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The prime focus of our Institute is to understand human pathology, primarily in the central nervous system, cancer, and immune-mediated diseases. To this end we integrate;
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imsb skip to content toggle navigation home team publications contact toggle navigation home team research groups publications contact imsb home sven 2025 02 10t09 48 47 00 00 news research research groups teaching opportunities strengthening global collaboration advancing pcai development in india our researchers collaborated with the university of uppsala department of information technology during a two week visit to thiruvananthapuram in southern india to advance the prostate cancer aggressiveness index pcai and adapt it to locally collected biopsy samples in partnership with the c dac centre and regional cancer centre rcc the work also includes developing a dedicated scanning solution the visit featured a one day international workshop in computational pathology where the team joined as international delegates and gained insight into how ai is being applied in pathology alongside scientific exchange and lab visits the team experienced strong local commitment to improving healthcare underscoring india s potential for impactful scalable diagnostic solutions read more autoantibody triggered podocyte membrane budding drives autoimmune kidney disease chronic kidney disease affects millions worldwide with damage to podocytes playing a key role we show that in membranous nephropathy disease causing autoantibodies form aggregates on podocytes that are released into the urine as specialized extracellular vesicles these vesicles offer a new non invasive way to detect and monitor autoimmune kidney disease read more a systematic analysis of the impact of data variation on ai based histopathological grading of prostate cancer histopathological grading by experts is central to prostate cancer diagnosis but ai models often struggle when biopsy samples vary in preparation and staining in this study we analyze how such variations impact ai performance and introduce pcai a robust ai based grading framework trained on patient outcomes rather than subjective labels by using large diverse multicenter data and targeted algorithmic improvements pcai achieves reliable performance that surpasses experienced pathologists read more biocontextai is a community hub for agentic biomedical systems large language models are increasingly used in biomedical research but their reliability is limited by missing domain knowledge hallucinations and poor access to specialized scientific resources we present biocontextai an open source framework for building agent based ai research assistants that can flexibly connect to biomedical databases literature and analytical tools by using standardized reusable components biocontextai enables more reliable and extensible ai workflows that align with fair principles for research software read more the prime focus of our institute is to understand human pathology primarily in the central nervous system cancer and immune mediated diseases to this end we integrate and curate big biomedical data using automated systems and extract disease relevant information using statistical graph and machine learning approaches we then use the insights we have gained to understand predict and potentially cure human malady our central technical focus is currently the development and application of deep learning based algorithms to boost the performance of clinical decision support systems the prime focus of our institute is to understand human pathology primarily in the central nervous system cancer and immune mediated diseases to this end we integrate and curate big biomedical data using automated systems and extract disease relevant information using statistical graph and machine learning approaches we then use the insights we have gained to understand predict and potentially cure human malady our central technical focus is currently the development and application of deep learning based algorithms to boost the performance of clinical decision support systems research knowledge bases although recent progress in biomedicine has made it possible to obtain quantitative information for thousands to tens of thousand entities in a few hours we are still far from understanding disease so how come that the knowledge that we are accumulating with lightning speed is not sufficient to understand what goes wrong in a human body we strongly believe that data integration and re use is what is currently limiting our understanding which is why we build intelligent knowledge bases we build automated software systems to extract transform normalize and analyze big biological data this means that we can compare hundreds of diseases at the same time across genomic epigenetic transcriptomic and protein levels considering age gender and medication it seems probable that once the machine thinking method had started it would not take long to outstrip our feeble powers alan turing machine learning to strip our knowledge bases of their secrets we use statistical inference and machine learning approaches on the one hand we use classical machine learning as well as deep learning approaches to stratify and predict disease on the other hand we use them to understand how diseases come about and extract detailed mechanisms of human pathology especially for the latter task we develop and apply state of the art deep learning algorithms to learn and extract disease relevant features that can later be used to reproduce and cure pathology ultimately our insights will fuel medical researchers and pharmaceutical engineers to understand and combat human malady research networks our institute participates in various research consortia including dfg funded collaborative research centers such as crc 1192 crc 1286 and crc 1700 working together with national and international partners on cutting edge scientific questions research knowledge bases although recent progress in biomedicine has made it possible to obtain quantitative information for thousands to tens of thousand entities in a few hours we are still far from understanding disease so how come that the knowledge that we are accumulating with lightning speed is not sufficient to understand what goes wrong in a human body we strongly believe that data integration and re use is what is currently limiting our understanding which is why we build intelligent knowledge bases we build automated software systems to extract transform normalize and analyze big biological data this means that we can compare hundreds of diseases at the same time across genomic epigenetic transcriptomic and protein levels considering age gender and medication it seems probable that once the machine thinking method had started it would not take long to outstrip our feeble powers alan turing machine learning to strip our knowledge bases of their secrets we use statistical inference and machine learning approaches on the one hand we use classical machine learning as well as deep learning approaches to stratify and predict disease on the other hand we use them to understand how diseases come about and extract detailed mechanisms of human pathology especially for the latter task we develop and apply state of the art deep learning algorithms to learn and extract disease relevant features that can later be used to reproduce and cure pathology ultimately our insights will fuel medical researchers and pharmaceutical engineers to understand and combat human malady research groups biomedical data analysis sven 2023 05 16t09 31 37 00 00 biomedical data analysis computational pathology sven 2023 05 16t09 53 40 00 00 computational pathology data integration sven 2023 05 16t11 26 15 00 00 data integration genomic ai sven 2025 06 04t07 43 03 00 00 genomic ai research groups sven 2023 05 16t09 31 37 00 00 biomedical data analysis biomedical data analysis sven 2023 05 16t09 53 40 00 00 computational pathology computational pathology sven 2023 05 16t11 26 15 00 00 data integration data integration sven 2025 06 04t07 43 03 00 00 genomic ai genomic ai teaching we are teaching courses in machine and deep learning to physicists and computer scientists we offer courses in bioinformatics systems biology data analysis and programming for students with various backgrounds ranging from computer scientists to medical students in addition we offer seminar series with external speakers on advanced topics and recent scientific breakthroughs in biomedical ai via our baiome lecture series teaching we are teaching courses in machine and deep learning to physicists and computer scientists we offer courses in bioinformatics systems biology data analysis and programming for students with various backgrounds ranging from computer scientists to medical students in addition we offer seminar series with external speakers on advanced topics and recent scientific breakthroughs in biomedical ai via our baiome lecture series opportunities we are continuously taking on talented bachelor master phd and postdoctoral students if you want to join our team of ai experts don t hesitate to contact us michael brehler at zmnh uni hamburg de please also consider openings at our baiome center for biomedical ai at the university medical center of hamburg eppendorf baiome org opportunities we are continuously taking on talented bachelor master phd and postdoctoral students if you want to join our team of ai experts don t hesitate to contact us michael brehler at zmnh uni hamburg de please also consider openings at our baiome center for biomedical ai at the university medical center of hamburg eppendorf baiome org falkenried 94 20251 hamburg zmnh uke 49 40 7410 55081 privacy policy legal disclosure baiome github privacy policy legal disclosure baiome github close sliding bar area stay in touch contact info 9876 west green street phone 1 800 987 6543 mobile 1 800 345 6789 email info your domain com web avada wp theme page load link go to top
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