Objectives: Traditionally, summarization of research themes and trends within a given discipline was accomplished by manual review of scientific works in the field. However, with the ushering in of the age of “big data”, new methods for discovery of such information bec...

Summarization is often a time-consuming task for humans. Automated methods can summarize a larger volume of source material in a shorter amount of time, but creating a good summary with these methods remains challenging. This submission contains all work related to a se...

This is the Neural Network Document Summarization project for the Multimedia, Hypertext, and Information Access (CS 4624) course at Virginia Tech in the 2018 Spring semester. The purpose of this project is to generate a summary from a long document through deep learning...

A complete over-representation analysis for RCV gene sets against GTEx tissue type transcriptomes. The analysis is displayed as a heat map with RCV on the Y-axis, GTEx on the X-axis, over-respresentation in blue and under-respresentation in red. Both axes are clustered ...

Abstract Background The Gene Ontology (GO) consists of over 40,000 terms for biological processes, cell components and gene product activities linked into a graph structure by over 90,000 relationships. It has been used to annotate the functions and cellular locations o...

Abstract Background Traditionally text mention normalization corpora have normalized concepts to single ontology identifiers (“pre-coordinated concepts”). Less frequently, normalization corpora have used concepts with multiple identifiers (“post-coordinated concepts”) b...

Most of our knowledge of wild chimpanzee behaviour stems from fewer than 10 long-term field sites. This bias limits studies to a potentially unrepresentative set of communities known to show great behavioural diversity on small geographic scales. Here, we introduce a ne...

Abstract Background The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machi...

1,000 name-strings randomly selected from GNI and used to determine Accuracy, Precision and Recall data (Table 1). (TXT 32 kb)

Includes a full and formal explanation of all parser fields as a JSON schema. (JSON 9 kb)