Reshaping Society through Analytics, Collaboration, and Decision Support: Role of Business Intelligence and Social Media (Volume 18 of the series Annals of Information Systems)

Jie Tao (with Sagnika Sen amd Amit V. Deokar) is a contributing author, On the Role of Ontologies in Information Extraction , pp. 115-133. Book description: This volume explores emerging research and pedagogy in analytics, collaboration, and decision support with an emphasis on business intelligence and social media. In general, the chapters help understand where technology involvement in human decisions is headed. Reading the chapters can help understand the opportunities and threats associated with the use of information technology in decision making. Computing and information technologies are reshaping our global society, but they can potentially reshape it in negative as well as positive ways. Analytics, collaboration and computerized decision support are powerful decision aiding and decision making tools that have enormous potential to impact crisis decision making, regulation of financial systems, healthcare decision making and many more important decision domains. Many information technologies can potentially support, assist and even decide for human decision makers. Despite the potential, some researchers think that we know the answers to how these technologies will change society. The Wisdom of Crowds or Big Data become the topic of the day and are soon replaced with new marketing terms. In many ways, mobile technology is just another form factor to adapt decision support capabilities too and experiment with new capabilities. The cloud is a nebulous metaphor that adds to the mystery of information technology. Wireless technology enables the ubiquitous presence of analytics and decision support. With new networking capabilities, collaboration is possible anywhere and everywhere using voice, video and text. Documents can be widely shared and massive numbers of documents can be carried on a small tablet computer. Recent developments in technologies impact the processes organizations use to make decisions. In addition, academics are looking for ways to enhance their pedagogy to train students to be more adept in understanding how emerging technology will be used effectively for decision making in organizations. The chapters are based on papers originally reviewed at the Special Interest Group on Decision Support Systems (SIGDSS) Workshop at the 2013 International Conference on Information Systems (ICIS 2013). Ultimately this volume endeavors to find a balance between systematizing what we know, so we can teach our findings from prior research better, and stimulating excitement to move the field in new directions.https://digitalcommons.fairfield.edu/business-books/1037/thumbnail.jp

Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta

Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. The ability of a molecule to effectively cross the placental barrier and reach the fetus determines the drug’s toxicological effects on the fetus. In this regard, our study aims to predict the permeability of molecules across the placental barrier. Based on publicly available datasets, several machine learning models are comprehensively analysed across different fingerprints and toolkits to find the best suitable models. Several dataset analysis models are utilised to study the data diversity. Further, this study demonstrates the application of neural network-based models to effectively predict the permeability. K-nearest neighbour (KNN), standard vector classifier (SVC) and Multi-layer perceptron (MLP) are found to be the best-performing models with a prediction percentage of 82%, 86.4% and 90.8%, respectively. Different models are compared to predict the chosen set of drugs, drugs like Aliskiren, some insulin secretagogues and glucocorticoids are found to be negative while predicting the permeability