Science and medicine are becoming increasingly digital. Analyzing the resulting volumes of information -- known as "big data" -- is considered a key to better treatment options. "Medical research data are a treasure. They can play a decisive role in developing personalized therapies that are tailored to each individual more precisely than conventional treatments," said Joachim Schultze, Director of Systems Medicine at the DZNE and professor at the Life & Medical Sciences Institute (LIMES) at the University of Bonn. "It's critical for science to be able to use such data as comprehensively and from as many sources as possible."

Following a similar principle--called "swarm learning"--an international research team has trained artificial intelligence algorithms to detect blood cancer, lung diseases and COVID-19 in data stored in a decentralized fashion. This approach has advantage over conventional methods since it inherently provides privacy preservation technologies, which facilitates cross-site analysis of scientific data. Swarm learning could thus significantly promote and accelerate collaboration and information exchange in research, especially in the field of medicine. Experts from the DZNE, the University of Bonn, the information technology company Hewlett Packard Enterprise (HPE) and other research institutions report on this in the scientific journal Nature. Science and medicine are becoming increasingly digital.

Artificial intelligence is a much-discussed topic in medicine, especially in the field of diagnostics. "We aimed to investigate the potential on the basis of a specific example," explains Prof. Joachim Schultze, a research group leader at the DZNE and head of the Department for Genomics and Immunoregulation at the LIMES Institute of the University of Bonn. "Because this requires large amounts of data, we evaluated data on the gene activity of blood cells. Numerous studies have been carried out on this topic and the results are available through databases. Thus, there is an enormous data pool. We have collected virtually everything that is currently available."

Washington: Tracking down acute myeloid leukaemia (AML), researchers have proved that artificial intelligence can detect forms of blood cancer. The study was conducted by the researchers of the German Center for Neurodegenerative Diseases (DZNE) and was published in the recent edition of the iScience journal. The approach used by researchers revolved around the gene activity analysis of cells that are present in the blood. This approach could support conventional diagnostics and accelerate therapy of the disease. "Some studies have been carried out on this topic and the results are available through. Thus, there is an enormous data pool. We have collected virtually everything that is currently available," said Professor Joachim Schultze who led the group of researchers of the study.

Artificial intelligence is a much-discussed topic in medicine, especially in the field of diagnostics. "We aimed to investigate the potential on the basis of a specific example," explains Prof. Joachim Schultze, a research group leader at the DZNE and head of the Department for Genomics and Immunoregulation at the LIMES Institute of the University of Bonn. "Because this requires large amounts of data, we evaluated data on the gene activity of blood cells. Numerous studies have been carried out on this topic and the results are available through databases. Thus, there is an enormous data pool. We have collected virtually everything that is currently available."

Artificial Intelligence can help detect one of the most common forms of blood cancer - acute myeloid leukaemia (AML) - with high reliability, new research has found. Their approach, based on the analysis of the gene activity of cells found in the blood, could support conventional diagnostics and possibly accelerate the beginning of therapy, said the study published in the journal iScience. In the early stages the symptoms of AML can resemble those of a bad cold. However, AML is a life-threatening disease that should be treated as quickly as possible. "With a blood test, as it seems possible on the basis of our study, it is conceivable that the family doctor would already clarify a suspicion of AML," said Joachim Schultze, a research group leader at the German Center for Neurodegenerative Diseases (DZNE).

Artificial intelligence can detect one of the most common forms of blood cancer - acute myeloid leukemia (AML) - with high reliability. Researchers at the German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn have now shown this in a proof-of-concept study. Their approach is based on the analysis of the gene activity of cells found in the blood. Used in practice, this approach could support conventional diagnostics and possibly accelerate the beginning of therapy. The research results have been published in the journal "iScience".

BASF and Technische Universität Berlin (TU Berlin) have signed an agreement to cooperate closely in the area of machine learning. The aim of the Berlin-based Joint Lab for Machine Learning (BASLEARN) is to develop workable new mathematical models and algorithms for fundamental questions relating to chemistry, for example from process or quantum chemistry. Both partners are jointly committed to this aim in the coming years. As an essential part of the cooperation, BASF will support the research work of Dr. Klaus Robert Müller, professor of machine learning and spokesperson for the Berlin Center for Machine Learning at TU Berlin, with a total of over €2.5 million over the coming five years. Machine learning is a key pillar of artificial intelligence. The objective is to analyze large volumes of data to recognize patterns and relationships which can be used to develop prediction models that optimize themselves based on their results.