Obtaining relevant information about gene interactions is critical for understanding disease processes and treatment. With the rise in text mining approaches, the volume of such biomedical data is rapidly increasing, thereby creating a new problem for the users of this data: information overload. A tool for efficient querying and visualization of biomedical data that helps researchers understand the underlying biological mechanisms for diseases and drug responses, and ultimately helps patients, is sorely needed. To this end we have developed GeneDive, a web-based information retrieval, filtering, and visualization tool for large volumes of gene interaction data. GeneDive offers various features and modalities that guide the user through the search process to efficiently reach the information of their interest. GeneDive currently processes over three million gene-gene interactions with response times within a few seconds. For over half of the curated gene sets sourced from four prominent databases, more than 80% of the gene set members are recovered by GeneDive. In the near future, GeneDive will seamlessly accommodate other interaction types, such as gene-drug and gene-disease interactions, thus enabling full exploration of topics such as precision medicine. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net.

No abstract

No abstract

The purpose of this study was to evaluate an academic support program that aims to improve persistence and graduation for lower-division students who are low income, first generation, and/or underrepresented. Students were organized in 10 academies that serve as a “school within a school” and have three main elements: a pathway of two linked general education courses that students follow, cohort-style, over four semesters; wraparound student services integrated into the classroom; and a 45-hour faculty development process. Program participants (<em>n </em>= 2,281) were compared to a matched comparison group (<em>n </em>= 2,276). Multimodal logistic regression analyses showed that program participants statistically significantly outperformed the comparison group on every outcome measure: GPA, completion of developmental coursework, persistence toward graduation, and graduation rates.