Rezarta Islamaj Dogan

TL;DR: The results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.

TL;DR: This article introduces the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM, a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data.

TL;DR: This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries, which provided insight into PubMed users’ needs and their behavior.

TL;DR: Interactive feature browsing and visualization tool that allows the user to make splice-site predictions for submitted sequences and browse the rich catalog of features that underlies these predictions, and which has been found capable of providing high classification accuracy on human splice sites.

TL;DR: The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging, and text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results.