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Myocardial Iron Overload Assessment with Automatic Segmentation of Cardiac MR Images based on Deep Neural Networks

Iranian Journal of medical Physics 2025年第1期22卷 6-13页

作者： Bakhshali, Mohamad Amin Gholizadeh, Maryam Layegh, Parvaneh Eslami, Saeid Department of Medical Informatics School of Medicine Mashhad University of Medical Sciences Mashhad Iran Institute of Bioinformatics University Medicine Greifswald Greifswald Germany Department of Radiology Imam Reza Hospital School of Medicine Mashhad University of Medical Sciences Mashhad Iran

Introduction: Heart failure due to myocardial iron overload is one of the main causes of death in thalassemia major (TM) patients. Therefore, cardiac magnetic resonance (CMR) imaging method with a multi-echo sequence can be used to assess the iron overload of TM patients. This study aimed to evaluate the myocardial iron overload in TM patients with automatic left ventricular (LV) segmentation of CMR images. Material and Methods: Thirty-six TM patients were selected to acquire CMR images and clinical data. Automatic LV segmentation was implemented with U-Net, an automatically adapted deep convolutional neural network based on U-Net. With the signal intensity of the LV segmented area, T2* value can be calculated at different echo times, a widely used and approved method to assess myocardial iron overload. Results: The accuracy of LV segmentation as measured by intersection-over-union (0.95) was substantially higher than non-deep learning based methods and at par with other deep learning based methods like. In addition, our results indicate that the proposed method outperformed in assessing LV iron overload over other deep learning based methods in terms of negative predictive value, positive predictive value, and Jaccard. Conclusion: Relying on these outcomes, the proposed method as a deep learning based model yields better LV segmentation and notably impacts assessing myocardial iron overload. © (2025), (Mashhad University of medical Sciences). All rights reserved.

关键词： Image segmentation

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Dynamic k-Anonymity for Electronic Health Records: A Topological Framework 19th

Dynamic k-Anonymity for Electronic Health Records: A Topolo...

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19th International Workshop on Data Privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024

作者： Swaminathan, Arjhun Akgün, Mete Medical Data Privacy and Privacy Preserving Machine Learning Department of Computer Science University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen Germany

ISBN: (纸本)9783031823480

With the rapid digitization of Electronic Health Records (EHRs), fast and adaptive data anonymization methods have become increasingly important. While tools from topological data analysis (TDA) have been proposed to anonymize static datasets—allowing the creation of multiple generalizations for different anonymization needs from a single computation—the application to dynamic datasets remains unexplored. To address this, our work adapts existing methodologies to the dynamic setting. We develop an improved version of weighted persistence barcodes that track higher-dimensional holes in data, allowing us to edit persistence information on the fly. Additionally, we introduce filtration trimming, a novel technique designed to update persistence data quickly with minimal computing effort when data is added. Our work represents a significant advancement in healthcare data privacy, offering a refined approach to protecting highly sensitive and evolving patient data through dynamic k-anonymity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Graph based recurrent network for context specific synthetic lethality prediction

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Science China(Life Sciences) 2025年第2期68卷 527-540页

作者： Yuyang Jiang Jing Wang Yixin Zhang ZhiWei Cao Qinglong Zhang Jinsong Su Song He Xiaochen Bo Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjin 300072China Department of Bioinformatics Institute of Health Service and Transfusion MedicineBeijing 100850China School of Medicine Tsinghua UniversityBeijing 100084China School of Informatics Xiamen UniversityXiamen 361005China

The concept of synthetic lethality(SL)has been successfully used for targeted *** further explore SL for cancer therapy,identifying more SL interactions with therapeutic potential are ***,graph neural network-based deep learning methods have been proposed for SL prediction,which reduce the SL search space of wet-lab based ***,these methods ignore that most SL interactions depend strongly on genetic context,which limits the application of the predicted *** this study,we proposed a graph recurrent network-based model for specific context-dependent SL prediction(SLGRN).In particular,we introduced a Graph Recurrent Network-based encoder to acquire a context-specific,low-dimensional feature representation for each node,facilitating the prediction of novel *** leveraged gate recurrent unit(GRU)and it incorporated a context-dependent-level state to effectively integrate information from all *** a result,SLGRN outperforms the state-of-the-arts models for SL *** subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival *** in vitro experiments and retrospective clinical analysis,we emphasize the potential clinical significance of this context-specific SL prediction model.

关键词： synthetic lethality graph recurrent network context-specific graph combination therapy

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Scanpath Classification with an n-mer Deep Neural Network Architecture 25

Scanpath Classification with an n-mer Deep Neural Network Ar...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Wolfgang Fuhl Susanne Zabel Kay Nieselt Eberhard Karls Universität Tübingen Institute for Bioinformatics and Medical Informatics Tübingen Germany Eberhard Karls Universität Tübingen Institute for Bioinformatics and Medical Informatics Tuebingen Germany

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Reanalysis of whole genome sequencing ends a diagnostic odyssey of neurodevelopmental disorders caused by RNU4-2 variants

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Science China(Life Sciences) 2025年第4期68卷 1194-1196页

作者： Shiqi Fan Shuanghao Yang Miao Sun Weiyue Gu Xue Zhang State Key Laboratory for Complex Severe and Rare Diseases Peking Union Medical College Hospital&Institute of Basic Medical SciencesChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing100005China McKusick-Zhang Center for Genetic Medicine Institute of Basic Medical SciencesChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing1000005China Hubei Key Laboratory of Agricultural Bioinformatics College of InformaticsHuazhong Agricultural UniversityWuhan430070China Chigene(Beijing)Translational Medical Research Center Co.Ltd. Beijing100176China

Dear Editor,Neurodevelopmental disorders(NDD)are a group of diseases with high phenotypic heterogeneity characterized by inability in cognition,communication,psychological skills,and motor *** common types of NDDs include autism spectrum disorder(ASD),attention-deficit/hyperactivity disorder(ADHD),epilepsy,schizophrenia,etc.(Parenti et al.,2020).

关键词： diagnostic odyssey autism spectrum disorder asd attention deficit hyperactivity reanalysis rnu variants whole genome sequencing neurodevelopmental disorders

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The SPHN Schema Forge – transform healthcare semantics from human-readable to machine-readable by leveraging semantic web technologies

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Journal of Biomedical Semantics 2025年第1期16卷 1-16页

作者： Touré, Vasundra Unni, Deepak Krauss, Philip Abdelwahed, Abdelhamid Buchhorn, Jascha Hinderling, Leon Geiger, Thomas R. Österle, Sabine Personalized Health Informatics SIB Swiss Institute of Bioinformatics Basel 4051 Switzerland Accenture Basel 4051 Switzerland Swiss Academy of Medical Sciences Bern 3001 Switzerland Independent researcher Basel Switzerland

Background: The Swiss Personalized Health Network (SPHN) adopted the Resource Description Framework (RDF), a core component of the Semantic Web technology stack, for the formal encoding and exchange of healthcare data in a medical knowledge graph. The SPHN RDF Schema defines the semantics on how data elements should be represented. While RDF is proven to be machine readable and interpretable, it can be challenging for individuals without specialized background to read and understand the knowledge represented in RDF. For this reason, the semantics described in the SPHN RDF Schema are primarily defined in a user-accessible tabular format, the SPHN Dataset, before being translated into its RDF representation. However, this translation process was previously manual, time-consuming and labor-intensive. Result: To automate and streamline the translation from tabular to RDF representation, the SPHN Schema Forge web service was developed. With a few clicks, this tool automatically converts an SPHN-compliant Dataset spreadsheet into an RDF schema. Additionally, it generates SHACL rules for data validation, an HTML visualization of the schema and SPARQL queries for basic data analysis. Conclusion: The SPHN Schema Forge significantly reduces the manual effort and time required for schema generation, enabling researchers to focus on more meaningful tasks such as data interpretation and analysis within the SPHN framework. © The Author(s) 2025.

关键词： Conversion FAIR Healthcare RDF Schema Semantic Web Semantics Spreadsheet Web Service

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From Polar Bears to People: The Role of Ethnic Genetic Variation in Thermoregulation and Heat-Related Health Risk

From Polar Bears to People: The Role of Ethnic Genetic Varia...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Baumann, Alexandra Thiel, Jakob Haffer, Nina Gupta, Shailendra Wolfien, Markus Department of Systems Biology and Bioinformatics University of Rostock Rostock Germany Institute for Medical Informatics and Biometry Faculty of Medicine and University Hospital Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Berlin Institute of Health at Charite- Universitatsmedizin Berlin Germany Charite- University Medicine Berlin Germany Dresden/Leipzig Dresden Germany

As climate change increases the frequency and severity of acute heat events, it is crucial to determine fac tors for appropriate healthcare strategies and predictive models. Previously, it was stated that socioeconomic factors primarily play a role in heat-related illness risk. Analogous to the polar bear’s unique adaptations to the cold, humans exhibit distinct genetic traits shaped by their migration to diverse climates. This posi tion paper hypothesizes that genetic differences among human ethnic groups, in addition to socioeconomic and other factors, also contribute to variations in thermoregulation and influence susceptibility to heat-related diseases. To understand genetic adaptations across human ethnicities (initially European and African), we propose a genetic association analysis of single nucleotide polymorphisms (SNPs) in genes associated with thermoregulation. An assessment of changes in thermoregulation gene regulation networks will be possible by conducting a functional pathway analysis. Expected outcomes include identifying differences in SNP dis tributions of thermoregulation-associated genes across ethnicities. Challenges such as the underrepresentation of African populations in genomic databases must also be addressed. This research aims to provide a foun dational understanding of genetic contributions to heat adaptation, guiding the development of personalized, equitable healthcare responses to climate-induced heat stress. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Diseases

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Single-cell technologies: current and near future

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Science China(Life Sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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The Effect of Multi-Task Learning on the Prediction of Neoantigen–MHC Class II Binding

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and bioinformatics 2025年第2期22卷 570-580页

作者： Kazuhiro Ikkyu Itoshi Nikaido Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Life Science Innovation (Bioinformatics) University of Tsukuba Tsukuba Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Saitama Japan Department of Functional Genome Informatics Division of Biological Data Science Medical Research Laboratory (MRL) Institute of Integrated Research (IIR) Institute of Science Tokyo Bunkyo Japan

Neoepitopes are significant therapeutic cancer vaccine candidates, given that tumor neoepitopes induce an immune response to eliminate cancer cells. This immune activation depends on the binding affinity between the antigen peptide and the major histocompatibility complex (MHC). The epitope–MHC binding assay is a technologically difficult, time-consuming, and expensive technique. Therefore, prediction methods for these binding affinities have been developed using computational prediction approaches. However, these predictive models are trained on datasets biased toward viral peptides and some MHC alleles and are limited in their prediction of neoepitopes. In particular, because of the wide variety of MHC class II binding formats, the performance of MHC class II prediction must be improved. Here, we propose a novel deep learning model that consists of multi-task bidirectional long short-term memory (Bi-LSTM) models. Our multi-task model can predict neoepitope–MHC class II bindings from limited training data by sharing MHC class I and II training parameters. We confirm that multi-task learning significantly enhances the prediction performances of cancer antigens. Our model achieves an area under the receiver operating characteristic curve (AUC-ROC) of 82.2%, outperforming existing state-of-the-art single allele neoantigen prediction models while maintaining its strong generalization performance.

关键词： Peptides Predictive models Multitasking Antigens Cancer Data models Training Computer architecture Training data Immune system

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A novel mathematical framework for pedigree-based calculation of Y-STR match probabilities

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Scientific Reports 2025年第1期15卷 1-12页

作者： Caliebe, Amke Zandstra, Dion Ralf, Arwin Kayser, Manfred Krawczak, Michael Institute of Medical Informatics and Statistics Kiel University University Hospital Schleswig-Holstein Brunswiker Straße 10 Kiel 24105 Germany Department of Genetic Identification Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Department of Pathology and Clinical Bioinformatics Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands

Y-chromosomal short tandem repeat (Y-STR) markers are routinely used in forensic casework to identify male donors of biological traces left at crime scenes, particularly in sexual assault cases. However, the evidential value of a match between the Y-STR profile of a trace and a potential donor, usually a crime suspect, is difficult to quantify, and the common albeit inappropriate practise to equate Y-STR match probabilities with Y-STR profile frequencies estimated from population databases has been subject to scientific debate for decades. As a solution to this long-standing problem, we suggest an alternative approach to the calculation of Y-STR match probabilities that involves splitting the group of potential donors other than the suspect into two: (i) his close male relatives (termed his ‘pedigree’) and (ii) all other males. While an upper limit to the match probability is easily calculated for the second group, it is computationally challenging to derive for the first. We therefore developed a mathematical framework that uses importance sampling to reconstruct and evaluate the Y-STR profiles of untyped members of the suspect’s pedigree by way of simulation. Extensive testing with elementary pedigrees of different structure and complexity confirmed that both, the framework and its Python-based software implementation yield match probability estimates that approximate well the correct analytical results, depending upon the number of simulations performed. Our methodology thus facilitates a more appropriate and valid solution to the long-standing problem of interpreting Y-STR profile matches in forensic casework. © The Author(s) 2025.

关键词： Biological trace DNA profile Forensic genetics Importance sampling Sexual assault Simulation Suspect population Y-STR

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