OmicsFootPrint: Mayo Clinic's AI Tool Offers a New Way to Visualize Disease

Mayo Clinic researchers have pioneered an artificial intelligence (AI) tool, called OmicsFootPrint, that helps convert vast amounts of complex biological data into two-dimensional circular images. The details of the tool are published in a study in Nucleic Acids Research.

Omics is the study of genes, proteins and other molecular data to help uncover how the body functions and how diseases develop. By mapping this data, the OmicsFootPrint may provide clinicians and researchers with a new way to visualize patterns in diseases, such as cancer and neurological disorders, that can help guide personalized therapies. It may also provide an intuitive way to explore disease mechanisms and interactions.

"Data becomes most powerful when you can see the story it's telling," says lead author Krishna Rani Kalari, Ph.D., associate professor of biomedical informatics at Mayo Clinic's Center for Individualized Medicine. "The OmicsFootPrint could open doors to discoveries we haven't been able to achieve before."

Genes act as the body’s instruction manual, while proteins carry out those instructions to keep cells functioning. Sometimes, changes in these instructions - called mutations - can disrupt this process and lead to disease. The OmicsFootPrint helps make sense of these complexities by turning data - such as gene activity, mutations and protein levels - into colorful, circular maps that offer a clearer picture of what’s happening in the body.

In their study, the researchers used the OmicsFootPrint to analyze drug response and cancer multi-omics data. The tool distinguished between two types of breast cancer - lobular and ductal carcinomas - with an average accuracy of 87%. When applied to lung cancer, it demonstrated over 95% accuracy in identifying two types: adenocarcinoma and squamous cell carcinoma.

The study showed that combining several types of molecular data produces more accurate results than using just one type of data.

The OmicsFootPrint also shows potential in providing meaningful results even with limited datasets. It uses advanced AI methods that learn from existing data and apply that knowledge to new scenarios - a process known as transfer learning. In one example, it helped researchers achieve over 95% accuracy in identifying lung cancer subtypes using less than 20% of the typical data volume.

"This approach could be beneficial for research even with small sample size or clinical studies," Dr. Kalari says.

To enhance its accuracy and insights, the OmicsFootPrint framework also uses an advanced method called SHAP (SHapley Additive exPlanations). SHAP highlights the most important markers, genes or proteins that influence the results to help researchers understand the factors driving disease patterns.

Beyond research, the OmicsFootPrint is designed for clinical use. It compresses large biological datasets into compact images that require just 2% of the original storage space. This could make the images easy to integrate into electronic medical records to guide patient care in the future.

The research team plans to expand the OmicsFootPrint to study other diseases, including neurological diseases and other complex disorders. They are also working on updates to make the tool even more accurate and flexible, including the ability to find new disease markers and drug targets.

Tang X, Prodduturi N, Thompson KJ, Weinshilboum R, O'Sullivan CC, Boughey JC, Tizhoosh HR, Klee EW, Wang L, Goetz MP, Suman V, Kalari KR.
OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks.
Nucleic Acids Res. 2024 Nov 27;52(21):e99. doi: 10.1093/nar/gkae915

Most Popular Now

Stepping Hill Hospital Announced as SPAR…

Stepping Hill Hospital, part of Stockport NHS Foundation Trust, has replaced its bedside units with state-of-the art devices running a full range of information, engagement, communications and productivity apps, to...

DMEA 2025: Digital Health Worldwide in B…

8 - 10 April 2025, Berlin, Germany. From the AI Act, to the potential of the European Health Data Space, to the power of patient data in Scandinavia - DMEA 2025...

Is AI in Medicine Playing Fair?

As artificial intelligence (AI) rapidly integrates into health care, a new study by researchers at the Icahn School of Medicine at Mount Sinai reveals that all generative AI models may...

Generative AI's Diagnostic Capabili…

The use of generative AI for diagnostics has attracted attention in the medical field and many research papers have been published on this topic. However, because the evaluation criteria were...

New System for the Early Detection of Au…

A team from the Human-Tech Institute-Universitat Politècnica de València has developed a new system for the early detection of Autism Spectrum Disorder (ASD) using virtual reality and artificial intelligence. The...

Diagnoses and Treatment Recommendations …

A new study led by Prof. Dan Zeltzer, a digital health expert from the Berglas School of Economics at Tel Aviv University, compared the quality of diagnostic and treatment recommendations...

AI Tool can Track Effectiveness of Multi…

A new artificial intelligence (AI) tool that can help interpret and assess how well treatments are working for patients with multiple sclerosis (MS) has been developed by UCL researchers. AI uses...

Surrey and Sussex Healthcare NHS Trust g…

Surrey and Sussex Healthcare NHS Trust has marked an important milestone in connecting busy radiologists across large parts of South East England, following the successful go live of Sectra's enterprise...

DMEA 2025 Ends with Record Attendance an…

8 - 10 April 2025, Berlin, Germany. DMEA 2025 came to a successful close with record attendance and an impressive program. 20,500 participants attended Europe's leading digital health event over the...

Dr Jason Broch Joins the Highland Market…

The Highland Marketing advisory board has welcomed a new member - Dr Jason Broch, a GP and director with a strong track record in the NHS and IT-enabled transformation. Dr Broch...

AI-Driven Smart Devices to Transform Hea…

AI-powered, internet-connected medical devices have the potential to revolutionise healthcare by enabling early disease detection, real-time patient monitoring, and personalised treatments, a new study suggests. They are already saving lives...

Multi-Resistance in Bacteria Predicted b…

An AI model trained on large amounts of genetic data can predict whether bacteria will become antibiotic-resistant. The new study shows that antibiotic resistance is more easily transmitted between genetically...