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Single-cell technologies: current and near future

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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Membership Inference Attacks Against Semantic Segmentation Models

arXiv

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arXiv 2022年

作者： Chobola, Tomas Usynin, Dmitrii Kaissis, Georgios Department of Informatics Technical University of Munich Munich Germany Artificial Intelligence in Medicine and Healthcare Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Institute for Machine Learning in Biomedical Imaging Helmholtz Zentrum München Munich Germany

Membership inference attacks aim to infer whether a data record has been used to train a target model by observing its predictions. In sensitive domains such as healthcare, this can constitute a severe privacy violation. In this work we attempt to address the existing knowledge gap by conducting an exhaustive study of membership inference attacks and defences in the domain of semantic image segmentation. Our findings indicate that for certain threat models, these learning settings can be considerably more vulnerable than the previously considered classification settings. We additionally investigate a threat model where a dishonest adversary can perform model poisoning to aid their inference and evaluate the effects that these adaptations have on the success of membership inference attacks. We quantitatively evaluate the attacks on a number of popular model architectures across a variety of semantic segmentation tasks, demonstrating that membership inference attacks in this domain can achieve a high success rate and defending against them may result in unfavourable privacy-utility trade-offs or increased computational costs. © 2022, CC BY.

关键词： Semantics

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From reflection to action: Combining machine learning with expert knowledge for nutrition goal recommendations 21

From reflection to action: Combining machine learning with e...

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2021 CHI Conference on Human Factors in computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Mitchell, Elliot G. Heitkemper, Elizabeth M. Burgermaster, Marissa Levine, Matthew E. Miao, Yishen Hwang, Maria L. Desai, Pooja M. Cassells, Andrea Tobin, Jonathan N. Tabak, Esteban G. Albers, David J. Smaldone, Arlene M. Mamykina, Lena Department of Biomedical Informatics Columbia University United States School of Nursing The University of Texas at Austin United States Department of Population Health Dell Medical School and Department of Nutritional Sciences The University of Texas at Austin United States Department of Computing and Mathematical Sciences California Institute of Technology United States Department of Molecular Cellular and Developmental Biology University of California Santa Barbara United States Department of Science and Math Fashion Institute of Technology United States United States and The Rockefeller University United States Courant Institute of Mathematical Sciences United States University of Colorado Anschutz Medical Campus Section of Informatics and Data Science United States Departments of Pediatrics Biomedical Engineering and Biostatistics and Informatics United States School of Nursing Columbia University United States

ISBN: (纸本)9781450380966

Self-tracking can help personalize self-management interventionsfor chronic conditions like type 2 diabetes (T2D), but refecting onpersonal data requires motivation and literacy. Machine learning(ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health *** introduce GlucoGoalie, which combines ML with an expert system to trans-late ML output into personalized nutrition goal suggestions forindividuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and action-able. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identifed tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that ofer more interactivity, feedback, and negotiation. © 2021 ACM.

关键词： Feedback

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Guidelines for reporting cell types: The MIRACL standard

arXiv

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arXiv 2022年

作者： Lubiana, Tiago Roncaglia, Paola Mungall, Christopher J. Quardokus, Ellen M. Fortriede, Joshua D. Osumi-Sutherland, David Diehl, Alexander D. School of Pharmaceutical Sciences University of São Paulo SP São Paulo Brazil Ronin Institute United States Wellcome Genome Campus Hinxton Cambridge CB10 1SD United Kingdom Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH45224 United States Department of Biomedical Informatics University at Buffalo Jacobs School of Medicine and Biomedical Sciences BuffaloNY14203 United States

Cell types are at the root of modern biology, and describing them is a core task of the Human Cell Atlas project. Surprisingly, there are no standards for reporting new cell types, leading to a gap between classes mentioned in biomedical literature and the Cell Ontology, the primary registry of cell types. Here we introduce the Minimal Information Reporting About a CelL (MIRACL) standard, a guideline for describing cell types alongside scientific articles. In a MIRACL sheet, authors organize a label, a diagnostic description, a taxon, an anatomical structure, and a parent cell class for each cell type of interest. The MIRACL standard bridges the gap between wet-lab researchers and ontologists, facilitating the integration of biomedical knowledge into ontologies and artificial intelligence systems. © 2022, CC BY.

关键词： Cells

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A Multimodal Monitoring Approach to Predicting the Onset of Physiological Incidents Using Machine Learning

A Multimodal Monitoring Approach to Predicting the Onset of ...

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IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： E. Moyer I. Isozaki D. Moberg Moberg Analytics Ambler Pennsylvania USA School of Biomedical Engineering Science and Health Systems Drexel University Philadelphia Pennsylvania USA College of Computing and Informatics Drexel University Philadelphia Pennsylvania USA

ISBN: (纸本)9781665428989

Traumatic Brain Injury (TBI) is a complex, heterogeneous disease affecting millions of people in the U.S. each year [1]. Multimodal monitoring (MMM) is a relatively new attempt to access and monitor the brain post injury by incorporating multiple sources of information recorded in the intensive care unit (ICU) [2]. As a result, MMM has led to the creation of exhaustive datasets, allowing for the integration of many different signals and modalities pertaining to the management of severe TBI [2]–[4]. One of the most pressing questions about a TBI patient is whether an incident, or an event, will occur in the near future [5]. Additionally, clinicians require sufficient time before the onset of an event to administer a drug to the patient to avoid the oncoming event. Therefore, the aim of this work is to detect whether physiological events will occur in the future using the multimodal physiological data as features. In this study, we leverage both the breath and resolution of physiological data to predict the onset of physiological incidents 30 minutes before their onset. Specifically, we focus on three physiological events, namely high intercranial pressure (ICP), out of range diastolic arterial blood pressure (ABP), and out of range systolic ABP, using seven relevant modalities.

关键词： Drugs Pressing Machine learning Signal processing Feature extraction Biology biomedical monitoring

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Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia

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Computers in Biology and Medicine 2024年 178卷 108735-108735页

作者： Kosvyra, Α. Karadimitris, Α. Papaioannou, Μ. Chouvarda, I. Laboratory of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Centre for Haematology and Hugh and Josseline Langmuir Centre for Myeloma Research Department of Immunology and Inflammation Imperial College London Department of Haematology Hammersmith Hospital Imperial College Healthcare NHS Trust Du Cane Road LondonW12 0NN United Kingdom Hematology Unit 1st Dept of Internal Medicine AHEPA Hospital School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece

Background: Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction. Method: This study aims to enhance prognostic capabilities in AML by integrating multi-omics data, especially gene expression and methylation, through network-based feature selection methodologies. By employing artificial intelligence and network analysis, we are exploring different methods to build a machine learning model for predicting AML patient survival. We evaluate the effectiveness of combining omics data, identify the most informative method for network integration and compare the performance with standard feature selection methods. Results: Our findings demonstrate that integrating gene expression and methylation data significantly improves prediction accuracy compared to single omics data. Among network integration methods, our study identifies the best approach that improves informative feature selection for predicting patient outcomes in AML. Comparative analyses demonstrate the superior performance of the proposed network-based methods over standard techniques. Conclusions: This research presents an innovative and robust methodology for building a survival prediction model tailored to AML patients. By leveraging multilayer network analysis for feature selection, our approach contributes to improving the understanding and prognostic capabilities in AML and laying the foundation for more effective personalized therapeutic interventions in the future. © 2024 Elsevier Ltd

关键词： Gene expression

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State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

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JMIR Medical informatics 2022年第8期10卷 e38454页

作者： Petmezas, Georgios Stefanopoulos, Leandros Kilintzis, Vassilis Tzavelis, Andreas Rogers, John A. Katsaggelos, Aggelos K. Maglaveras, Nicos Lab of Computing Medical Informatics and Biomedical-Imaging Technologies The Medical School Aristotle University of Thessaloniki Thessaloniki Greece Department of Biomedical Engineering Northwestern University Evanston IL United States Department of Material Science Northwestern University Evanston IL United States Department of Electrical and Computer Engineering Northwestern University Evanston IL United States

Background: Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective: This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods: The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results: We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions: We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making. ©Georgios Petmezas, Leandros Stefanopoulos, Vassilis Kilintzis, Andreas Tzavelis, John A Rogers

关键词： clinical decision CNN convolutional neural networks decision support deep learning diagnostic tools ECG ECG databases electrocardiogram long short-term memory LSTM residual neural network ResNet

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SeqGO-CPA: Improving Compound-Protein Binding Affinity Prediction with Sequence Information and Gene Ontology Knowledge

SeqGO-CPA: Improving Compound-Protein Binding Affinity Predi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chunyu Wang Yan Zhu Naifeng Wen Lingling Zhao Junjie Wang Faculty of Computing Harbin Institute of Technology Harbin China School of Mechanical and Electrical Engineering Dalian Minzu University Dalian China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

ISBN: (纸本)9781665429825

The compound-protein binding affinity (CPA) pre-diction is vital for drug discovery and drug repurposing. Deep learning methods have been developed to model the complicated relationship between CPA and the sequences or structures of proteins and molecules. This study proposes a novel deep learning method, SeqGO-CPA, integrating protein function knowledge represented by Gene Ontology (GO) annotations in the CPA prediction. To capture the semantic information of GO annotations, a fine-tuned natural language processing model for biomedical domains is utilized to encode the set of GO terms. Meanwhile, based on the observation that CPA often occurs in sub-structures, our method uses the tokenization algorithm to learn sub-structure information of proteins and compounds from a large number of unlabeled sequences. Further, a deep neural network architecture involving the jointly-feature representation and a highway block is developed to enhance the CPA prediction ability. The proposed model was evaluated on two public benchmark datasets in both standard cross-validation and blinding split settings. The experimental results demonstrate our method outperforms the deep learning-based baselines, meanwhile the incorporating of GO information further improves the prediction performance.

关键词： Proteins Deep learning Drugs Annotations Biological system modeling Ontologies Prediction algorithms

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From Hospital to Portables: A Universal ECG Foundation Model Built on 10+ Million Diverse Recordings

arXiv

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arXiv 2024年

作者： Li, Jun Aguirre, Aaron Moura, Junior Liu, Che Zhong, Lanhai Sun, Chenxi Clifford, Gari Westover, Brandon Hong, Shenda National Institute of Health Data Science Peking University Beijing China Department of Cardiology Massachusetts General Hospital BostonMA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics School of Medicine Emory University AtlantaGA United States Harvard Medical School BostonMA United States Department of Neurology Beth Israel Deaconess Medical Center BostonMA United States Department of Computing Data Science Institute Imperial College London London United Kingdom Zhongshan school of medical Sun Yat-sen University Guangzhou China

Artificial Intelligence (AI) has shown great promise in electrocardiogram (ECG) analysis and cardiovascular disease detection. However, developing a general AI-ECG model has been challenging due to inter-individual variability and the diversity of ECG diagnoses, limiting existing models to specific diagnostic tasks and datasets. Moreover, current AI-ECG models struggle to achieve comparable performance between single-lead and 12-lead ECGs, limiting the application of AI-ECG to portable and wearable ECG devices. To address these limitations, we introduce an ECG Foundation Model (ECGFounder), a general-purpose model that leverages real-world ECG annotations from cardiology experts to broaden the diagnostic capabilities of ECG analysis. ECGFounder is trained on over 10 million ECGs with 150 label categories from the Harvard- Emory ECG Database, enabling comprehensive cardiovascular disease diagnosis through ECG analysis. The model is designed to be both effective out-of-the-box and fine-tunable for downstream tasks, maximizing usability. More importantly, we extend its application to single-lead ECGs, enabling complex condition diagnoses and supporting various downstream tasks in mobile and remote monitoring scenarios. Experimental results demonstrate that ECGFounder achieves expert-level performance on internal validation sets for both 12-lead and single-lead ECGs, while also exhibiting strong classification performance and generalization across various diagnoses on external validation sets. When fine-tuned, ECGFounder outperforms baseline models in demographics detection, clinical event detection, and cross-modality cardiac rhythm diagnosis. ECGFounder not only enhances existing cardiovascular diagnostics capabilities but also facilitates integration into mobile and remote monitoring devices, enabling continuous, real-time cardiac health monitoring crucial for early detection and management of chronic heart conditions. The trained model and data will be publicly rele

关键词： Remote patient monitoring

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Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

arXiv

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arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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