Fetch.ai Partners with PSNC to Utilize Collective Learning for Cancer Cell Detection

Cambridge-based tech startup Fetch.ai has announced a partnership with Poland’s Poznan Supercomputing and Networking Center (PSNC).

Fetch.ai has built an open access, tokenized, decentralized machine learning network to enable smart infrastructure built around a decentralized digital economy. Its network is based around an open-source technology that any user can run to connect to the network, giving access to the power of AI on a world-scale secure dataset, to carry out complex coordination tasks in the modern economy. One of the company’s key tools is a set of autonomous software agents that provide AI services, connecting suppliers and consumers of raw and processed data.

PSNC, affiliated to the Institute of Bioorganic Chemistry of the Polish Academy of Sciences, is an internationally known node of the European Research Area in the field of IT infrastructure of science and an important R&D center in the field of information and communication technologies (ICT). As a development center of e-Infrastructure, PSNC designed and built the Metropolitan Network POZMAN, High-Performance Computing Center, and the national broadband network PIONIER, maintained and still developed by PSNC.

Utilizing Fetch.ai’s Collective Learning model, PSNC will facilitate the sharing and analysis of collective data in order to identify cancer cells in blood or tissue biopsies.

Recently, the collective learning protocol successfully distinguished COVID-19 patients from those with pneumonia from different causes with 97% accuracy. By using experimental data from PSNC, Fetch’s collective learning algorithms will analyze data in a shared setting, keeping each dataset private, while making the results, (a machine learning model) publicly available for usage in identifying cancer cells in patient’s blood or tissue biopsies. It will also assist in the molecular profiling of tumors to identify targetable alterations in treatments, as well as the early discovery and therapeutic management of cancer patients. In addition, it will allow healthcare data to be accessible to multiple stakeholders (hospitals and research labs) and used for research to accelerate the development of new therapies for cancer and other diseases.

“PSNC provides networking to all the clinical university hospitals in Poland. PSNC jointly with the Institute of Bioorganic Chemistry have been conducting many R&D activities to offer added-value services for advanced data analysis and AI in the area of bioinformatics,” said Maria Minaricova of Fetch.ai. “Combining their reach with the Fetch.ai collective learning module will allow researchers to make use of biomedical datasets across Europe, and analyze them remotely, whilst retaining the data within the country of origin. The ultimate goal is to facilitate the use of healthcare data for research to accelerate the development of new therapies for cancer and other diseases, and make it accessible across Europe.”

According to data going back as far as 1950, early detection is a key indicator in the prevention of many diseases. This can be seen with the implementation of technological evolutions such as the Pap test, which after its introduction resulted in a 70% decline in cervical cancer incidence and deaths in developed countries. The key to the development of these early detection methods is access to large, high-quality datasets that represent a diverse global population.

“Decentralized ledgers (blockchains) have the potential to optimize the process in which we share private data without requiring trust between participants. Paired with artificial intelligence, this can enable more comprehensive understandings of vast amounts of data,” said Krzysztof Kurowski of PSNC. “Fetch.ai’s Collective Learning module has created a scenario in which multiple stakeholders (hospitals and research labs) across Europe can build a shared machine learning model without compromising the privacy of their data. This will allow them to share sensitive information in order to further analyze and detect cancer symptoms amongst patients. We look forward to spearheading this research alongside them.”

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