BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260617T132208Z UID:45cd3bac-08fd-4e6a-bf3e-d0b2128a947a DTSTART:20260302T080000Z DTEND:20260306T160000Z DESCRIPTION:Educators &\; Organizers:\nOliver Kohlbacher (CIBI)\, Philip p Thiel\, Manfred Claassen\, Carsten Eickhoff\, Kerstin Ritter\n\nDate:\n0 2-03-2026 to 06-03-2026\n\nLocation:\n\nConference Center at Heiligkreuzta l Monastery\n(Tagungshaus Kloster Heiligkreuztal)\nAm Münster 7\n88499 Al theim-Heiligkreuztal\nGermany\n\nhttps://www.kloster-heiligkreuztal.de/ \n https://maps.app.goo.gl/9gKrCXXoj1aNowrw9 \n\nContents:\nIncreasingly larg e machine learning models are transforming how research is done in the lif e sciences. Such models enable addressing research questions with complex data modalities\, and further to jointly consider multiple such data modal ities to this end. While such approaches show impressive capabilities to e stablish non-trivial input-output relationships\, interpretation of the un derlying models remains a challenge.\n\nOur spring school aims at bridging this gap by covering interpretable machine learning approaches to study v arious data modalities encountered and integrated in translational researc h projects. Specifically\, we plan to consider natural language-\, radiolo gical- and molecular imaging data. The spring school will comprise input l ectures and integrated project work that will be supervised by invited lec turers and their teams.\n\nSpecifically\, we will cover lectures on interp retable models of single-cell biology\, radiological data and natural lang uage. These lectures will introduce basic and advanced methodological conc epts and their application in translational projects. The spring school pa rticipants will apply these concepts in hands-on workshops on multimodal d atasets covering the data modalities introduced by the lecturers with the goal to identify potentially novel intermodal patterns of translational re levance.\n\nAgenda:\n\nArrival - March 2\n\nfrom 2.00 pm\n\narrival at ven ue\n\n6.00 - 7.00 pm\n\ndinner\n\nDay 1 - March 3 \n\n09.00 - 10.00 am\n\n Introduction round/activity participants &\; trainers\n\n10.00 - 12.00 am\n\nInput lecture: Interpretable Machine Learning Models for Single-Cell Biology (Claassen)\n\n12.00 - 01.00 pm\n\nLunch break\n\n01.00 - 01.30 pm \n\nIntroduction to spring school data set(s) &\; definition of teamwor k goals\n\n01.30 - 02.00 pm\n\nDefinition teams \n\n02.00 - 06.00 pm\n\nTe amwork interpretable machine learning models for single cell biology\n\n06 .00 - 07.00 pm\n\nDinner\n\n07.00 - \n\nEvening activity\n\n \n\nDay 2 - M arch 4\n\n09.00 - 12.00 am\n\nInput lecture: Interpretable Machine Learnin g Models for Medical Imaging Data (Ritter)\n\n12.00 - 01.00 pm\n\nLunch br eak\n\n01.00 - 03.00 pm\n\nTeam activity (e.g. hiking)\n\n03.00 - 06.00 pm \n\nTeamwork interpretable machine learning models for radiology\n\n06.00 - 07.00 pm\n\nDinner\n\n07.00 - \n\nEvening activity\n\n \n\nDay 3 - March 5 \n\n09.00 - 12.00 am\n\nInput lecture: Language Modeling and Interpreta tion (Eickhoff)\n\n12.00 - 01.00 pm\n\nLunch break\n\n01.00 - 06.00 pm\n\n Teamwork large language models for interpretation \n\n06.00 - 07.00 pm\n\n Dinner\n\n07.00 - \n\nEvening activity\n\n \n\nDay 4 - March 6 \n\n09.00 - 12.00 am\n\nConsolidation results and preparation of final presentation\n \n12.00 - 01.00 pm\n\nLunch break\n\n01.00 - 05.00 pm\n\nConcluding sympos ium and discussion\n\n05.00 - 05.30 pm\n\nWrap-up and departure\n\nLearnin g goals:\nParticipants will gain hands-on experience in interpretable mach ine learning for multimodal biomedical data\, developing the skills to col laboratively design and implement (publication-ready) bioinformatic analys esthat drive insight and impact in translational research.\n\nPrerequisite s:\nYou are a passionate PhD student or postdoctoral researcher eager to w ork at the intersection of cutting-edge data science and biomedical discov ery?\n\nWe welcome applicants from two complementary backgrounds:\n\nBioin formatics\, machine learning\, or data science\, with a keen interest and some hands-on experience in analyzing biological or medical data.\nExperim ental biologyor translational medicine\, with a strong track record of per forming your own data analyses using bioinformatics or machine learning me thods.\nIf you’re excited about bridging disciplines and unlocking insig hts from complex biomedical data\, and you have solid programming skills i n Python\, we’d love to have you on board.\n\nYou must bring a modern la ptop with WLAN and Python development capabilities\n\nKeywords:\nInterpret able AI\, ML in life sciences\, multimodal data\, translational research\, AI in biomedical research\, data integration\n\nTools:\nPython LOCATION:Am Münster 7\, 7 Am Münster SUMMARY:Spring School - Interpretable Machine Learning Models in Biomedicin e URL;VALUE=URI:https://www.denbi.de/training-courses-2026/1892-spring-school -interpretable-machine-learning-models-in-biomedicine END:VEVENT END:VCALENDAR