Powerful deep learning models enable Vyasa to answer questions in natural language, allowing you to uncover insights from complex literature.

Named-entity recognition (NER) pre-built into Layar enables you to identify and categorize terms and phrases from integrated content — proteins, cell lines, diseases and more. These concepts are continuously updated and refined by deep learning models to reflect the domain’s current language and capture novel terms previously not mentioned (e.g., COVID-19).

Deep-learning text analytics also recognizes similar names and descriptions for content such as symptoms and diseases.

When developing a new drug, pharmaceutical firms need to review thousands of PDFs, each hundreds of pages long, from public sources and their own repositories.

Historically, large teams have spent weeks manually processing these documents. Layar analyzes this content with proprietary deep learning models. Researchers can pose natural language questions in Layar’s apps, such as:

• What indications are being studied?
• What is the age range of the population?
• What is the number of patients in the study?
• How was the drug administered?

The platform retrieves the answers and automatically compiles them into easy to review formats including smart spreadsheets. Collectively, the protocol summaries are produced in minutes, instead of days or weeks.

The early stages of target discovery require searching the latest scientific literature which is both time and labor-intensive.

Vyasa brings these insights to a researcher’s fingertips, enabling users to run queries in natural language and explore results through visual, low-code tools. Vyasa Axon collects query results from across scientific literature, public and private databases connected to the Layar data fabric. Results are represented in a dynamic knowledge graph, allowing researchers to uncover novel insights and relationships in their data to identify biological entities and targets. 

Images are rich with insight, but increasingly challenging
to manage, access and analyze via traditional methods. As a result, critical data points can be missed that can influence early detection, diagnosis and research.

Vyasa’s image analytics capabilities address this problem head-on. By accessing Vyasa’s Layar data fabric, users can connect to diverse sets of images regardless of storage location or file type without moving or replicating the content. Layar then applies deep learning to connected image sets creating an intuitive environment for exploring and analyzing images in a single platform.

Users of Layar streamline image processing, classification and model training.

Users leverage our powerful deep learning models to predict compound toxicity and generate new molecules to enhance drug design and trial research.

Vyasa Layar can analyze complex file types including BAM and VCF files to provide a powerful tool to learn more about genomic variants.

Users can explore their genomics data in a variety of Vyasa applications, including Axon knowledge graphs.

Enable comprehensive biomedical literature and clinical trial reviews to identify unmet disease area needs and monitor publishing trends. Identify key authors, organizations and technologies as well as sentiment and publishing focus. Detect relevant insights and emerging trends across all relevant sources. Generate semi-structured data records from vast document repositories in fractions of the time required by manual review and extraction (typically a saving of weeks to months of time per project).