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Estimating Classification Confidence Using Kernel Densities

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arXiv 2022年

作者： Salamon, Peter Salamon, David Cantu, V. Adrian An, Michelle Perry, Tyler Edwards, Robert A. Segall, Anca M. The Department of Mathematics and Statistics The Computational Science Research Center The Viral Information Institute San Diego State University San DiegoCA92182 United States The Department of Mathematics and Statistics San Diego State University San DiegoCA92182 United States The Computational Science Research Center The Department of Biology San Diego State University San DiegoCA92182 United States The Perelman School of Medicine The University of Pennsylvania 425 Johnson Pavilion 3610 Hamilton Walk PhiladelphiaPA19104 United States The Computational Science Research Center San Diego State University San DiegoCA92182 United States Google 1600 Amphitheater Parkway Mountain ViewCA94043 United States The Bioinformatics and Medical Informatics Program San Diego State University San DiegoCA92182 United States Helix 9875 Towne Center Drive San DiegoCA92121 United States The Department of Biology The Computational Science Research Center The Viral Information Institute The Bioinformatics and Medical Informatics Program San Diego State University San DiegoCA92182 United States

This paper investigates the post-hoc calibration of confidence for "exploratory" machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the "one-versus-all" approach (top-label calibration) must be used rather than the "calibrate-the-full-response-matrix" approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel, bulletproof algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) [1], [2] as well as the classic MNIST benchmark [3]. Finally, our analysis argues that post-hoc calibration should always be performed, should be based only on the test dataset, and should be sanity-checked visually. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

A transcriptome meta-analysis of ethanol embryonic exposure: Implications in neurodevelopment and neuroinflammatory genes

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Neuroscience informatics 2022年第3期2卷 100094-100094页

作者： Lord, Vinícius Oliveira Giudicelli, Giovanna Câmara Recamonde-Mendoza, Mariana Vianna, Fernanda Sales Luiz Kowalski, Thayne Woycinck Laboratory of Genomic Medicine Center of Experimental Research Hospital de Clínicas de Porto Alegre (HCPA) Porto Alegre Brazil Centro Universitário CESUCA Cachoeirinha Brazil Post-Graduation Program in Genetics and Molecular Biology Genetics Department Universidade Federal do Rio Grande do Sul (UFRGS) Porto Alegre Brazil Institute of Informatics Universidade Federal do Rio Grande do Sul (UFRGS) Porto Alegre Brazil Bioinformatics Core Hospital de Clínicas de Porto Alegre (HCPA) Porto Alegre Brazil National Institute of Medical Population Genetics (INAGEMP) Porto Alegre Brazil Post-Graduation Program in Medical Sciences Medical College Universidade Federal do Rio Grande do Sul (UFRGS) Porto Alegre Brazil

Fetal Alcohol Spectrum Disorder (FASD) comprises the phenotypes induced by prenatal alcohol exposure. Understanding the molecular mechanisms of FASD is needed since it is a public health problem. This study aimed to evaluate the impact of ethanol in the differential gene expression (DGE) of embryonic cells and fetal tissues by performing a transcriptome meta-analysis in microarrays datasets publicly available. The datasets were obtained in the GEO database and DGE was evaluated, followed by meta-analysis. DGE was also analyzed in a RNA-Seq dataset, although it was not included in the meta-analysis. To filter the main candidate genes, a database and literature review was performed, followed by ontologies enrichment analyses. In the meta-analysis, 1,938 genes were deregulated and 487 were perturbed in the RNA-Seq. Calcium homeostasis and neuroinflammation genes were overrepresented in the meta-analysis and RNA-Seq, respectively. After the database and literature review, DOCK8, FOXG1, IL1RN, and PRKN genes were proposed as new candidates for FASD;they are associated with neurodevelopment and neuroinflammation. BDNF and SLC2A1, previously associated to FASD, were also suggested in meta-analysis as candidate genes. It is known neuroinflammation reduction might help to minimize the alcohol damage. Hence, there is an urgent need to understand FASD molecular mechanisms to help in strategies aimed at preventing ethanol-induced neurologic damage. © 2022 The Authors

关键词： bioinformatics Differential gene expression Fetal alcohol spectrum disorder Microarray Neurophenotypes Ontology Prenatal ethanol exposure Random effect model RNA-Seq

Implications of self-identified race, ethnicity, and genetic ancestry on genetic association studies in biobanks within health systems

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arXiv 2024年

作者： Johnson, Ruth Pasaniuc, Bogdan Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Computer Science University of California Los Angeles Los AngelesCA90095 United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA90095 United States Department of Pathology Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States Institute of Precision Health University of California Los Angeles Los AngelesCA90095 United States

Precision medicine aims to create biomedical solutions tailored to specific factors that affect disease risk and treatment responses within the population. The success of the genomics era and recent widespread availability of electronic health records (EHR) has ushered in a new wave of genomic biobanks connected to EHR databases (EHR-linked biobanks). This perspective aims to discuss how race, ethnicity, and genetic ancestry are currently utilized to study common disease variation through genetic association studies. Although genetic ancestry plays a significant role in shaping the genetic landscape underlying disease risk in humans, the overall risk of a disease is caused by a complex combination of environmental, sociocultural, and genetic factors. When using EHR-linked biobanks to interrogate underlying disease etiology, it is also important to be aware of how the biases associated with commonly used descent-associated concepts such as race and ethnicity can propagate to downstream analyses. We intend for this resource to support researchers who perform or analyze genetic association studies in the EHR-linked biobank setting such as those involved in consortium-wide biobanking efforts. We provide background on how race, ethnicity, and genetic ancestry play a role in current association studies, highlight considerations where there is no consensus about best practices, and provide transparency about the current shortcomings. © 2024, CC BY.

关键词： Genome

Differential regulation of JAK1 expression by ETS1 associated with predisposition to primary biliary cholangitis

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Journal of Genetics and Genomics 2023年第10期50卷 807-812页

作者： Peng Jiang Chan Wang Mingming Zhang Ye Tian Weifeng Zhao Junyi Xin Yexi Huang Zhibin Zhao Wenjuan Sun Jie Long Ruqi Tang Fang Qiu Xingjuan Shi Yi Zhao Li Zhu Na Dai Lei Liu Xudong Wu Jinshan Nie Bo Jiang Youlin Shao Yueqiu Gao Jianjiang Yu Zhigang Hu Zhidong Zang Yuhua Gong Yaping Dai Lan Wang Ningling Ding Ping Xu Sufang Chen Lu Wang Jing Xu Luyao Zhang Junyan Hong Ruonan Qian Hu Li Xuan Jiang Congwei Chen Wenyan Tian Jian Wu Yuzhang Jiang Chongxu Han Kui Zhang Hong Qiu Li Li Hong Fan Liming Chen Jianqiong Zhang Zhongsheng Sun Xiao Han Zhenhua Dai Erguang Li M.Eric Gershwin Zhexiong Lian Xiong Ma Michael F.Seldin Weichang Chen Meilin Wang Xiangdong Liu Key Laboratory of Developmental Genes and Human Diseases School of Life Science and TechnologySoutheast UniversityNanjingJiangsu 210096China Institute of Translational Medicine Yangzhou University Medical CollegeYangzhouJiangsu 225009China Department of Radiology&Oncology The Second Affiliated Hospital of Soochow UniversitySuzhouJiangsu 215004China The First Affiliated Hospital of Soochow University SuzhouJiangsu 215006China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjingJiangsu 210029China Author links open overlay panelYexi Huang Guangdong Provincial People's Hospital Guangdong Academy of Medical SciencesGuangzhouGuangdong 510080China Department of Gastroenterology and Hepatology Shanghai Institute of Digestive DiseasesShanghai Jiao Tong University School of Medicine Affiliated Renji HospitalShanghai 200001China Department of Laboratory Medicine The Fourth Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsu 210031China Department of Gastrointestinal Endoscopy Eastern Hepatobiliary Surgery HospitalShanghai 201800China The Fifth People's Hospital Soochow UniversitySuzhouJiangsu 215007China Department of Gastroenterology Jiangsu University Affiliated Kunshan HospitalKunshanJiangsu 215300China Department of Gastroenterology Yixing People's HospitalYixinJiangsu 214200China Department of Gastroenterology Yancheng First People's HospitalYanchengJiangsu 224005China Department of Gastroenterology Taicang First People's HospitalSoochow UniversityTaicangJiangsu 215400China Department of Hepatology Jingjiang Second People's HospitalJingjiangJiangsu 214500China Department of Hepatology The Third People's Hospital of ChangzhouChangzhouJiangsu 213001China Department of Liver Diseases Shuguang HospitalShanghai University of Traditional Chinese MedicineShanghai 201203China Department of Laboratory Medicine Jiangyin People's HospitalSoutheast UniversityJiangyinJiangsu 214400China Department of Labora

Primary biliary cholangitis(PBC)is an autoimmune liver disease characterized by the destruction of intrahepatic small bile ducts and progressive cholestasis,eventually leading to liver cirrhosis and hepatic failure wi... 详细信息

关键词： cholangitis biliary

Assessing the Potential of GPT-4 to Annotate medical Notes Across Different Languages and Countries: A Model Evaluation Study

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SSRN

SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： medical computing

An Open-Source Knowledge Graph Ecosystem for the Life Sciences

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

Comprehensive functional annotation of susceptibility variants identifies genetic heterogeneity between lung adenocarcinoma and squamous cell carcinoma

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Frontiers of Medicine 2021年第2期15卷 275-291页

作者： Na Qin Yuancheng Li Cheng Wang Meng Zhu Juncheng Dai Tongtong Hong Demetrius Albanes Stephen Lam Adonina Tardon Chu Chen Gary Goodman Stig EBojesen Maria Teresa Landi Mattias Johansson Angela Risch H-Erich Wichmann Heike Bickeboller Gadi Rennert Susanne Arnold Paul Brennan John KField Sanjay Shete Loic Le Marchand Olle Melander Hans Brunnstrom Geoffrey Liu Rayjean JHung Angeline Andrew Lambertus AKiemeney Shan Zienolddiny Kjell Grankvist Mikael Johansson Neil Caporaso Penella Woll Philip Lazarus Matthew BSchabath Melinda CAldrich Victoria LStevens Guangfu Jin David CChristiani Zhibin Hu Christopher IAmos Hongxia Ma Hongbing Shen Department of Epidemiology Center for Global HealthSchool of Public HealthNanjing Medical UniversityNanjing 211166China State Key Laboratory of Reproductive Medicine Center for Global HealthNanjing Medical UniversityNanjing 211166China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China Jiangsu Key Lab of Cancer Biomarkers Prevention and TreatmentCollaborative Innovation Center for Cancer MedicineNanjing Medical UniversityNanjing 211166China China International Cooperation Center for Environment and Human Health School of Public HealthNanjing Medical UniversityNanjing 211166China Division of Cancer Epidemiology and Genetics National Cancer InstituteBethesdaMD 20892-9304USA Department of Integrative Oncology British Columbia Cancer AgencyVancouverBC V5Z 1L3Canada Faculty of Medicine University of Oviedo and CIBERESPOviedo 33006Spain Program in Epidemiology Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleWA 98109-1024USA Public Health Sciences Division Swedish Cancer InstituteSeattleWA 98026USA Department of Clinical Biochemistry Copenhagen University HospitalCopenhagen DK-1017Denmark National Cancer Institute BethesdaMD 20892-9304USA Genetic Epidemiology Group International Agency for Research on CancerLyon 69372France Cancer Center Cluster Salzburg at PLUS Department of Molecular BiologyUniversity of SalzburgHeidelberg 5020Austria Institute of Medical Informatics Biometry and EpidemiologyChair of EpidemiologyLudwig Maximilians UniversityMunichBavaria 80539Germany Department of Genetic Epidemiology University Medical Center GoettingenGoettingen 37075Germany Technion Faculty of Medicine Carmel Medical CenterHaifa 3448516Israel Markey Cancer Center University of KentuckyLexingtonKY 40506-0054USA Department of Molecular and Clinical Cancer Medicine Roy Castle Lung Cancer Research ProgrammeThe University of Liverpool Institute of Translational MedicineLiverpool L697Z

Although genome-wide association studies have identified more than eighty genetic variants associated with non-small cell lung cancer(NSCLC)risk,biological mechanisms of these variants remain largely *** integrating a large-scale genotype data of 15581 lung adenocarcinoma(AD)cases,8350 squamous cell carcinoma(SqCC)cases,and 27355 controls,as well as multiple transcriptome and epigenomic databases,we conducted histology-specific meta-analyses and functional annotations of both reported and novel susceptibility *** identified 3064 credible risk variants for NSCLC,which were overrepresented in enhancer-like and promoter-like histone modification peaks as well as DNase I hypersensitive *** factor enrichment analysis revealed that USF1 was AD-specific while CREB1 was *** annotation and genebased analysis implicated 894 target genes,including 274 specifics for AD and 123 for SqCC,which were overrepresented in somatic driver genes(ER=1.95,P=0.005).Pathway enrichment analysis and Gene-Set Enrichment Analysis revealed that AD genes were primarily involved in immune-related pathways,while SqCC genes were homologous recombination deficiency *** results illustrate the molecular basis of both wellstudied and new susceptibility loci of NSCLC,providing not only novel insights into the genetic heterogeneity between AD and SqCC but also a set of plausible gene targets for post-GWAS functional experiments.

关键词： lung cancer genome-wide association study function annotation immune homologous recombination repair deficiency genetic heterogeneity

White Paper on Enabling Global Image Data Sharing in the Life Sciences

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arXiv 2024年

作者： Bajcsy, Peter Bhattiprolu, Sreenivas Börner, Katy Cimini, Beth A. Collinson, Lucy Ellenberg, Jan Fiolka, Reto Giger, Maryellen Goscinski, Wojtek Hartley, Matthew Hotaling, Nathan Horwitz, Rick Jug, Florian Kreshuk, Anna Lundberg, Emma Mathur, Aastha Narayan, Kedar Onami, Shuichi Plant, Anne L. Prior, Fred Swedlow, Jason Taylor, Adam Keppler, Antje National Institute of Standards and Technology GaithersburgMD20899 United States ZEISS Microscopy Customer Center DublinCA94568 United States Intelligent Systems Engineering Indiana University BloomingtonIN47408 United States Imaging Platform Broad Institute CambridgeMA United States Electron Microscopy Science Technology Platform Francis Crick Institute 1 Midland Road LondonNW1 1AT United Kingdom European Molecular Biology Laboratory Heidelberg Germany Lyda Hill Department of Bioinformatics UT Southwestern Medical Center DallasTX United States Department of Radiology and Committee on Medical Physics University of Chicago ChicagoIL United States National Imaging Facility Brisbane Australia European Molecular Biology Laboratory European Bioinformatics Institute Hinxton Cambridge United Kingdom National Center for Advancing Translational Science National Institutes of Health Rockville United States Allen Institute for Cell Science Seattle United States Fondazione Human Technopole Milan Italy Stanford University California US and SciLifeLab KTH Royal Institute of Technology Stockholm Sweden Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD20892 United States Cancer Research Technology Program Frederick National Laboratory for Cancer Research FrederickMD21702 United States RIKEN Center for Biosystems Dynamics Research Kobe Japan Department of Biomedical Informatics Department of Radiology University of Arkansas for Medical Sciences Little RockAR72205 United States Divisions of Computational Biology and Molecular Cell and Developmental Biology University of Dundee United Kingdom Sage Bionetworks SeattleWA United States

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ‘ideal world scenario’ for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled "Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data", which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location. © 2024, CC BY-NC-SA.

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Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

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Biomedicine and Pharmacotherapy 2023年第1期166卷 115429-115429页

作者： Hsieh, Chiao-Hui Huang, Chen-Tsung Cheng, Yi-Sheng Hsu, Chun-Hua Hsu, Wen-Ming Chung, Yun-Hsien Liu, Yen-Lin Yang, Tsai-Shan Chien, Chia-Yu Lee, Yu-Hsuan Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Plant Biology National Taiwan University Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei Taiwan Department of Agricultural Chemistry National Taiwan University Taipei Taiwan Department of Surgery National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan Department of Pediatrics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma. © 2023 The Authors

关键词： Computational molecular docking Drug discovery High-risk neuroblastoma Metabolic reprogramming Molecular target therapy Phosphoglycerate dehydrogenase