An Information Systems Instructional Model for Supporting the DPMA 1990 Guidelines

The recent DPMA 1990 curriculum guidelines emphasize the integration of theory and practice in information systems. The guidelines are a systematic approach to covering the knowledge clusters and to preparing students to be practitioners who can develop real systems. Traditional I.S. curricula, however, tend to offer theoretical self-contained courses. The information systems Instructional Model proposed here integrates courses and bridges the gap between theory and practice. Our model supports the DPMA 1990 and is based on an underlying methodology called the Cleanroom Systems Development Process (CSDP). This innovative approach to systems development is discussed, and the progress in implementing the model is described

Weighted-persistent-homology-based machine learning for RNA flexibility analysis

With the great significance of biomolecular flexibility in biomolecular dynamics and functional analysis, various experimental and theoretical models are developed. Experimentally, Debye-Waller factor, also known as B-factor, measures atomic mean-square displacement and is usually considered as an important measurement for flexibility. Theoretically, elastic network models, Gaussian network model, flexibility-rigidity model, and other computational models have been proposed for flexibility analysis by shedding light on the biomolecular inner topological structures. Recently, a topology-based machine learning model has been proposed. By using the features from persistent homology, this model achieves a remarkable high Pearson correlation coefficient (PCC) in protein B-factor prediction. Motivated by its success, we propose weighted-persistent-homology (WPH)-based machine learning (WPHML) models for RNA flexibility analysis. Our WPH is a newly-proposed model, which incorporate physical, chemical and biological information into topological measurements using a weight function. In particular, we use local persistent homology (LPH) to focus on the topological information of local regions. Our WPHML model is validated on a well-established RNA dataset, and numerical experiments show that our model can achieve a PCC of up to 0.5822. The comparison with the previous sequence-information-based learning models shows that a consistent improvement in performance by at least 10% is achieved in our current model