Normalized Gaussian Networks with Mixed Feature Data

With mixed feature data, problems are induced in modeling the gating network of normalized Gaussian (NG) networks as the assumption of multivariate Gaussian becomes invalid. In this paper, we propose an independence model to handle mixed feature data within the framework of NG networks. The method is illustrated using a real example of breast cancer data.

Zebrafish as a model for caveolin-associated muscle disease; caveolin-3 is required for myofibril organization and muscle cell patterning

Caveolae are an abundant feature of many animal cells. However, the exact function of caveolae remains unclear. We have used the zebrafish, Danio rerio, as a system to understand caveolae function focusing on the muscle-specific caveolar protein, caveolin-3 (Cav3). We have identified caveolin-1 (alpha and beta), caveolin-2 and Cav3 in the zebrafish. Zebrafish Cav3 has 72% identity to human CAV3, and the amino acids altered in human muscle diseases are conserved in the zebrafish protein. During embryonic development, cav3 expression is apparent by early segmentation stages in the first differentiating muscle precursors, the adaxial cells and slightly later in the notochord. cav3 expression appears in the somites during mid-segmentation stages and then later in the pectoral fins and facial muscles. Cav3 and caveolae are located along the entire sarcolemma of late stage embryonic muscle fibers, whereas beta-dystroglycan is restricted to the muscle fiber ends. Down-regulation of Cav3 expression causes gross muscle abnormalities and uncoordinated movement. Ultrastructural analysis of isolated muscle fibers reveals defects in myoblast fusion and disorganized myofibril and membrane systems. Expression of the zebrafish equivalent to a human muscular dystrophy mutant, CAV3P104L, causes severe disruption of muscle differentiation. In addition, knockdown of Cav3 resulted in a dramatic up-regulation of eng1a expression resulting in an increase in the number of muscle pioneer-like cells adjacent to the notochord. These studies provide new insights into the role of Cav3 in muscle development and demonstrate its requirement for correct intracellular organization and myoblast fusion.

Fitness assessment of document model

Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, Resource selection for domain-specific cross-lingual IR, in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154-161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification.

Mutation analysis to verify feature matrices for isolating errors in simulation models

Software simulation models are computer programs that need to be verified and debugged like any other software. In previous work, a method for error isolation in simulation models has been proposed. The method relies on a set of feature matrices that can be used to determine which part of the model implementation is responsible for deviations in the output of the model. Currrently these feature matrices have to be generated by hand from the model implementation, which is a tedious and error-prone task. In this paper, a method based on mutation analysis, as well as prototype tool support for the verification of the manually generated feature matrices is presented. The application of the method and tool to a model for wastewater treatment shows that the feature matrices can be verified effectively using a minimal number of mutants.