Pathology Slides
In our research, we analyze digitized images of Hematoxylin-Eosin (H&E) slides equipped with tumorous tissues from patient derived xenograft models to build our bio-inspired computation method, namely Personalized Relevance Parameterization of Spatial Randomness (PReP-SR). Applying spatial pattern analysis techniques of quadrat counts, kernel estimation and nearest neighbor functions to the images of the H&E samples, slide-specific features are extracted to examine the hypothesis that existence of dependency of nuclei positions possesses information of individual tumor characteristics. These features are then used as inputs to PReP-SR to compute tumor growth parameters for exponential-linear model. Differential evolution algorithms are developed for tumor growth parameter computations, where a candidate vector in a population consists of size selection indices for spatial evaluation and weight coefficients for spatial features and their correlations. Using leave-one-out-cross-validation method, we showed that, for a set of H&E slides from kidney cancer patient derived xenograft models, PReP-SR generates personalized model parameters with an average error rate of 13:58%. The promising results indicate that bio-inspired computation techniques may be useful to construct mathematical models with patient specific growth parameters in clinical systems.