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arXiv 2024年

作者： Wijesinghe, Isuru Nix, Michael Zakeri, Arezoo Hokmabadi, Alireza Al-Qaisieh, Bashar Gooya, Ali Taylor, Zeike A. Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Mechanical Engineering University of Leeds Leeds United Kingdom Institute of Medical and Biological Engineering School of Mechanical Engineering University of Leeds Leeds United Kingdom Department of Medical Physics and Engineering St James’s University Hospital Leeds Teaching Hospitals NHS Trust Leeds United Kingdom Division of Informatics Imaging and Data Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom School of Medicine and Population Health University of Sheffield Sheffield United Kingdom School of Computing Science University of Glasgow Glasgow United Kingdom

We propose Deep-Motion-Net: an end-to-end graph neural network (GNN) architecture that enables 3D (volumetric) organ shape reconstruction from a single in-treatment kV planar X-ray image acquired at any arbitrary projection angle. Estimating and compensating for true anatomical motion during radiotherapy is essential for improving the delivery of planned radiation dose to target volumes while sparing organs-at-risk, and thereby improving the therapeutic ratio. Achieving this using only limited imaging available during irradiation and without the use of surrogate signals or invasive fiducial markers is attractive. The proposed model learns the mesh regression from a patient-specific template and deep features extracted from kV images at arbitrary projection angles. A 2D-CNN encoder extracts image features, and four feature pooling networks fuse these features to the 3D template organ mesh. A ResNet-based graph attention network then deforms the feature-encoded mesh. The model is trained using synthetically generated organ motion instances and corresponding kV images. The latter is generated by deforming a reference CT volume aligned with the template mesh, creating digitally reconstructed radiographs (DRRs) at required projection angles, and DRR-to-kV style transferring with a conditional CycleGAN model. The overall framework was tested quantitatively on synthetic respiratory motion scenarios and qualitatively on in-treatment images acquired over full scan series for liver cancer patients. Overall mean prediction errors for synthetic motion test datasets were 0.16±0.13 mm, 0.18±0.19 mm, 0.22±0.34 mm, and 0.12±0.11 mm. Mean peak prediction errors were 1.39 mm, 1.99 mm, 3.29 mm, and 1.16 mm. © 2024, CC BY-SA.

关键词： Radiotherapy

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Complex wound analysis using AI

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Computers in Biology and Medicine 2025年 190卷 109945-109945页

作者： Robinson, Connor J. Dickie, Bruce Lindner, Claudia Herrera, Jeremy Dingle, Lewis Reid, Adam J. Wong, Jason K.F. Hiebert, Paul Cootes, Timothy F. Kurinna, Svitlana Division of Cell Matrix Biology & Regenerative Medicine School of Biological Sciences FBMH The University of Manchester M13 9PT United Kingdom Division of Informatics Imaging & Data Sciences School of Health Sciences FBMH The University of Manchester M13 9PT United Kingdom Department of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Plastic Surgery & Burns Wythenshawe Hospital Manchester University NHS Foundation Trust Manchester Academic Health Science Centre ManchesterM23 9LT United Kingdom Biomedical Institute for Multimorbidity Centre for Biomedicine Hull York Medical School The University of Hull HU6 7RX United Kingdom

Impaired wound healing is a significant clinical challenge. Standard wound analysis approaches are macroscopic, with limited histological assessments that rely on visual inspection of haematoxylin and eosin (H&E)-stained sections of biopsies. The analysis is time-consuming, requires a specialist trained to recognise various wound features, and therefore is often omitted in practice. We present an automated deep-learning (DL) approach capable of objectively and comprehensively analysing images of H&E-stained wound sections. Our model has a deep neural network (DNN) architecture, optimised for segmentation of characteristic wound features. We employed our model for the first-time analysis of human complex wounds. Histologically, human wounds are extremely variable, which presented a challenge when segmenting the different tissue classes. To validate our approach, we used mouse wound biopsy images across four timepoints of healing and employed the same DNN architecture for training and analysis in this context (89 % mean test set accuracy). We revised our approach for human complex wounds, analysing the biopsies at a cellular level, where our model performance improved (97 % mean test set accuracy). Together, our approach allows: (i) comprehensive analysis of human wound biopsy images;(ii) in-depth analysis of key features of mouse wound healing with accurate morphometric analysis and;(iii) analysis and quantification of immune cell infiltration, to aid clinical diagnosis of human complex wounds. © 2025 The Authors

关键词： Biopsy

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Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

arXiv

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arXiv 2025年

作者： Huang, Yepeng Su, Xiaorui Ullanat, Varun Liang, Ivy Clegg, Lindsay Olabode, Damilola Ho, Nicholas John, Bino Gibbs, Megan Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Program in Biological and Biomedical Sciences Harvard Medical School BostonMA United States Harvard College CambridgeMA United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca GaithersburgMD United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Program in Computational Biology Carnegie Mellon University PittsburghPA United States Imaging and Data Analytics Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations, reducing late-stage clinical failures, and accelerating the development of precision therapies. Current models rely on structural or target-based features to identify high-efficacy, low-toxicity drug combinations. However, these approaches fail to incorporate the multimodal data necessary for accurate, clinically-relevant predictions. Here, we introduce MADRIGAL, a multimodal AI model that learns from structural, pathway, cell viability, and transcriptomic data to predict drug combination effects across 953 clinical outcomes and 21,842 compounds, including combinations of approved drugs and novel compounds in development. MADRIGAL uses a transformer bottleneck module to unify preclinical drug data modalities while handling missing data during training and inference—a major challenge in multimodal learning. It outperforms single-modality methods and state-of-the-art models in predicting adverse drug interactions. MADRIGAL performs virtual screening of anticancer drug combinations and supports polypharmacy management for type II diabetes and metabolic dysfunction-associated steatohepatitis (MASH). It identifies transporter-mediated drug interactions. MADRIGAL predicts resmetirom, the first and only FDA-approved drug for MASH, among therapies with the most favorable safety profile. It supports personalized cancer therapy by integrating genomic profiles from cancer patients. Using primary acute myeloid leukemia samples and patient-derived xenograft models, it predicts the efficacy of personalized drug combinations. Integrating MADRIGAL with a large language model allows users to describe clinical outcomes in natural language, improving safety assessment by identifying potential adverse interactions and toxicity risks. MADRIGAL provides a multimodal approach for designing combination therapies with improved predictive accuracy and clinical relevance. Copy

关键词： Diseases

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A multiomics discriminatory analysis approach to identify drought-related signatures in maize nodal roots

A multiomics discriminatory analysis approach to identify dr...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Sidharth Sen Tyler McCubbin Shannon K. King Laura A. Greeley Shuai Zeng Cheyenne Baker Rachel Mertz Nicole D. Niehues Jonathon T. Stemmle Felix B. Fristchi David Braun Scott C. Peck Melvin J. Oliver Robert E. Sharp Trupti Joshi Institute for Data Science and Informatics & Informatics & Interdisciplinary Plant Group University of Missouri Columbia MO USA Division of Plant Sciences & Interdisciplinary Plant Group University of Missouri Columbia MO USA University of Missouri Columbia MO USA Division of Biological Sciences & Interdisciplinary Plant Group University of Missouri Columbia MO USA School of Journalism University of Missouri Columbia MO USA Divisions of Plant Science & Biological Sciences Interdisciplinary Plant Group University of Missouri Columbia MO USA Institute for Data Science & Informatics Columbia MO USA

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Maize is one of the major food crops grown in the continental US, and as such, major interest is directed towards understanding its adaptability to drought stress. Certain cultivars of maize have shown increased resistance to water shortages, by continuing to maintain root growth even when under severe water stress. To better understand the molecular mechanisms which lead to such adaptation, we analyzed multiomics datasets generated from the growth zone of nodal roots from FR697, an inbred line that shows a superior capacity for root growth maintenance under drought stress. We used a research pipeline consisting of a discriminatory multiomics data integration approach, which uses a combination of sparse Generalized Canonical Correlation Analysis (sGCCA) and generalized Partial Least Square (PLS) analysis instead of traditional “filter funnel” approaches, to incorporate all datasets into one holistic global network and form clusters spanning all omics levels. We then linked significant elements from these clusters to various observations associated with drought stress in the root tip samples, reinforced by their roles in biological pathways.

关键词： Plants (biology) Stress Biology Correlation Bioinformatics Genomics Stress measurement

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Predicting risk of cardiovascular disease using retinal optical coherence tomography imaging

arXiv

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arXiv 2024年

作者： Maldonado-Garcia, Cynthia Bonazzola, Rodrigo Ferrante, Enzo Julian, Thomas H. Sergouniotis, Panagiotis I. Ravikumar, Nishant Frangi, Alejandro F. Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds Leeds United Kingdom CONICET-UNL Santa Fe Argentina Division of Evolution Infection and Genomics School of Biological Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom Manchester Royal Eye Hospital Manchester University NHS Foundation Trust Manchester United Kingdom Manchester Centre for Genomic Medicine Saint Mary’s Hospital Manchester University NHS Foundation Trust Manchester United Kingdom Wellcome Genome Campus Cambridge United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom School of Computer Science Faculty of Science and Engineering University of Manchester Kilburn Building Manchester United Kingdom Christabel Pankhurst Institute University of Manchester Manchester United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom

Cardiovascular diseases (CVD) are the leading cause of death globally. Non-invasive, cost-effective imaging techniques play a crucial role in early detection and prevention of CVD. Optical coherence tomography (OCT) has gained recognition as a potential tool for early CVD risk prediction, though its use remains underexplored. In this study, we investigated the potential of OCT as an additional imaging technique to predict future CVD events. We analysed retinal OCT data from the UK Biobank. The dataset included 612 patients who suffered a myocardial infarction (MI) or stroke within five years of imaging and 2,234 controls without CVD (total: 2,846 participants). A self-supervised deep learning approach based on Variational Autoencoders (VAE) was used to extract low-dimensional latent representations from high-dimensional 3D OCT images, capturing distinct features of retinal layers. These latent features, along with clinical data, were used to train a Random Forest (RF) classifier to differentiate between patients at risk of future CVD events (MI or stroke) and healthy controls. Our model achieved an AUC of 0.75, sensitivity of 0.70, specificity of 0.70, and accuracy of 0.70, outperforming the QRISK3 score (the third version of the QRISK cardiovascular disease risk prediction algorithm;AUC = 0.60, sensitivity = 0.60, specificity = 0.55, accuracy = 0.55). The choroidal layer in OCT images was identified as a key predictor of future CVD events, revealed through a novel model explainability approach. This study demonstrates that retinal OCT imaging is a cost-effective, non-invasive alternative for predicting CVD risk, offering potential for widespread application in optometry practices and hospitals. © 2024, CC BY-NC-ND.

关键词： Optical coherence tomography

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Population-based germline testing of BRCA1, BRCA2, and PALB2 in breast cancer patients in the United Kingdom: Evidence to support extended testing, and definition of groups who may not require testing

Genetics in Medicine Open

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Genetics in Medicine Open 2024年 2卷 100849-100849页

作者： Evans, D. Gareth Woodward, Emma R. Burghel, George J. Allen, Sophie Torr, Beth Hamill, Monica Kavanaugh, Grace Hubank, Mike Bremner, Stephen Jones, Christopher I. Schlecht, Helene Astley, Susan Bowers, Sarah Gibbons, Sarah Ruane, Helen Fosbury, Caroline Howell, Sacha J. Forde, Claire Lalloo, Fiona Newman, William G. Smith, Miriam J. Howell, Anthony Turnbull, Clare Gandhi, Ashu Manchester Centre for Genomic Medicine Manchester University Hospitals NHS Foundation Trust Manchester United Kingdom Division of Evolution and Genomic Sciences School of Biological Sciences Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Prevent Breast Cancer Centre Wythenshawe Hospital Manchester Universities Foundation Trust Wythenshawe Manchester United Kingdom Manchester Breast Centre The Christie NHS Foundation Trust Wilmslow Road Manchester United Kingdom The Institute of Cancer Research 15 Cotswold Road Surrey Sutton United Kingdom Brighton and Sussex Clinical Trials Unit Brighton and Sussex Medical School Brighton United Kingdom Division of Informatics Imaging and Data Sciences Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Division of Cancer Sciences Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom

Purpose: To assess the contribution of germline pathogenic variants (PVs) in population-based series of breast cancers and the best strategy to improve detection rates. Methods: Three cohort studies were utilized, including a hospital-based series identified from new UK mainstream testing criteria (group-1), offering testing to all women (group-2-BReast CAncer [BRCA]-DIRECT), and a Greater Manchester cohort study recruited from the mammography screening population (group-3-Predicting Risk of Cancer at Screening). DNA samples from women with breast cancer were sequenced for PVs in BRCA1, BRCA2, and Partner and Localiser of BRCA2 (PALB2). The Manchester score (MS) was used at different points thresholds. Current mainstream criteria include women diagnosed <40 years and all triple negative <60 years or an MS ≥15. Results: Thirty-six PVs (BRCA1 = 9, BRCA2 = 18, PALB2 = 9) were identified among 1061 women with breast cancer (3.4%). Mainstreaming criteria identified 21 of 36 (58%) of PVs by testing 190 women;detection rate (8.4%), specificity = 83.5%. A better detection rate was found using an MS threshold of 12-points with 66.7% (24/36) sensitivity and 85.7% specificity in 171 women. No PVs were identified in 158 women with grade-1 invasive cancers. The best strategy to detect all PVs was an MS ≥3 with specificity of 32.6%. Conclusion: In order to detect higher PV rates on a population basis the best strategy is to reduce the MS threshold for genetic testing. © 2023 The Authors

关键词： BRCA1 BRCA2 Breast cancer Manchester score PALB2 population testing

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Mlf-core: A framework for deterministic machine learning

arXiv

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arXiv 2021年

作者： Heumos, Lukas Ehmele, Philipp Kuhn Cuellar, Luis Menden, Kevin Miller, Edmund Lemke, Steffen Gabernet, Gisela Nahnsen, Sven Tübingen University of Tübingen Tübingen Germany Institute of Computational Biology Helmholtz Zentrum München Munich Germany Munich Germany School of Life Sciences Weihenstephan Technical University of Munich Munich Germany Department of Informatics University of Hamburg Hamburg Germany Department of Biological Sciences Center for Systems Biology The University of Texas at Dallas RichardsonTX United States Biomedical Data Science Department for Computer Science University of Tübingen Tübingen Germany

Machine learning has shown extensive growth in recent years and is now routinely applied to sensitive areas1. To allow appropriate verification of predictive models before deployment, models must be deterministic. However, major machine learning libraries default to the usage of non-deterministic algorithms based on atomic operations. Solely fixing all random seeds is not sufficient for deterministic machine learning. To overcome this shortcoming, various machine learning libraries released deterministic counterparts to the non-deterministic algorithms. We evaluated the effect of these algorithms on determinism and runtime. Based on these results, we formulated a set of requirements for deterministic machine learning and developed a new software solution, the mlf-core ecosystem, which aids machine learning projects to meet and keep these requirements. We applied mlf-core to develop deterministic models in various biomedical fields including a single cell autoencoder with TensorFlow, a PyTorch-based U-Net model for liver-tumor segmentation in CT scans, and a liver cancer classifier based on gene expression profiles with XGBoost. © 2021, CC BY-SA.

关键词： Libraries

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An open unified deep graph learning framework for discovering drug leads

arXiv

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arXiv 2022年

作者： Yin, Yueming Hu, Haifeng Yang, Zhen Yang, Jitao Ye, Chun Wu, Jiansheng Goh, Wilson Wen Bin The School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China The School of Computer Science and Engineering Nanyang Technological University 637551 Singapore The National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing210003 China The School of Geographic and Biologic Information with Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Lee Kong Chian School of Medicine School of Biological Sciences Nanyang Technological University 637551 Singapore Center for Biomedical Informatics 636921 Singapore

Computational discovery of ideal lead compounds is a critical process for modern drug discovery. It comprises multiple stages: hit screening, molecular property prediction, and molecule optimization. Current efforts are disparate, involving the establishment of models for each stage, followed by multistage multi-model integration. However, this is non-ideal, as clumsy integration of incompatible models increases research overheads, and may even reduce success rates in drug discovery. Facilitating compatibilities requires establishing inherent model consistencies across lead discovery stages. Towards that effect, we propose an open deep graph learning (DGL) based pipeline: generative adversarial feature subspace enhancement (GAFSE), which first unifies the modeling of these stages into one learning framework. GAFSE also offers standardized modular design and streamlined interfaces for future expansions and community support. GAFSE combines adversarial/generative learning, graph attention network, graph reconstruction network, and optimizes the classification/regression loss, adversarial/generative loss, and reconstruction loss simultaneously. Convergence analysis theoretically guarantees model generalization performance. Exhaustive benchmarking demonstrates that the GAFSE pipeline achieves excellent performance across almost all lead discovery stages, while also providing valuable model interpretability. Hence, we believe this tool will enhance the efficiency and productivity of drug discovery researchers. Copyright © 2022, The Authors. All rights reserved.

关键词： Molecules

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Mitigating medical dataset bias by learning adaptive agreement from a biased council

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Medical image analysis 2025年 105卷 103629页

作者： Luyang Luo Xin Huang Minghao Wang Zhuoyue Wan Wanteng Ma Hao Chen Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Biomedical Informatics Harvard University Cambridge USA. Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China. Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China. Department of Computing The Hong Kong Polytechnic University Hong Kong China. Department of Statistics and Data Science University of Pennsylvania Philadelphia USA. Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China Division of Life Science Hong Kong University of Science and Technology Hong Kong China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Hong Kong China. Electronic address: jhc@cse.ust.hk.

dataset bias in images is an important yet less explored topic in medical images. Deep learning could be prone to learning spurious correlation raised by dataset bias, resulting in inaccurate, unreliable, and unfair models, which impedes its adoption in real-world clinical applications. Despite its significance, there is a dearth of research in the medical image classification domain to address dataset bias. Furthermore, the bias labels are often agnostic, as identifying biases can be laborious and depend on post-hoc interpretation. This paper proposes learning Adaptive Agreement from a Biased Council (Ada-ABC), a debiasing framework that does not rely on explicit bias labels to tackle dataset bias in medical images. Ada-ABC develops a biased council consisting of multiple classifiers optimized with generalized cross entropy loss to learn the dataset bias. A debiasing model is then simultaneously trained under the guidance of the biased council. Specifically, the debiasing model is required to learn adaptive agreement with the biased council by agreeing on the correctly predicted samples and disagreeing on the wrongly predicted samples by the biased council. In this way, the debiasing model could learn the target attribute on the samples without spurious correlations while also avoiding ignoring the rich information in samples with spurious correlations. We theoretically demonstrated that the debiasing model could learn the target features when the biased model successfully captures dataset bias. Moreover, we constructed the first medical debiasing benchmark focusing on addressing spurious correlation from four datasets containing seven different bias scenarios. Our extensive experiments practically showed that our proposed Ada-ABC outperformed competitive approaches, verifying its effectiveness in mitigating dataset bias for medical image classification. The codes and organized benchmark datasets can be accessed via https://***/LLYXC/Ada-ABC.

关键词： dataset bias Shortcut learning Trustworthy artificial intelligence

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data gaps and opportunities for modeling cancer health equity

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Journal of the National Cancer Institute - Monographs 2023年第62期2023卷 246-254页

作者： Trentham-Dietz, Amy Corley, Douglas A. Del Vecchio, Natalie J. Greenlee, Robert T. Haas, Jennifer S. Hubbard, Rebecca A. Hughes, Amy E. Kim, Jane J. Kobrin, Sarah Li, Christopher I. Meza, Rafael Neslund-Dudas, Christine M. Tiro, Jasmin A. Department of Population Health Sciences and Carbone Cancer Center School of Medicine and Public Health University of Wisconsin-Madison Madison WI United States Division of Research Kaiser Permanente Northern California Oakland CA United States Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle WA United States Marshfield Clinic Research Institute Marshfield WI United States Division of General Internal Medicine Massachusetts General Hospital Boston MA United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Population and Data Sciences University of Texas Southwestern Medical Center Dallas TX United States Department of Health Policy and Management Center for Health Decision Science Harvard T.H. Chan School of Public Health Boston MA United States Healthcare Delivery Research Program Division of Cancer Control & Population Sciences National Cancer Institute National Institutes of Health Rockville MD United States Department of Integrative Oncology British Columbia (BC) Cancer Research Institute Vancouver BC Canada Department of Public Health Sciences and Henry Ford Cancer Henry Ford Health Detroit MI United States Department of Public Health Sciences University of Chicago Biological Sciences Division University of Chicago Medicine Comprehensive Cancer Center Chicago IL United States

Population models of cancer reflect the overall US population by drawing on numerous existing data resources for parameter inputs and calibration targets. Models require data inputs that are appropriately representative, collected in a harmonized manner, have minimal missing or inaccurate values, and reflect adequate sample sizes. data resource priorities for population modeling to support cancer health equity include increasing the availability of data that 1) arise from uninsured and underinsured individuals and those traditionally not included in health-care delivery studies, 2) reflect relevant exposures for groups historically and intentionally excluded across the full cancer control continuum, 3) disaggregate categories (race, ethnicity, socioeconomic status, gender, sexual orientation, etc.) and their intersections that conceal important variation in health outcomes, 4) identify specific populations of interest in clinical databases whose health outcomes have been understudied, 5) enhance health records through expanded data elements and linkage with other data types (eg, patient surveys, provider and/or facility level information, neighborhood data), 6) decrease missing and misclassified data from historically underrecognized populations, and 7) capture potential measures or effects of systemic racism and corresponding intervenable targets for change. © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: ***@***.

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