Partnership Aims to Advance Precision Oncology Through Data-Driven Insights

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HAMBURG, Germany, May 16, 2019 /PRNewswire/ — Indivumed, a physician-led, integrated global oncology solution provider, announced today its partnership with Gnosis Data Analysis, a leading provider of automated Machine Learning and data analytics solutions. This partnership represents the next step in enhancing InduvType, Indivumed’s ground-breaking true multi-omics cancer library, with cutting edge Machine Learning technologies to create insights and make discoveries not available in any single-omics approach to cancer research.

“It is exciting to partner with a company like Gnosis, that shares our vision for advancing cancer research and patient treatments through our high-quality multi-omics library. The wealth of information in IndivuType can only be fully realized by using powerful analytical tools like the ones Gnosis develops,” said Hartmut Juhl, CEO of Indivumed. “We look forward to working with them towards advancing precision oncology by enabling faster development of affordable new treatments for patients.”

IndivuType is a knowledge and discovery platform that creates and accelerates new insights into cancer biology, thus supporting research efforts and making the development of innovative cancer drugs more efficient. IndivuType contains vast amounts of genomics, transcriptomics, proteomics, phospho-proteomics, and immuno-phenotype imaging information together with clinical and outcome data, making it a unique resource for the oncology community worldwide. Coupled with cutting-edge data analytics, the cancer database can provide deep insights into the underlying mechanisms of a patient’s cancer, addressing important needs in translational research and molecular diagnostics to ultimately deliver personalized cancer healthcare.

“Our mission is to empower discovery and development through advanced automated Machine Learning technology and AI,” said Ioannis Tsamardinos, CEO of Gnosis DA. “Our Just Add Data (JAD) Biosystem is specifically designed for the complexity and magnitude of multi-omics data and via our AI Decision Support System for model selection, we can now put unprecedented multi-omics discovery capabilities in the hands of Indivumed scientists. We are, therefore, extremely excited to partner with Indivumed and have the opportunity to apply JAD Bio to the deepest data that exists in cancer biology!”

IndivuType is the result of more than fifteen years’ experience in collecting and curating high-quality cancer biospecimens through partnerships with leading healthcare institutions around the world. Only high-quality samples can produce reliable data enabling novel discoveries in cancer biology. Currently, it takes at least ten years for a new drug to complete the process from discovery to market, and according to the Tufts Center for the Study of Drug Development (CSDD), the average cost of developing a new drug is in the range of USD $2.6 Billion. Indivumed’s Indivutype cancer library and the discoveries that come from it will have a profound impact on both the time and cost of new treatments in personalized oncology.

About Indivumed
Indivumed is a physician-led, integrated global oncology company committed to unveiling complex mechanisms of cancer, in order to support precision oncology. Only a multi-omics approach can achieve this goal – and reliable multi-omics require especially high-quality biospecimens and data. Through its three divisions, IndivuServ, IndivuType, and IndivuTest, Indivumed offers specialized products and services that support 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 www.jadbio.com

Source: PRNewswire

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