April 1, 2026 – A new AI algorithm can dramatically hasten the search for the genetic causes of rare diseases, a process that often takes years and often ends without a diagnosis.

According to the study published in Genetics Medicine and led by Dr. Christina Canavati and Prof. Yuval Tabach from the Faculty of Medicine at the Hebrew University of Jerusalem (HU), the tool identifies the gene that is responsible for a patient’s symptoms.

The AI algorithm, named EvORanker, compares genetic evolutionary patterns across more than 1,000 species to detect hidden relationships between genes, even those that medical knowledge has previously never linked to a disease. In most cases, the tool successfully identified the disease-causing gene. This approach could significantly shorten the diagnostic journey and help guide doctors toward effective treatments much sooner.

For families of children living with rare diseases, the search for a diagnosis is often long, uncertain, and deeply exhausting, marked by years of unanswered questions, repeated tests, and the absence of clear medical explanations. Globally, rare diseases affect up to 5% of the population. In Israel, the burden is even higher, due in part to genetic factors within certain communities. For these patients and families, faster diagnosis brings renewed hope for treatment.

This journey, often called the “diagnostic odyssey,” can take up to a decade. Even in the age of advanced genetic sequencing, most patients remain undiagnosed, leaving families without clarity, treatment, or closure.

In clinical testing, the algorithm identified the correct disease-causing gene as the top candidate in nearly 70% of cases, and placed it within the top five in 95% of cases, outperforming existing tools, especially in the most challenging scenarios involving poorly understood genes.

But beyond the numbers are real patients.

In one case described in the study, a child with a complex neurodevelopmental disorder had undergone extensive testing without a diagnosis. Using EvORanker, researchers identified a previously unrecognized gene as the likely cause, opening the door to understanding the disease and, potentially, treating it.

In another case, the algorithm revealed the genetic basis of a severe multisystem disorder affecting multiple organs. The discovery not only provided answers to the family but also pointed researchers toward possible therapeutic strategies.

“These are thousands of cases like that around the world that fall through the cracks of current medicine,” said Prof. Tabach. “Our goal was to give patients and clinicians a tool that can find fast and accurate answers where none existed before.”

The implications go even further. By uncovering new disease genes, EvORanker also helps identify existing drugs that could be repurposed, a shortcut that could save years of development time and bring treatments to patients faster.

The research builds on more than a decade of work combining evolutionary biology and computational science. Earlier discoveries by Prof. Tabach and collaborators demonstrated that genes that evolve together often function together, leading to dozens of breakthroughs. EvORanker turns that principle into a powerful and fast diagnostic engine.

And while rare diseases are the immediate focus, the team is already looking ahead to applying the EvORanker to cancer diagnoses to uncover why some tumors unexpectedly regress, and how those mechanisms could be harnessed for the treatment of Stage 4 cancer patients.

EvORanker is now available as an accessible tool for researchers and clinicians, with additional studies already underway and new clinical applications emerging.

This work is an expanded real-life case study based on the theoretical work published in January 2024 in the article “Using multi-scale genomics to associate poorly annotated genes with rare diseases,” which can be accessed here.

The research paper titled “Biallelic SUPT4H1 Variants Cause a Multi-system Neurodevelopmental Disorder Associated with Disrupted Transcription” is now available in Genetics Medicine and can be accessed here.

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