Partnership Will Accelerate IndivuType Database Analysis, Facilitate Discovery of Additional Drug Targets

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Hamburg, Germany (August 27, 2020) –Indivumed GmbH (“Indivumed”) and Gnosis today announced the expansion of the two companies’ partnership to expand artificial intelligence and machine learning capabilities available in the IndivuType database. In May 2019, the original partnership gave Indivumed access to JADBio, Gnosis’ software tool that specializes in predictive analytics and is specifically designed for the complexity and magnitude of multi-omics data contained in IndivuType. The incorporation of JADBio into the IndivuType database has already proven successful in several research projects by not only accelerating IndivuType’s data analysis but also uncovering previously unknown relationships between protein signatures and colorectal cancer molecular subtypes, which are being discussed with potential partners to explore its diagnostic potential and the opportunities for commercialization. The partnership expansion will deliver exclusive access to additional AI and machine learning tools for IndivuType’s rapidly growing analytics service portfolio, especially in the area of image analysis for predictive and prognostic biosignature discovery.

“Our mission to collect the highest quality tissue samples using proprietary methods throughout our global network of 31 partnered oncology clinics has allowed us to gather a tremendous volume of multidimensional data,” said Dr. Hartmut Juhl, Indivumed CEO, and founder. “The advanced data analytics solutions that Gnosis provides are essential to fully understanding the depth and complexity of multi-omics information available in the IndivuType database and will aid in the discovery of biomarkers and biosignatures to guide drug development and accurately diagnose patients.”

The IndivuType database is a powerful resource for the oncology community due to the high-quality samples that enable analysis beyond genomics by combining clinical and outcome data with transcriptomics, proteomics, phosphoproteomics, and immunophenotyping. Pairing these high-quality samples with Gnosis’ AI and machine learning solutions has the potential to provide deeper insights into oncology therapeutics and patient diagnoses and help accelerating the discovery process for new oncology drugs and therapies.

“We are honored to continue and further expand our partnership with Indivumed, which is a testimonial to the tremendous opportunities that exists in the combination of high quality deep multi-omics data applied to advanced data analytics,” said Ioannis Tsamardinos, CEO and founder of Gnosis DA. “JADBIO continues to build and expand its AI Decision support system, and with this partnership, we are able to both participate in providing deep insights for the benefit of future therapeutics in the oncology field and expand the knowledgebase of JADBio.”

About Indivumed
Indivumed is a science-led, integrated global oncology company. Our platform is an enabler for the next generation of precision oncology through our proprietary multi-omics cancer database and customized data analytics, underpinned by our global network of affiliated clinics with gold-standard quality of biospecimens. Through our unique platform, we offer specialized products and services that support our customers in biomarker and target discovery, drug development, clinical trials, individualized therapy, and more. More information at www.indivumed.com

About JADBio
JADBio is an automated machine learning tool, specifically designed for biomedical data, such as multi-omics studies. JADBio’s easy-to-use interface allows biologists, bioinformaticians, clinicians, and non-expert analysts to perform sophisticated analyses with the click of a button. It is fully automatic drastically boosting productivity, even for expert analysts. It tries thousands of combinations of algorithms and tuning parameters to find the optimal model. Novel statistical methods avoid overfitting and overestimation of performance even for low sample sizes. It performs feature selection (biosignature identification) by removing irrelevant, but also redundant features (markers) for prediction. It has been validated on hundreds of public datasets, producing novel scientific results. More information at jadbio.com

Source: Indivumed

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