Omic is teaching AI the language of biology in order to discover new therapies and optimize patient outcomes. Unlike most discovery efforts that focus on a single protein target or pathway, we apply the entire network of DNA, RNA, protein, and metabolite interactions that can impact disease. This process is powered by our biomedical AI operating system, Omic OS, that integrates deep learning and bioinformatics workflows with one of the world's most expansive research and clinical evidence knowledge graphs.
We envision a future where health events are no longer surprises and every disease has a safe, effective treatment driven by each patient's unique biology. We are building this future.
Type of Organization
Startup - Newly established businesses, investable
Size of Organization
Organization Mailing Address
3518 Ashworth Ave N
Seattle, WA 98103
The Omic Operating System
Omic has developed an operating system for biomedical discoveries in order to enable healthcare and life sciences researchers to optimize patient treatment paths and discover new therapies. The SaaS platform includes clinical and genomic data integration and preparation tools, data science authoring, bioinformatics and cheminformatics, and embedded visualizations. Users can build AI-driven applications for patient risk mitigation, small-molecule drug discovery, and other critical precision medicine applications.
The Omic Operating System (OmicOS) is a SaaS platform for connecting multidisciplinary healthcare and life sciences researchers. To advance biomedical discoveries, we are reimagining the worlds of therapy design and treatment optimization through advanced artificial intelligence tools atop the world’s largest biomedical knowledge graph. The platform includes the following features:
Biological data, especially in the realm of genomics, needs bioinformatics tools and pipelines to extract insights from raw data. OmicBio is an integrated suite of bio- and cheminformatics tools and AI-tuned pipelines. It brings raw DNA, RNA, proteins, and metabolites on level ground with structured clinical and research data sources.
To find new treatment paths and therapies, we need a new system of scalable biomedical knowledge. OmicGraph organizes relationships in human biology into disease maps using research, knowledge bases, raw and processed data. Disease maps can be used to identify new mechanisms of disease, treatment paths, and therapies.
With the scale of biomedical data available for analysis, machine and deep learning methods are rapidly becoming the standard. OmicModel is a full data science authoring platform and model library, including artificial intelligence authoring, packaging, and deployment tools. Data science is now a core requirement in biomedical discoveries. It’s integrated into all elements of the Omic Operating System.
Longitudinal medical insights are notoriously difficult to extract from structured and unstructured clinical and genomic data. OmicMed strucutres clinical, genomic, and external patient records and frees the data and analyses through natural language searches. Systems of insight such as the Omic Operating System enable new forms of precision medicine.
Challenges exist in combining and sharing complex workflows, AI models, results, and visualizations while keeping data safe. OmicApp connects authored models and pipelines directly to dashboards and visualizations, in order to make insights immediately available and shareable without exposing underlying data. Both data security and collaboration are necessary for optimizing patient care and novel biomedical discoveries.
Integrating and organizing clincial and molecular data carries well-known challenges and limits rapid analysis and discovery. OmicBits is a clinco-genomic data management platform with AI-supported integration, external data crawling and indexing, and a virtual file system. Nearly any biomedical database or dataset can be converted into an AI-ready source.
Category of Innovation
Health IT - Health IT refers to the “pipes” or “infrastructure” that technological systems are built upon and that digital health solutions may use to provide information or other necessities. Ex. Electronic medical records (EMRs)
Intended End User
Provider - Individuals or organizations responsible for providing care to patients (e.g. doctors, nurses, hospital/clinic administrators, etc.)
Payer - Organizations responsible for issuing or administering payment for the care received by a population of people (e.g. insurance companies)
Startup - newly established business, investable
Vendor - i.e. established company, non-startup
Problem (i.e. barrier, issue, complication, etc.) being solved for the end user
Both healthcare and drug discovery researchers face challenges in developing scalable, repeatable artificial intelligence solutions that take advantage of genomic, transcriptomic, proteomic, and related -omics data.
In healthcare research, the application of diverse data such as omics and social determinants of health, in combination with clinical data, has been shown to improve population health risk prediction and clinical decision support. The data integration and transformation, along with model building, validation, packaging, and deployment, can be prohibitively time-consuming and expensive.
In drug discovery research, in silico artificial intelligence models, and in particular, deep learning, have become more prevalent. Many of these have accelerated candidate therapy generation. Still, the majority of new therapy discovery focuses on interactions between an individual protein target and a small molecule candidate, not accounting for the full context of human biology, -omics and disease networks. This has resulted in a disconnect between the promise and reality of an AI revolution in drug discovery.
Idea/solution to the problem, if applicable
The solution is to teach AI the language of biology. Omic has developed an operating system driven by the context of systems biology AI. This is how we can bring seemingly-distinct problems such as drug discovery and clinical risk prediction onto level ground.
Level of adoption (i.e. list of customers/users, testimonials, etc.), if applicable
Our platform has been battle-tested via https://c19.ai, an open-source COVID-19 AI research consortium. Through C19, Omic partnered with organizations such as the National Scientist Volunteer Database as well as various academic researchers and institutions. On our platform, users have performed such novel research as predicting COVID-19 severity using a combination of viral genetics and host factors, discovering new potential therapies to rival current leading drugs, and performing core disease and virology research.