Author: Nick Brown

How to Edit Ontologies

Getting Started

Step 1. Click the “Ontologies” subsection under your “Data Catalog” menu, located on the left-hand side of your Layar home page.

Step 2. Click the ontology name to see the Ontology View for that ontology, with the terms, synonyms, and related metadata.

Step 3. Search andclick on a term to see its references, synonyms, and other metadata. Here I have searched for “Antineoplastic chemotherapy regimen“.

Now you are ready to modify this ontology term.

Add a Parent Node

  1. On the “+” Icon, to the right of the Ontology Search Bar.
  2. Complete the information on the modal pop up for the new parent node, including the term, synonyms, and any database that references this entity with its own unique ID.
  3. Click “Save
  4. You will now see the parent node within the ontology, with an Ontology Card with the information you provided in step 2.

Add a Child Node

  1. On the Ontology Card, click the icon with three vertical dots and select “Add Child Node“.2. Complete the information on the modal pop up for the child node, including the term, synonyms, and any database that references this entity with its own unique ID. 3. Click “Save” 4. You will now see the child node underneath the parent term, with an Ontology Card with the information you provided in step 2. Edit an Ontology TermTo Edit, click the icon with three vertical dots on the Ontology Card and select “Edit“.For any of the properties of that term, you can add additional values (one per line). For example, I added in three new references for my “Xrefs” property below. Click Save. The new values will be populated in the property they were added to. Delete an Ontology TermTo Delete, click the icon with three vertical dots on the Ontology Card and select “Delete”. Confirm you want to permanently remove this ontology term. Any child nodes underneath that ontology term will also be permanently deleted.

AI Software Provides Intelligent Data Fabric for Life Sciences

Vyasa software provides a novel AI-powered platform for organizations to integrate and analyze content across their entire enterprise data landscape. Recently acquired by Certara, Vyasa deep learning software enables healthcare and life science professionals to gain new value from their data. Its highly-scalable data fabric allows users to connect disparate data sources regardless of file type or storage location. Low-code applications built on top of the data fabric allow healthcare and life science teams to leverage deep learning to accelerate tasks such as early-stage research, clinical trial design, small-compound analytics, patient cohort curation, and more.

Address dark data with Vyasa, NVIDIA and World Wide Technology

Industry analysts report that 97% of enterprises use at least one type of cloud deployment, with the majority deploying hybrid or multi-cloud environments. 

While these environments deliver new flexibility to organizations, they can also deepen dark data issues which currently impact as much as 80% of organizational data. Dark data refers to data and workflows stored across multiple silos, but also the variety of formats the data is stored in such as spreadsheets, written documents, reports, PDFs, images, and more. 

As today’s data landscapes become increasingly diverse and distributed, how can organizations future-proof their environments to maintain productivity and collaboration? 

Intelligent data fabrics for deep learning

When deploying new AI solutions, organizations have traditionally developed custom environments requiring massive amounts of data for their workflows to operate. As a result, teams are burdened with managing multiple silos that fail to give organizations the agility and transparency they’re seeking in their modernization efforts. 

Vyasa has developed a novel approach to address this challenge. Known as an intelligent data fabric, Vyasa Layar enables organizations to apply deep learning across large sets of data regardless of storage location or file structure. As a result, customers can integrate and run analytics across their entire data landscape within a single platform. 

Vyasa Layar is delivered with pre-built deep learning models, and customers have the flexibility to integrate their own models into Layar to run custom workflows against their data fabric, leveraging a suite of low code application interfaces.

Certara’s Vyasa AI-powered platform accelerates life sciences research

Life sciences, pharmaceutical and other healthcare research firms can use the Vyasa AI software platform to accelerate research and increase speed-to-market. Specific benefits of Vyasa data fabric and deep learning capabilities allow organizations to: 

  • Connect disparate cloud and on-premise data sources into a single platform without moving or replicating data.
  • Identify relationships within data via dynamic knowledge graphs.
  • Turn unstructured content into structured insights through smart spreadsheets.
  • Track events and anomalies occurring within your data fabric with real-time dashboards.

Organizations leverage Vyasa to accelerate early-stage life science research, enhance clinical trial design & enrollment, conduct competitor intelligence, improve analyses around customer touchpoints, and gain a better understanding of patient and customer populations.

AI platform deployment considerations

Deploy where you want it

As a fully containerized solution, Vyasa Layar can be deployed on premises, as well as in public or private cloud environments. This provides a dynamic solution for organizations operating across multiple environments. 

Initial installation requirements include:

  • RAM: 256GB
  • CPU: 16+ cores
  • GPU: 2x NVIDIA V100 32GB generation or later
  • Disk: 1TB SSD

Want the advantages of the cloud while maintaining the data control and security of an on-premise environment? Vyasa’s architecture allows you to deploy an intelligent data fabric across cloud adjacent infrastructure.

Enterprise support and validated performance with NVIDIA VMI

NVIDIA Virtual Machine Image, or VMI, a part of NVIDIA AI Enterprise, dramatically cuts down deployment time by automatically installing Kubernetes, the latest GPU drivers, and other required software. It’s designed for hybrid and multi-cloud environments and enables customers to use the latest NVIDIA software and application frameworks for the development and deployment of AI, all of which is fully supported by NVIDIA through the NVIDIA AI Enterprise offering.

Vyasa Layar is fully validated for deployment via NVIDIA VMI, providing a seamless and accelerated experience for deployment in a self-managed Kubernetes cluster in your preferred cloud. 

Flexible AI for healthcare & life science analytics 

Vyasa, NVIDIA and World Wide Technology provide a flexible solution for healthcare and life science organizations to apply advanced AI analytics across their data. With an intelligent data fabric, organizations can enhance and accelerate

  • Early-stage research
  • Clinical trial analysis & recruitment
  • Drug target discovery
  • De novo compound generation
  • Image analysis
  • Genomic analysis
  • Competitor & market intelligence

Evaluate and test data fabrics

Interested in testing Vyasa? Access the Vyasa data fabric lab within the Advanced Technology Center (ATC).

WWT, NVIDIA and Vyasa encourage anyone looking to evaluate this solution to leverage the environments the team has built and tested within AWS and within the Advanced Technology Center. Create an account on to access both the ATC physical environment and the AWS instance via a web browser of their choice from anywhere in the world. 

Your organization can also work with the WWT, NVIDIA and Vyasa teams to customize the solution before validating a custom configuration that is right for them or they can simply be granted access to a solution ready to be validated.

This article was originally published on the World Wide Technology blog.

Applying Structure to Your Unstructured Data

Today’s data landscapes are more dispersed and diverse than ever before. For example, 97% of enterprises use at least one type of cloud deployment, with the majority deploying hybrid or multi-cloud environments. Further, industry analysts estimate that 80-90% of the new data being stored in these environments is unstructured. 

This scenario creates a number of complexities for organizations looking to harness this valuable data. First, this data needs to be identified and collected across multiple storage locations and once this process is complete the work has just begun. Then teams have to sift through and prepare the data – a process that can be a significant burden when considering the complexities of these data types such as:

  • Published research
  • Market analyses
  • Quarterly reports
  • Lab notes
  • Emails 
  • Images
  • Presentations
  • PDFs

Traditionally, analyzing this data requires manually searching through and extracting relevant data points – a task that can take days, weeks or months to complete depending on the size of the desired data set. For example, life science researchers undergoing systematic literature reviews anticipate each review to take months to complete, with each project costing roughly $140,000

A New Way Forward with Deep Learning 

Recent advancements in deep learning, known as large language models (LLMs), have presented a paradigm shift in how organizations can access, analyze and harness new value from this data. 

Unlike a traditional model that processes each term separately and outside the context of the sequence, LLMs use self-attention to build rich representations of each constituent in the data span, allowing these models to understand the relevance of the location of a term, the relation of one term to the next (even if far away from each other) and more. When trained on larger datasets, these models reach remarkable accuracy and recall for understanding unstructured data like large documents of natural language text.

LLMs are being applied to a variety of use cases to help address unstructured data challenges. For example, Vyasa’s Synapse smart spreadsheet technology applies LLMs to sets of unstructured documents and allows organizations to autonomously extract key data points into structured spreadsheets.

At Vyasa, we’ve helped life science consultancies leverage these models to improve productivity for systematic literature reviews, pharmaceutical companies improve early research, medical research centers enhance clinical trial enrollment, healthcare providers reduce time spent reporting and organizations improve call center transcript analysis. 

Watch our video below with SAP to learn about how we’re improving unstructured text analysis for a joint customer.

Healthcare Fireside Chats Episode 3 – Deep Learning Hits its Stride in Healthcare

The healthcare industry’s expectations around deep learning and A.I. have become a reality as organizations undergo digital transformation and regain control of their data.

According to Gartner, healthcare technology leaders are prioritizing data integration, NLP and data annotation in their AI investments.

However, industry analysts also note that managing & preparing data remains the top time investment for deploying AI.

In this installment of our healthcare fireside chats, Steve Lazer, Global Healthcare & Life Sciences CTO at Dell Technologies joins us to discuss the evolution of AI in healthcare and where we’re seeing implementation in the market today.

Watch more below:

Webinar: Accelerating Healthcare Analytics with Intelligent Data Fabric

Organizations only access 20% of the data they have available to them. The other 80% is difficult to access due to silos, unstructured formats and redundant or obsolete content.

This scenario creates immense challenges for organizations looking to deploy A.I. In fact, IDC’s AI InfrastructureView Survey reports that managing & preparing data remains the top time investment for deploying A.I.

Experts from Vyasa, Dell Technologies & NVIDIA discussed how Vyasa’s Layar intelligent data fabric helps solve this challenge during a recent webinar.

Watch below to learn more about how data fabrics are driving healthcare analytics.

Identifying Document ID & Data Source in Vyasa Layar

Identifying a document ID or data source is useful for both search and API calls. Below is a step-by-step guide for identifying both via the Layar interface.

Identifying a Document ID

1. Select a document from the Layar data catalog.

2. Click the three-dot menu on the right-hand side of the Layar document view.

3. Select “Access with Layar ID” from the drop-down menu.

4. Copy/paste Layar ID from pop-up window.

Identifying a Document Data Source

1. View document URL in the web browser.

2. Identify document data source directly after Document ID in URL.

For more information, please visit the Vyasa Developer Hub.

Table Extraction Tutorial

How to use Vyasa Layar’s automated table extraction feature.

Step 1: Click the “View Original” menu item from the Three Dot menu in the upper left hand corner of the Document view.

Step 2: Navigate to the page with the table you are interested in extracting. Click and Drag a box to fit around the table.

Step 3: Click “Extract Table”

Step 4: Click out of the PDF Preview and refresh your webpage.

Step 5: Click the “Related Documents” link beneath the title of the document.

Step 6: Use the filters on the right-hand side and select “Tables”. If you’ve extracted multiple tables, you’ll need to search through the thumbnails to identify which one you are interested in. Click the thumbnail of the table you are interested in.

Step 7: Click “View Detail” to view the extracted table in a Document View in Layar.

Step 8: Compare to the original PDF table by clicking “Compare to Original Image” on the right-hand side of the table.

Step 9: Make any touch-ups or edits necessary for the table. All edits are saved automatically.

Scaling AI in Healthcare & Life Science with Intelligent Data Fabrics – Vyasa at SC22 with Oracle, NVIDIA

According to IDC’s AI InfrastructureView Survey, only one-third of companies surveyed claim to have reached mature AI adoption. The survey notes that infrastructure remains the most consequential, but least mature decision when deploying AI. 

In most cases, companies build custom environments for deploying AI, all of which require massive amounts of data for their workflows to operate. Unfortunately, this scenario poses challenges by deepening data silos and creating redundant or obsolete data as AI deployments become mature. Over time, this negatively impacts the quality of data teams have access to as well as the productivity and enhanced intelligence companies are looking to achieve through these projects.

A Paradigm Shift in Data Analytics

Innovations in data management and deep learning are addressing this issue and presenting a paradigm shift in how companies access and apply analytics to their data. Known as a data fabric, companies can now overcome silos by making data accessible and searchable in a single platform. 

At Vyasa, we’ve combined this approach with advanced deep learning to create our Layar intelligent data fabric. With Layar, companies can connect to disparate data sources regardless of storage location. Layar then applies transformer-based deep learning to the connected data sources, making the content easily searchable and accessible all in natural language. This enables Layar to expand its understanding of both structured and unstructured data, allowing companies to gain new insight and value across their data landscape. 

As a result, companies can scale AI deployments by applying deep learning models across silos via the Layar data fabric.

Accelerated Analytics with Vyasa, Oracle and NVIDIA

Innovative cloud infrastructure from Oracle leveraging NVIDIA accelerated computing provides a high-performance environment for deploying a Layar data fabric.

As an Independent Software Vendor partner in the NVIDIA Partner Network (NPN)  ISV Partner, Vyasa accelerates these capabilities with the NVIDIA AI Enterprise software suite, which includes essential AI workflow processing engines from NVIDIA, including transformer-based deep learning models like NeMo Megatron, NVIDIA Triton Inference Server, NVIDIA RAPIDS data science libraries and more.

Deep Learning Hits its Stride in Healthcare 

Healthcare technology leaders are prioritizing data integration, NLP and data annotation in their AI investments, according to recent reports from Gartner. The Layar data fabric provides a suite of low-code application interfaces that meets many of these priorities, enabling healthcare and life science teams to:

  • Accelerate early research and systematic literature review
  • Enhance clinical trial assessment and enrollment
  • Complement small compound analysis 
  • Improve target discovery
  • Better analyze genomics content
  • Streamline analyses of biomedical images

With Layar, users have experienced a 97% improvement in query accuracy for clinical trial protocol assessment and a 25% increase in productivity when compared to manual literature review.

Vyasa, Oracle and NVIDIA at SC22 

Interested in learning more about Vyasa’s deep learning analytics for healthcare & life science? Visit our demo in the Oracle & NVIDIA booth #2406 at SC22 from November 14-17. 

Access a free trial of Vyasa here



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.


One of the biggest challenges associated with clinical trial enrollment is identifying candidates due to the vast amounts of siloed and unstructured data needed. In fact, over half of eligibility criteria is unstructured, making it incredibly complex to search and access. Attempting to make sense of this content, combined with searching across data silos such as patient records and genomics data is incredibly time intensive, with key insights and candidates missed.


A leading U.S. cancer research center is turning to Vyasa to solve this problem. By establishing an intelligent data fabric with Vyasa Layar, the research center has created a unified data platform to search and identify relationships between clinical trials and its patient population to improve trial enrollment and viability. With Vyasa, the research center can:

  • Unify siloed data sources including patient records, genomics files and clinical trials into a single platform without moving or replicating files.
  • Search across multiple data sources and quickly identify insights with the power of deep learning.
  • Explore data in natural language. No coding or data science experience required.
  • Discover relationships in their data through dynamic knowledge graphs.
  • Visualize their data in smart spreadsheets and real-time dashboards.

With Vyasa, organizations empower users across skill sets to leverage deep learning and gain new value from their data. In turn, users have experienced a 25% improvement in productivity around data analysis tasks, all without compromising on accuracy.



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.


The healthcare industry faces a number of data challenges, all of which are impacting pediatric oncology. These include:

  • Data silos across departments and institutions.
  • Limited access to the latest research and trials on pediatric cancer and treatments.
  • Tools for quickly accessing insights in complex data. Ex: published research, biomedical images, drug-like compounds, genomics files.


Advancements in deep learning combined with a novel data architecture developed by Vyasa are changing the way hospitals, researchers and oncologists can collect and analyze data related to pediatric cancer. Known as an intelligent data fabric, Vyasa enables organizations to unify disparate data sources regardless of cloud or on-premise storage location and without moving or replicating the data. Once connected, Vyasa applies deep learning on the data making it easily searchable and accessible. With an intelligent data fabric, users improve search accuracy by 97% while decreasing manual analysis time by 25% through the ability to:

  • Collaborate and share data across departments, and identify trends across patient populations.
  • Easily search and extract insights from genomics data, including processing VCF and BAM files.
  • Harmonize multiple data sources together, including structured and unstructured content. (ex: scientific literature & images).
  • Discover terms and insights not listed in ontologies with Vyasa’s named entity recognition (NER).

The Vyasa intelligent data fabric comes with a suite of low code interfaces that enable users to search and visualize their data in natural language, accellerating access to insights for physicians, oncologists, researchers and more.