Life Sciences

Improve patient outcomes and accelerate discovery

Get AI-assisted visualizations and predictions that point at the opportunity to act, whether you’re untangling a medical mystery, putting new treatment into action, or working to improve patient care.

Use Cases

Pharma and bio research

  • Drug discovery and drug repurposing
  • Outcome trend modeling
  • Predictive maintenance

Hospital systems & providers

  • Identify and align treatment plans
  • Optimize revenue cycle management
  • Deliver targeted health interventions

Healthcare insurers

  • Predict high-risk claims
  • Optimize revenue cycle management
  • Prevent fraud

What clients are saying

Accelerating time to insight

“Virtualitics can help domain experts easily perform and share the results of sophisticated data analysis at a level usually reserved for data scientists.”

Professor Jim Heath
President of the Institute for Systems Biology (ISB)

Case Studies

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Extracting and analyzing complex relationships in omics data

Researchers at the Institute for Systems Biology make breakthroughs in identifying long Covid vulnerability using Virtualitics AI Platform

Challenge:

James R. Heath’s lab at the Institute for Systems Biology needed to identify critical trends in their omics data related to long COVID-19 at the single cell level but faced the following challenges making it extremely difficult to conduct single-cell analysis without any big data skills:

  • ISB collects significant amounts of omics data, but given its unstructured and disparate nature, the ability to draw rich insights from this data is limited. The data being collected by biologists wasn’t in a form that the data scientists could work with.
  • Current data analysis and visualization tools limit the ability to leverage relational data like a patient’s biomedical background including genomic and clinical data.
  • Analyzing such large amounts of data from different sources takes a lot of time making it impossible to make decisions in real-time.

Approach:

With Virtualitics AI Platform, the researchers at the Institute for Systems Biology were able to process and validate information 100X faster than with traditional workflows, reducing time to insight and action. 

  • Virtualitics AI Platform provided data scientists and biologists with a unified view of the data that both groups could leverage and apply their expertise to
  • Apply the full power of the immersive visualization capabilities of Virtualitics to spot anomalies or outliers in the single-cell omics data that would otherwise be hard to spot. 
  • Identified and described the communities of polyfunctional cells using Virtualitics patented network extractor.
  • Used Virtualitics no-code AI to quickly analyze and communities of polyfunctional cells.

Results:

As a result of the research enabled by Virtualitics AI Platform, the lab was able to:

  • Quickly identify trends in their omics data and determine contributing factors to long COVID-19.
  • Leverage a patient’s biomedical data—including genomic data, metabolomic data and clinical data—to predict whether a person will be susceptible to long COVID
  • Accurately diagnose long COVID if the patient is suffering from indicative symptoms
  • Ultimately treat the cause of long COVID, moving us closer to the day when everyone can make a full recovery from COVID-19.

Extracting and analyzing complex relationships in omics data

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Making the link between lung cancer and influenza

Colombia Medical School partnered with Virtualitics to explore the link between lung cancer mortality and flu epidemics leveraging state-of-the-art data analytics and multidimensional visualization capabilities.

The study was conducted using 195,038 patients with non-small cell lung cancer (NSCLC) located across 13 states using data obtained from the Surveillance, Epidemiology, and End Results (SEER) Program and the Centers for Disease Control and Prevention (CDC).

The research team compared monthly mortality rates during the high and low flu months between 2009 and 2015 for all at-risk patients in the study, as well as newly diagnosed patients. Influenza severity level was defined based on the percentage of outpatient visits to healthcare providers for influenza-like illness and matching CDC flu activity levels with SEER data by state and month.

The data was then analyzed using high-dimensional visualization coupled with AI routines so researchers could fully understand and explore the complex relationships within the data.

 

 

Making the link between lung cancer and influenza

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Isoplexis leverages Virtualitics AI Platform

IsoPlexis, a life science technology company developing a single-cell detection system partnered with Virtualitics to generate more robust insights around proteomic connections.

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Our customers are leveraging AI to achieve groundbreaking results and save lives.

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