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Machine learning and computational modelling have emerged as powerful tools in both preclinical and clinical research. In this context, hybrid pipelines integrating data clustering, mechanistic modelling, machine learning, and image processing algorithms are being developed to enhance analytical capabilities.These advancements not only improve automated analysis and decision-making but also provid
https://www.photoacoustics.lu.se/computational-modelling - 2025-10-15
PhD defense Ulf Dahlstrand March 20th, 2020 at 13.00Segerfalksalen, BMC LundThesis title: Supervisor: Professor Malin MalmsjöFaculty Oponent: PhD defense Linda Rannveig ThorisdottirMay 22nd, 2020 at 09.00Segerfalksalen, BMC LundThesis title: Supervisor: Professor Malin MalmsjöFaculty Oponent:
https://www.photoacoustics.lu.se/events/upcoming-events - 2025-10-15
Gaining insights into neural tube regulation and optimizing protocols for dopaminergic neuron generation require a novel framework integrating gene circuit models with morphogen inputs. Optimized using single-cell data from human embryonic stem cell differentiation (provided by Kirkeby lab, Copenhagen University, Parmar lab, Lund University), these models will capture rostro-caudal and dorso-ventr
https://www.photoacoustics.lu.se/computational-modelling/3d-modelling-neural-tube-development - 2025-10-15
T-cell development serves as a powerful model for studying lineage commitment from multipotent progenitors. To investigate the timing and inheritance of T-cell fate decisions, multi-scale agent-based models are designed, optimized, and validated using single-cell molecular and imaging data from the Rothenberg lab at Caltech. Selected publicationsOlariu V., Yui M.A., Krupinski P., Andersson E., Ro
https://www.photoacoustics.lu.se/computational-modelling/multiscale-models-t-cell-commitment - 2025-10-15
This interdisciplinary project integrates systems biology and machine learning with experimental neuroscience to enhance the production of specific neural cell types for therapeutic applications. In collaboration with the Ottosson lab at Lund University, a unique computational framework will be developed using single-cell experimental data to optimize the generation of Parvalbumin and Somatostatin
https://www.photoacoustics.lu.se/computational-modelling/optimizing-production-transplantable-neural-cells - 2025-10-15
This project focuses on direct reprogramming of adult human dermal fibroblasts into induced neurons and induced microglia, preserving the aging signature of the original patient cells. A multi-scale computational model is developed for a 3D cell culture system integrating both cell types from the same skin biopsy, enabling the first study of neuron-microglia interactions in an aging human-based sy
https://www.photoacoustics.lu.se/computational-modelling/neurodegenerative-diseases-modelling - 2025-10-15
A deep learning method is proposed for detecting tumor genes based on their unique combined epigenetic signatures. Large volumes of epigenetic data will be processed by the Tomoiaga group at Manhattan College and Columbia. This data will be utilized to train and validate deep network models capable of accurately detecting epigenetic patterns, which can then be leveraged for the classification of g
https://www.photoacoustics.lu.se/computational-modelling/machine-learning-detection-tumor-genes-epigenetic-data - 2025-10-15
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https://www.photoacoustics.lu.se/events/past-events - 2025-10-15
The reprogramming of fibroblasts into induced pluripotent stem cells remains inefficient and not fully understood. The goal is to use machine learning and mechanistic modelling to identify barriers to reprogramming from in vitro data provided by Kaji Lab at the University of Edinburgh. Addressing these barriers could improve reprogramming efficiency and increase stem cell production. Additionally,
https://www.photoacoustics.lu.se/computational-modelling/optimizing-cell-reprogramming-pluripotency - 2025-10-15
We propose a machine learning framework in which neural network models are trained and validated using hyperspectral tumor imaging data. These models are integrated with segmentation algorithms to accurately predict the tumor’s actual size and determine the optimal amount of tissue to remove. Beyond improving surgical precision, our approach also holds the potential to accurately classify tumor ty
Lund University Clinical Center For Spectral and Acoustic Imaging Welcome to the Clinical Center For Spectral and Acoustic Imaging Read about our platform. Presentation of our research A short presentation on some of our most exciting research projects and latest findings. Calendar Link to RSS More events Our Technical Research Projects Read about our technical development. Our Clinical Research R
https://www.photoacoustics.lu.se/lund-university-clinical-center-spectral-and-acoustic-imaging - 2025-10-15
https://www.lucc.lu.se/internal/landing-page-0 - 2025-10-15
- Facilitation: Stimulating cutting-edge cancer research LUCC strives to support excellent cutting-edge cancer research by creating optimal conditions to perform world-leading basic, translational and clinical studies. LUCC furthermore strives to include researchers from related research areas, to enhance interdisciplinary collaborations and viewpoints.Firstly, LUCC will be a driving force in the
https://www.lucc.lu.se/internal/research - 2025-10-15
LUCC is governed by the regulation "Dnr STYR 2024/2957" as decided by the Board of Faculty of Medicine at Lund University on 4 December, 2024. LUCC is managed by a board, internal reference group (network group leaders, scientific advisory board and supporting personnel. The term of office is usually 3 years (2 years for PhD student representatives and 1 year for Future Faculty representative) wit
https://www.lucc.lu.se/internal/organisation-governance - 2025-10-15
Summary statement: LUCC will act at the front-line to implement cutting-edge knowledge from basic and clinical research by addressing unmet clinical needs in close collaboration with the health care sector, industry and society-at-large. VISIONLund University Cancer Centre is a leading actor in understanding malignant diseases, explaining their implications, and improving knowledge-based cancer p
https://www.lucc.lu.se/internal/strategic-plan - 2025-10-15
LUCC has 17 strategic translational networks based on cancer diagnosis, technology or processes. The aim of the networks is to bridge the basic and clinical science and the diverse disciplines. Networking activities include discussions about scientific challenges in the field, facilitation of new collaborations, and sharing technical and educational expertise. The key goal is to create opportuniti
https://www.lucc.lu.se/internal/research/strategic-networks - 2025-10-15
Breast cancer is the most common diagnosis among women worldwide, and the LUCC-Breast network is the largest cancer research area at Lund University. LUCC-Breast comprises over 20 groups and 150 members in Lund and Malmö. The scientific focus areas span from basic science to clinical studies, with major research projects within genomics, genetics, tumor biology, cell signaling, prognostic and pred
https://www.lucc.lu.se/internal/research/strategic-networks/lucc-breast-network - 2025-10-15
The UroCAN - LUCC center aims to develop healthcare and research with a focus on the three most common forms of urological cancer: prostate cancer, bladder cancer and kidney cancer. To goal of the network is to facilitate the collaboration between clinics, research, business and patient representatives to face current paradigm shifts with molecular diagnostics and new treatments. The aim of the ce
https://www.lucc.lu.se/internal/research/strategic-networks/urocan-lucc - 2025-10-15
Pediatric oncology focuses on diagnosing and treating cancer in children and adolescents, presenting unique challenges due to the rarity and diverse nature of childhood cancers. In Sweden, approximately 350 children are diagnosed with cancer yearly, whereas 55 000-60 000 adults receive a cancer diagnosis. Among these 350 children, a large diversity of cancers are seen, ranging from leukemia and br
https://www.lucc.lu.se/internal/research/strategic-networks/child-cancer-network - 2025-10-15
Cancer epidemiology is broadly about studying the distribution, patterns, and causes of cancer and cancer progression in a population. The population investigated can be small or large, ranging from hundreds to millions of individuals, either population-based or a selected population such as a particular risk group for cancer or cancer patients. Many designs can be used in cancer epidemiology. The
https://www.lucc.lu.se/internal/research/strategic-networks/cancer-epidemiology - 2025-10-15
