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Research Square 2021年

作者： Jeon, ByoungJun Jung, Hyo Gi Lee, Sang Won Lee, Gyudo Shim, Jung Hee Kim, Mi Ok Kim, Byung Jun Kim, Sang-Hyon Lee, Hyungbeen Lee, Sang Woo Yoon, Dae Sung Jo, Seong Jin Choi, Tae Hyun Lee, Wonseok Interdisciplinary Program for Bioengineering Seoul National University Seoul08826 Korea Republic of School of Biomedical Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of Department of Plastic and Reconstructive Surgery Research Services Seoul National University Bundang Hospital Seongnam13620 Korea Republic of Department of Plastic and Reconstructive Surgery Institute of Human Environment Interface Biology Seoul National University College of Medicine Seoul03087 Korea Republic of Department of Internal Medicine Keimyung University Dongsan Medical Center Daegu41931 Korea Republic of Department of Biomedical Engineering Yonsei University Wonju26493 Korea Republic of R&D Center of Curigin Ltd. Seoul04778 Korea Republic of Department of Dermatology Seoul National University College of Medicine Seoul03087 Korea Republic of Department of Electrical Engineering Korea National University of Transportation Chungju27469 Korea Republic of

Melanoma is visible unlike other types of cancer, but it is still challenging to diagnose correctly because of the difficulty in distinguishing between benign nevus and melanoma. We conducted a robust investigation of melanoma, identifying considerable differences in local elastic properties between nevus and melanoma tissues by using atomic force microscopy (AFM) indentation of histological specimens. Specifically, the histograms of the elasticity of melanoma displayed multimodal Gaussian distributions, exhibiting the heterogeneous mechanical properties, in contrast with the unimodal distributions of elasticity in the benign nevus. We identified this notable signature was consistent regardless of blotch incidence by sex, age, anatomical site (e.g., thigh, calf, arm, eyelid, and cheek), or cancer stage (I, IV, and V). In addition, we found that the non-linearity of the force-distance curves for melanoma is increased compared to benign nevus. We believe that AFM indentation of histological specimens may technically complement conventional histopathological analysis for earlier and more precise melanoma detection. © 2021, CC BY.

关键词： Elasticity

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Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text

arXiv

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arXiv 2023年

作者： Rehana, Hasin Çam, Nur Bengisu Basmaci, Mert Zheng, Jie Jemiyo, Christianah He, Yongqun Özgür, Arzucan Hur, Junguk Computer Science Graduate Program University of North Dakota Grand ForksND58202 United States Department of Computer Engineering Bogazici University Istanbul34342 Turkey Unit for Laboratory Animal Medicine Department of Microbiology and Immunology University of Michigan Ann ArborMI48109 United States Department of Biomedical Sciences University of North Dakota School of Medicine and Health Sciences Grand ForksND58202 United States Center for Computational Medicine and Bioinformatics University of Michigan Ann ArborMI48109 United States

Detecting protein-protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. However, with the fast-paced growth of biomedical literature, there is a growing need for automated and accurate extraction of PPIs to facilitate scientific knowledge discovery. Pre-trained language models, such as generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT), have shown promising results in natural language processing (NLP) tasks. We evaluated the performance of PPI identification of multiple GPT and BERT models using three manually curated gold-standard corpora: Learning Language in Logic (LLL) with 164 PPIs in 77 sentences, Human Protein Reference Database with 163 PPIs in 145 sentences, and Interaction Extraction Performance Assessment with 335 PPIs in 486 sentences. BERT-based models achieved the best overall performance, with BioBERT achieving the highest recall (91.95%) and F1-score (86.84%) and PubMedBERT achieving the highest precision (85.25%). Interestingly, despite not being explicitly trained for biomedical texts, GPT-4 achieved commendable performance, comparable to the top-performing BERT models. It achieved a precision of 88.37%, a recall of 85.14%, and an F1-score of 86.49% on the LLL dataset. These results suggest that GPT models can effectively detect PPIs from text data, offering promising avenues for application in biomedical literature mining. Further research could explore how these models might be fine-tuned for even more specialized tasks within the biomedical domain. © 2023, CC BY.

关键词： Extraction

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Ten simple rules for reproducible research in jupyter notebooks

arXiv

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arXiv 2018年

作者： Rule, Adam Birmingham, Amanda Zuniga, Cristal Altintas, Ilkay Huang, Shih-Cheng Knight, Rob Moshiri, Niema Nguyen, Mai H. Rosenthal, Sara Brin Pérez, Fernando Rose, Peter W. Design Lab UC San Diego San DiegoCA United States Center for Computational Biology and Bioinformatics UC San Diego San DiegoCA United States Department of Pediatrics UC San Diego San DiegoCA United States Data Science Hub San Diego Supercomputer Center UC San Diego San DiegoCA United States Departments of Bioengineering and Computer Science and Engineering Center for Microbiome Innovation UC San Diego San DiegoCA United States Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA United States Department of Statistics Berkeley Institute for Data Science UC Berkeley Lawrence Berkeley National Laboratory BerkeleyCA United States Biomedical Informatics Graduate Program Stanford University StanfordCA United States

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage

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Digital Imaging in Pathology: Whole-Slide Imaging and Beyond

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Pathology: Mechanisms of Disease 1000年第1期8卷 331-359页

作者： Farzad Ghaznavi Andrew Evans Anant Madabhushi Michael Feldman 1Department of Pathology and Laboratory Medicine University of Toronto Toronto Ontario M5S 2S2 Canada email: farzad.ghaznavi@mail.utoronto.ca 2Laboratory Medicine Program University Health Network Toronto General Hospital Toronto Ontario M5G 2M9 Canada 3Department of Biomedical Engineering Laboratory for Computational Imaging and Bioinformatics Rutgers University Piscataway New Jersey 08854 4Department of Pathology and Laboratory Medicine University of Pennsylvania School of Medicine Philadelphia Pennsylvania 19104 email: feldmanm@mail.med.upenn.edu

Digital imaging in pathology has undergone an exponential period of growth and expansion catalyzed by changes in imaging hardware and gains in computational processing. Today, digitization of entire glass slides at near the optical resolution limits of light can occur in 60 s. Whole slides can be imaged in fluorescence or by use of multispectral imaging systems. Computational algorithms have been developed for cytometric analysis of cells and proteins in subcellular locations by use of multiplexed antibody staining protocols. Digital imaging is unlocking the potential to integrate primary image features into high-dimensional genomic assays by moving microscopic analysis into the digital age. This review highlights the emerging field of digital pathology and explores the methods and analytic approaches being developed for the application and use of these methods in clinical care and research settings.

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Housing and husbandry factors affecting zebrafish novel tank test responses: a global multi-laboratory study

Lab animal

引用

Lab animal 2025年第6期54卷 2025 May 26页

作者： Courtney Hillman Barbara D Fontana Tamara G Amstislavskaya Maria A Gorbunova Stefani Altenhofen Karissa Barthelson Leonardo M Bastos João V Borba Carla D Bonan Caroline H Brennan Amaury Farías-Cea Austin Cooper Jamie Corcoran Eduardo R Dondossola Luis M Martinez-Duran Matheus Gallas-Lopes David S Galstyan Ella O Garcia Ewan Gerken Robert Hindges Justin W Kenney Maxim A Kleshchev Tatiana O Kolesnikova Adele Leggieri Sergey L Khatsko Michael Lardelli Guilherme Lodetti Giulia Lombardelli Ana C Luchiari Stefani M Portela Violeta Medan Lirane M Moutinho Evgeny V Nekhoroshev Barbara D Petersen Maureen L Petrunich-Rutherford Angelo Piato Maurizio Porfiri Emily Read Cássio M Resmim Eduardo P Rico Denis B Rosemberg Murilo S de Abreu Catia A Salazar Thailana Stahlhofer-Buss Júlia R Teixeira Ana M Valentim Alexander V Zhdanov Patricio Iturriaga-Vásquez Xian Wang Ryan Y Wong Allan V Kalueff Matthew O Parker Surrey Sleep Research Centre Department of Clinical and Experimental Medicine School of Biosciences University of Surrey Surrey UK. Laboratory of Experimental Neuropsychobiology Department of Biochemistry and Molecular Biology Natural and Exact Sciences Center Federal University of Santa Maria Santa Maria Brazil. Department of Biological Sciences Wayne State University Detroit MI USA. Laboratory of Experimental Models of Neuropsychiatric Disorders Scientific Research Institute of Neurosciences and Medicine Novosibirsk State University Novosibirsk Russia. Ural Federal University Yekaterinburg Russia. Laboratório de Neuroquímica e Psicofarmacologia Escola de Ciências da Saúde e da Vida Pontifícia Universidade Católica do Rio Grande do Sul Porto Alegre Brazil. Alzheimer's Disease Genetics Laboratory School of Biological Sciences Faculty of Sciences Engineering and Technology The University of Adelaide Adelaide South Australia Australia. Childhood Dementia Research Group College of Medicine and Public Health Flinders Health and Medical Research Institute Flinders University Adelaide South Australia Australia. Laboratório de Psicofarmacologia e Comportamento Departamento de Farmacologia Instituto de Ciências Básicas da Saúde Universidade Federal do Rio Grande do Sul Porto Alegre Brazil. School of Biological and Behavioural Sciences Queen Mary University of London London UK. Molecular Pharmacology and Medicinal Chemistry Lab Facultad de Ingeniería y Ciencias Universidad de la Frontera Temuco Chile. Department of Psychology University of Nebraska at Omaha Omaha NE USA. Translational Psychiatry Laboratory Graduate Program in Health Sciences University of Southern Santa Catarina Criciúma Brazil. St. Petersburg State University St. Petersburg Russia. Department of Psychology Indiana University Northwest Gary IN USA. Centre for Developmental Neurobiology and MRC Centre for Neurodevelopmental Disorders King's College London London UK. Neurobiology Program Sir

The reproducibility crisis in bioscience, characterized by inconsistent study results, impedes our understanding of biological processes. Global collaborative studies offer a unique solution to this problem. Here, we present a global collaboration using the zebrafish (Danio rerio) novel tank test, a popular behavioral assay for anxiety-like responses. We analyzed data from 20 laboratories worldwide, focusing on housing conditions and experimental setups. Our study included 488 adult zebrafish, tested for 5 min, focusing on a variety of variables. Key findings show that female zebrafish exhibit more anxiety-like behavior than males, highlighting sex as a critical variable. Housing conditions, including higher stocking densities and specific feed types, also influenced anxiety levels. Optimal conditions (5 fish/L) and nutritionally rich feeds (for example, rotifers) mitigated anxiety-like behaviors. Environmental stressors, such as noise and transportation, significantly impacted behavior. We recommend standardizing testing protocols to account for sex differences, optimal stocking densities, nutritionally rich feeds and minimizing stressors to improve the reliability of zebrafish behavioral studies.

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Microchip-Based Structure Determination of Small Proteins Using Cryo-EM

SSRN

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SSRN 2020年

作者： Casasanta, Michael A. Jonaid, G.M. Kaylor, Liam Luqiu, William Y. Solares, Maria J. Schroen, Mariah L. Dearnaley, William J. Wilson, Jarad Dukes, Madeline J. Kelly, Deborah F. Department of Biomedical Engineering Pennsylvania State University University ParkPA16802 United States Materials Research Institute Pennsylvania State University University ParkPA16802 United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16802 United States Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16802 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States RayBiotech Life Peachtree CornersGA30092 United States Applications Science Protochips Inc MorrisvilleNC27560 United States

Interest in cryo-Electron Microscopy (EM) imaging has skyrocketed in recent years due to its pristine views of macromolecules and materials. As advances in instrumentation and computing algorithms spurred this progress, there is renewed focus to address specimen-related challenges. Here we contribute a microchip-based toolkit to perform complementary structural and biochemical analysis on low molecular-weight proteins. As a model system, we used the SARS-CoV-2 nucleocapsid (N) protein (48 kDa) due to its stability and important role in therapeutic development. Cryo-EM structures of the N protein monomer revealed a flexible N-terminal “top hat” motif and a helical-rich C-terminal domain. To complement our structural findings, we engineered microchip-based immunoprecipitation assays that led to the discovery of the first antibody binding site on the N protein. The data also facilitated molecular modeling of a variety of pandemic and common cold-related coronavirus proteins. Such insights may guide future pandemic-preparedness protocols through immuno-engineering strategies to mitigate viral outbreaks. © 2020, The Authors. All rights reserved.

关键词： Proteins

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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

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Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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Study of equilibrium sense improvement displaying visual information

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Key engineering Materials 2008年 381-382卷 369-372页

作者： Kim, G.B. Jeong, S.H. Chong, W.S. Kang, H.S. Kim, S.J. Kim, M.H. Hwang, J.W. Hong, C.U. Education Program for 21st Century Chonbuk National University Research Institute of Clinical Medicine Duckjin-Dong Jeonju Chonbuk 561-756 Korea Republic of Department of Computer Engineering Graduate School Chonbuk National University Duckjin-Dong Jeonju Chonbuk 561-756 Korea Republic of Department of Biomedical Engineering Graduate School Chonbuk National University Duckjin-Dong Jeonju Chonbuk 561-756 Korea Republic of Department of Pharmacology College of Veterinary Medicine Chonbuk National University Duckjin-Dong Jeonju Chonbuk 561-756 Korea Republic of Department of Bio and Applied Chemistry Iksan National College Ma-Dong Iksan Chonbuk 570-752 Korea Republic of Department of Thoracic and Cardiovascular Surgery Chonbuk National University Medical Schools Duckjin-Dong Jeonju Chonbuk 561-756 Korea Republic of Collage of Engineering Chonbuk National University Duckjin-Dong Jeonju Chonbuk 561-756 Korea Republic of Division of Bionics and Bioinformatics Collage of Engineering Chonbuk National University Duckjin-Dong Jeonju Chonbuk 561-756 Korea Republic of

The effectiveness of the visual feedback to improve ability with the sense and muscular strength of human in our bicycle system was quantitatively verified in our study. Experiments were performed to find the factors related to the training of equilibrium sense. The subjects consisted of young and the aged and the group of young people was compared against the group of the aged. We investigated three different training modes, non-visual feedback (NVF), visual feedback of the weight (VFW), and visual feedback of the center of pressure (VFC) and measured the riding time and speed, the weight shift, and the center of pressure (COP) for twenties and seventies. The results showed that the running capability of the young and the aged became much better after repeated training. In addition, it was found out that the ability to control postural balance and the capability of equilibrium sense were improved with the presentation of the visual feedback information. These results will be effective in the diagnosis of equilibrium sense and vestibular function with the aged.

关键词： Feedback

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Graph Artificial Intelligence in Medicine

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biomedical Data Science 1000年第1期7卷 345-368页

作者： Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 1Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA email: marinka@hms.harvard.edu 2Berkowitz Family Living Laboratory Harvard Medical School Boston Massachusetts USA 3Bioinformatics and Integrative Genomics Program Harvard Medical School Boston Massachusetts USA 4Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Allston Massachusetts USA 5Broad Institute of MIT and Harvard Cambridge Massachusetts USA 7Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Allston Massachusetts USA 6Harvard Data Science Initiative Cambridge Massachusetts USA

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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