Abstract of the Flexynesis information integration and evaluation workflow. Credit score: Nature Communications (2025). DOI: 10.1038/s41467-025-63688-5
The researcher has been working for a while on growing instruments that use synthetic intelligence to make extra exact diagnoses and that additionally decide the perfect type of remedy tailor-made to particular person sufferers.
Akalin’s workforce has now developed a toolkit referred to as Flexynesis, which doesn’t rely solely on classical machine studying but additionally makes use of deep studying to judge very various kinds of information concurrently—for instance, multi-omics information in addition to specifically processed texts and pictures, similar to CT or MRI scans.
“In this way, it enables doctors to make better diagnoses, prognoses, and treatment strategies for their patients,” says Akalin. Flexynesis is described intimately in a paper revealed in Nature Communications.
“We are running multiple translational projects with medical doctors who want to identify biomarkers from multi-omics data that align with disease outcomes,” says Dr. Bora Uyar, first and co-corresponding writer of the publication.
“Although many deep-learning based methods have been published for this purpose, most have turned out to be inflexible, tied to specific modeling tasks, or difficult to install and reuse. That gap motivated us to build Flexynesis as a proper toolkit, which is flexible for different modeling tasks and packaged on PyPI, Guix, Docker, Bioconda, and Galaxy, so others can readily apply it in their own pipelines.”
The software finds the foundation of the illness
Deep studying is a subfield of machine studying that goes past easy neural networks with one or two computational layers, as a substitute utilizing deep networks that function with a whole bunch and even hundreds of layers. “Cancer and other complex diseases arise from the interplay of various biological factors, for example, at the DNA, RNA, and protein levels,” explains Akalin.
Attribute modifications at these ranges—similar to the quantity of HER2 protein produced in breast or abdomen most cancers—are sometimes recorded, however usually not but analyzed along with all different therapy-relevant elements.
That is the place Flexynesis is available in. “Comparable tools so far have often been either difficult to use, or only useful for answering certain questions,” says Akalin. “Flexynesis, by contrast, can answer various medical questions at the same time: for example, what type of cancer is involved, what drugs are particularly effective in this case, and how these will affect the patient’s chances of survival.”
The software additionally helps determine appropriate biomarkers for prognosis and prognosis, or—if metastases of unknown origin are found—to determine the first tumor. “This makes it easier to develop comprehensive and personalized treatment strategies for all kinds of cancer patients,” says Akalin.
Information integration within the clinic—even with out AI expertise
Final 12 months, Akalin launched one other AI-based software referred to as Onconaut, which equally helps to determine the fitting most cancers remedy. “Onconaut relies on known biomarkers, clinical trial results, and current guidelines—so it works on a completely different principle,” explains Akalin. “The tool won’t become obsolete, but rather can be a useful complement to Flexynesis.”
One of many hurdles the brand new software nonetheless has to beat, at the very least in Germany, is the truth that multi-omics information are usually not but routinely collected in hospitals. “In the US, on the other hand, this data is frequently discussed within hospital tumor boards, where physicians from different specialties jointly plan their patients’ treatment,” says Akalin.
And his workforce has proven that the information can be utilized to precisely predict whether or not a specific remedy shall be efficient. “In Germany, detailed multi-omics data has so far only been used in flagship programs such as the MASTER program for rare cancers,” he provides. However which will quickly change.
Akalin emphasizes that customers of his software, which is at present aimed primarily at physicians and medical researchers and is constantly up to date, don’t have to have any particular background in working with deep studying.
“I hope it lowers the barriers for hospitals and research groups to carry out multimodal data integration—that is, the simultaneous analysis of omics data, written reports, and images—even without AI experts at their side,” he says. Flexynesis is definitely accessible on-line, together with directions for utilizing the software.
Extra data:
Bora Uyar et al, Flexynesis: A deep studying toolkit for bulk multi-omics information integration for precision oncology and past, Nature Communications (2025). DOI: 10.1038/s41467-025-63688-5
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Max Delbrück Middle for Molecular Medication
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