Introducing Pi-OmniKG: Making Sense of Biomedical Data 

Converting scientific discoveries into medical products needs grounding in data — real time and historical. The biomedical field generates and utilizes terabytes of data at every stage of its value chain. However, despite a long data trail and relatively open data-sharing protocols, the industry struggles to get actionable insights on time and profitably predict outcomes due to lengthy and labor-intensive data mining processes.

Siloed, standalone datasets are opaque and the cognitive load to make sense or infer from them falls on the user. While biomedical firms deploy AI and machine learning (ML) models to offload the cognitive load, using these insights for critical decisions remains a challenge since they cannot be explained, without which firms run the risk of regulatory overburden.

For insights to become apparent, data needs to be understood within context, relationships, and patterns. In the biomedical domain, understanding the interrelatedness between disparate datasets can unlock synergies, facilitate mapping of drug-to-drug interactions, promote research toward repurposed drugs, and offset regulatory risks— all by correlating the hidden linkages of seemingly unrelated factors.

Correlating data through relational dependencies creates Knowledge Graphs (KG) — a data architecture that not only breaks down silos to boost data utilization and monetization but actively helps unearth richer and explainable insights. KGs integrate structured and unstructured multi-modal data into a cohesive network of nodes and relationships that are easier to understand and faster to draw inferences , with an unlimited sprawl potential.

However, with surging datasets, managing KGs in-house and on-premise can be untenable due to restraints on available computational power, storage, and integration capabilities. As with any other on-premises endeavor, KGs can quickly falter on performance, scalability, and cost, impacting user adoption and management buy-in.

Introducing Pi-OmniKG: A Ready-to-go, Google-Backed KG Framework

Persistent partnered with Google Cloud Platform (GCP) to create a biomedical-specific KG framework called Pi-OmniKG, which integrates data across sources, formats, types, and modalities to create intelligent, omniscient KGs that accelerate time-to-value with faster, explainable insights. The solution combines Google’s expandable, scalable, and high-performing infrastructure with Persistent’s three decades  of data engineering legacy to create a fungible KG offering that can be easily contextualized to individual client needs.

Pi-OmniKG allows biomedical firms to create their own knowledge graphs and get a bird’s-eye view of real-time and historical data across academic literature, clinical trial documents, regulatory filings, electronic health records (EHRs), etc.. It allows firms to glean relevant insights that further cement the probability of success —from discovering drug compounds to simplifying trial protocols and identifying the right patient cohorts.

Putting Data to Work: Biomedical Insights in Three Stages

KGs can be complex and intricate to operationalize. Pi-OmniKG deconstructs this in three stages:

1. Consolidating the knowledge base: Pi-OmniKG can seamlessly integrate large biomedical databases from public or proprietary scientific literature and authentic sources. Leveraging GCP’s storage, this structured and unstructured data can be stories in vectors and graphs, with the option of integrating it with other data sources based on events or triggers.
2. Designing the relational schema to create KG: Depending on the use case, Pi-OmniKG adopts a pre-defined schema to intake data and create a KG. ML models scan through the KG to understand inherent relationships and anticipate hidden ones, which can be gleaned as insights via natural language processing (NLP).
3. Actionable insights using KG: Generative AI (GenAI), backed by large language models (LLMs) that are trained on the KG, then help users with queries, or retrieving information faster through virtual assistants. GenAI also helps in analysis with quality control metrics.

Pi-OmniKG is explicitly built to support the needs of the biomedical industry, allowing it to:

• Pivot to evidence-based insights: As a framework that facilitates the generation of KGs, Pi-OmniKG enables firms to make sense of large volumes of unstructured and structured data while ensuring the insights are explainable with underlying data connections that otherwise would be difficult to unearth. This leads to evidence-based insights, which prove critical while seeking regulatory approvals or designing a trial.
• Increase efficiency by 60%: By streamlining access, utilization, and monetization across the data landscape, Pi-OmniKG enables firms to ground decisions in evidence-backed knowledge that reduces chances of failure – significantly removing guesswork and outliers, while reinforcing simplified protocols. In our experiences with clients, these factors can increase efficiency by up to 60%.
• Halve data mining and analytics costs: Pi-OmniKG acts as a precursor to advanced AI applications, specifically GenAI which automate insights gleaning through an interactive user interface. This significantly reduces time to insights and opens data to users other than data analysts to create richer, highly contextualized insights that reduce costs by as much as 50%.

To learn more about Pi-OmniKG and accelerate time-to-value through KGs, click here