Healthcare & Life Science Solutions

Healthcare and life science organizations today are straining to absorb raw data and its disparate formats, let alone effectively manage and analyze it. Vyasa provides a solution.

From genomic sequences and molecular characteristics to electronic medical records and patient journeys, the healthcare and life science fields are awash with potentially valuable raw data.

With Layar, you can analyze the full range of biomedical data types available, including published research, images, drug-like compounds, gene variants, real-world data, clinicians’ notes and electronic medical records. Revolutionize how your organization discovers insights, makes decisions and improves lives.

Deep Learning-Powered
Healthcare & Life Science Analytics

Scientific Literature & Real-World Data

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.

Clinical Trials

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.

Target Discovery

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. 

Biomedical Images

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.

Drug-Like Compounds
Vyasa provides a simple-to-use application interface for exploring, analyzing and de novo generating small compounds based on simple drug inputs.

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

Gene Variants

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.

Medical Writing

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).

Powerful Biomedical
Data Catalog

We’ve created the world’s first catalog of transformer-based deep learning analyzed biomedical data sources. This unique and proprietary resource allows users to unify valuable healthcare and life science content with their internal data.

Layar provides access to tens of millions of deep learning analyzed life science and healthcare records, including:


PubMed Central Open Access 

UK Healthcare Protocols


U.S. Patent Office (USPTO)




National Institute of Health and Care Excellence (NICE)


With access to Vyasa’s Reference Data Fabric, organizations can accelerate their access to industry literature that can fuel clinical trial design and rare disease research, monitor competitor patents, guide paths of care and more.

You can harness the analytic power through Layar’s apps or the Layar API.

Uses in Healthcare

  • Discover and understand emerging diseases
  • Process and store genomic analysis
  • Manage patient cohorts and improve care delivery
  • Analyze health insurance claims
  • Follow patient journeys through social media posts, forum discussions, reported data and wearable monitoring
  • Analyze population data between providers and payers, including the distinct line items involved — location, patient ethnicity and spoken language, clinician notes, etc.
  • Understand patient needs and stay ahead of healthcare trends
  • Easily manage imaging files and streamline image analysis for faster cohort curation and model training

Uses in Life Science

  • Access the latest life science research from PubMed, PubChem, and other Canonical Data Fabric sources
  • Analyze gene mutations and variants
  • Accelerate early-stage research and systematic literature review
  • Discover and analyze small compounds in drug inputs
  • Predict compound toxicity and generate new molecules to enhance drug design and trial research
  • Understand and analyze lines of treatment
  • Identify factors that made past clinical trials viable
  • Monitor real-world data around products and technologies
  • Harmonize genomics data across multiple data sources and tables
  • Review previous projects to influence early-stage R&D

Use Cases



Situation 85% of clinical trials end with less than expected enrollment, while 19% are terminated or fail to meet accrual goals at all. This scenario has significant impact on the viability of trials, leading to insufficient results, loss of time and financial resources and ultimately a reduction in innovation. Problem One of the biggest challenges […]


Situation The NIH’s Childhood Cancer Data Initiative is set to revolutionize the data landscape around pediatric cancers and treatments. While important progress is being made, hospitals and oncology departments still face an uphill battle with the resources they have available to understand patient illnesses and gain the insights they need. Problem The healthcare industry faces […]

Vyasa features a suite of application interfaces
for exploring your data fabric.