Research, Computation, Analysis
The BRAIN Commons was designed with user-friendly Tools to empower the Brain Health research community. This unique resource allows registered users to combine, interpret, and analyze vast and disparate data types from different sources with sophisticated visualization and analytics tools. It is also designed to be accessible across a spectrum of competencies from the casual user to the advanced data scientist.
The BRAIN Commons utilizes state-of-the art information technology to support petabyte-scale storage and integrated analysis of disparate data types including imaging, genomic, biomic, wearable, sensor, and clinical and preclinical data.
For the programmer and data scientist, this resource also includes a builder’s studio for developers to create new analyses, pipelines and visualization tools.
Featured Tools available in the BRAIN Commons
Cohort Analysis Tool
All data in the Commons can be filtered and saved. The cohort analysis tool enables researchers to perform ad-hoc experimentation on the data using a variety of suggested plots for visualization. Furthermore, the appropriate statistical analyses are performed automatically depending on the type of data selected, so that all researchers, regardless of the level of expertise, can start generating insights from data.
Connecting scientific information across 1000s of research articles is often a manual process focused on a specific domain of interest. The BRAIN Commons Knowledge MapTM is a Biological Expression Language (BEL) disease model that extracts and organizes the existing knowledge space around many factors deemed relevant for brain research, such as: core mechanisms, clinical symptoms, biomarkers, genetic variation, epidemiological studies and others.
This custom-made tool includes intuitive visualizations that allow searching and clustering of entities, e.g. conditions, symptoms and biomarkers based on the shared space they occupy in the published literature. Interactive network and Sankey diagrams reveal the connecting paths and causal relationships between entities of interest, often revealing hidden connections or gaps in the literature, leading to objective formulation of novel hypotheses in minutes rather than in months.
The PubMed Explorer increases the efficacy with which researchers and science writers can understand the landscape of research available to them. With the use of natural language processing, graph processing, and data visualization techniques, users can effectively engage with search results to identify research articles and key opinion leaders within their area of focus.
Find better keywords, decide which articles to read, and manage those articles without having to change your citation manager software. When you search PubMed using PubMed Explorer, you will see an interactive word cloud that lets you understand the context that normally only comes after reading dozens of pages of literature.