Elizabeth D. Trippe

TL;DR: Several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering are described, which briefly explain text mining in biomedical and health care domains.

Abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

TL;DR: The main approaches to automatic text summarization are described and the effectiveness and shortcomings of the different methods are described.

TL;DR: The Scientific Knowledge Extraction from Data (SKED) architecture is introduced, as a technology-agnostic framework to minimize the overhead of data integration, permit reuse of analytical pipelines, and guarantee reproducible quantitative results.

TL;DR: Recommendations for cyberinfrastructure support teams responsible for systems biology research programs based on lessons learned while establishing and leading a complex, transdisciplinary, host-pathogen malaria systems biology consortium involving many institutions, a variety of disciplines, animal infectious disease models, and clinical studies are described.