Research Literature Analytical Solutions
Zero in on key facts. Skip the fluff.
The volume of research literature modern practitioners need to monitor to stay on top of recent developments is growing exponentially. Researchers in academia and industry alike need to extract, summarize and organize information of interest from piles of articles, patent documents, and clinical trials.
The most widely used resource in pharmaceutical, biomedical and life sciences research, PubMed, provides over 28 million literature citations. Thousands of new publications are being added to PubMed daily. With such large amounts of data, traditional manual analysis is no longer an option for modern practitioners. They need automated tools for focused, accurate, fast, and high-throughput text analysis.
Solution
Megaputer offers a solution for automated analysis of research documents. It enables researchers to automatically extract and organize for further use key information including biological and medical entities (drugs, illnesses, biomarkers, chemicals, proteins, genes and their expressions, patient demographics, inclusion criteria, observed effect, etc.), as well as any other entities, relationships and facts of interest. The solution can reveal hot research topics and emerging trends. It facilitates high accuracy and speed of the analysis.
How it works
The solution can work directly on PubMed or cancer.gov data. Alternatively, it can process a collection of articles in various formats stored in your repository or perform the web-crawling to fetch the sought data.
The solution extracts and normalizes standard entities, such as proteins, genes, chemicals, drugs, and diseases. It can reveal other entities, relations and facts based on pre-defined or user-configured queries capturing linguistics, semantic, and lexicographical patterns. For example, researchers can learn what genes and biomarkers are related in the considered biological processes, or how efficient are different drugs used for treating the same disease. The solution organizes the extracted knowledge in structured form for further analysis. It can summarize results in interactive graphical reports and generate alerts when events of potential interest occur.
A global pharmaceutical company was evaluating the possibility of stepping into the field of auto-immune diabetes to develop new drugs. It needed to quickly analyze existing literature to collect information on what clinical trials have been conducted on auto-immune diabetes, what proteins and drugs are related to diabetes, and which researchers are most productive and respected in this field.
Benefits
