Abstract. We use invariant theory of finite groups to study shape enumerators of self-dual linear codes in a finite NRT metric space. We provide a new approach that avoids applying Molien’s formula to compute all possible shape enumerators. We also explicitly compute the shape enumerators of some low-dimensional self-dual NRT codes over an arbitrary finite field.

SUMMARY: An alternative splicing isoform switch is where a pair of transcript isoforms reverse their relative expression abundances in response to external or internal stimuli. Although computational methods are available to study differential alternative splicing, few tools for detection of isoform switches exist and these are based on pairwise comparisons. Here, we provide the TSIS R package, which is the first tool for detecting significant transcript isoform switches in time-series data. The main steps of TSIS are to search for the isoform switch points in the time-series, characterize the switches and filter the results with user input parameters. All the functions are integrated into a Shiny App for ease of implementation of the analysis. AVAILABILITY AND IMPLEMENTATION: The TSIS package is available on GitHub: https://github.com/wyguo/TSIS. CONTACT: [email protected].

In this paper, the recently developed Extreme Learning Machine (ELM) is used for direct multicategory classification problems in the cancer diagnosis area. ELM avoids problems like local minima, improper learning rate and overfitting commonly faced by iterative learning methods and completes the training very fast. We have evaluated the multi-category classification performance of ELM on three benchmark microarray datasets for cancer diagnosis, namely, the GCM dataset, the Lung dataset and the Lymphoma dataset. The results indicate that ELM produces comparable or better classification accuracies with reduced training time and implementation complexity compared to artificial neural networks methods like conventional back-propagation ANN, Linder's SANN, and Support Vector Machine methods like SVM-OVO and Ramaswamy's SVM-OVA. ELM also achieves better accuracies for classification of individual categories.

GAP-RBF (growing and pruning RBF) algorithm is a newly developed sequential growing and pruning algorithm for RBF networks for function approximation problems. It has been confirmed to produce excellent performance for problems in function approximation area, but its performance for classification problems has not been evaluated yet. In this paper, the performance of GAP-RBF for bio-medical classification problems is investigated. Its classification performance is compared with the conventional multilayer feed forward network (MFN) and a well-known sequential learning algorithm-minimal resource allocation network (MRAN) based on two benchmark problems from the bio-medical classification area from PROBEN1 database. The results indicate that GAP-RBF/ algorithm can achieve a higher or at least similar classification accuracy with a more compact network structure and faster learning speed. Some limitations of this algorithm for classification problems are also identified.