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Journal of Anesthesia and Translational Medicine 2024年第2期3卷 36-44页

作者： Furmanek, Stephen Salunkhe, Vidyulata Pahwa, Siddharth Samanapally, Harideep Nathala, Pavani Xu, Qian Han, Yuchen Huang, Emma C. Ali, T'shura Deepti, Fnu Glynn, Alex McGuffin, Trevor Huang, Justin J. Farah, Ian Jones, Christopher M. Ramirez, Julio A. Clifford, Sean P. Arnold, Forest W. Kong, Maiying Roser, Lynn Huang, Jiapeng Division of Infectious Diseases Center of Excellence for Research in Infectious Diseases (CERID) Department of Medicine University of Louisville 501 E Broadway Louisville 40202 KY United States Department of Bioinformatics and Biostatistics School of Public Health and Information Sciences University of Louisville 485 E Gray St Louisville 40202 KY United States School of Nursing University of Louisville 555 S Floyd St Louisville 40202 KY United States Department of Anesthesiology & Perioperative Medicine University of Louisville 530 S Jackson St Louisville 40202 KY United States Department of Pharmacology & Toxicology University of Louisville 505 S Hancock St Louisville 40202 KY United States Department of Cardiovascular & Thoracic Surgery University of Louisville 201 Abraham Flexner Way Louisville 40202 KY United States Center for Integrative Environmental Health Sciences University of Louisville 500 S Preston St Louisville 40202 KY United States Division of Transplantation Department of Surgery University of Louisville 323 E Chestnut St Louisville 40202 KY United States Norton Infectious Diseases Institute Norton Healthcare 234 E Gray St Louisville 40202 KY United States Department of Anesthesiology Duke University Durham 27710 NC United States Biometrics and Data Science Fosun Pharma Beijing 100026 China

Introduction: This study aims to explore the predictive roles of echocardiographic parameters and biomarkers in determining outcomes among hospitalized COVID-19 patients experiencing cardiovascular events. Methods: A retrospective cohort study was conducted involving 49 COVID-19 patients who encountered cardiovascular events during hospitalization and underwent echocardiography. Our findings revealed notable associations between echocardiographic parameters and survival time. Results: A decrease in left ventricular ejection fraction (LVEF) of 10% was linked to a 20% reduction in survival time (TR: 0.80, 95% CI: 0.67 – 0.96, p = 0.017). Similarly, an increase in left ventricular (LV) volume by 10 mL was associated with a 9% decrease in survival time (TR: 0.91, 95% CI: 0.84 – 0.98, p = 0.011). Moreover, an increase in left atrial (LA) volume by 10 mL corresponded to an 8% decrease in survival time (TR: 0.92, 95% CI: 0.86 – 0.99, p = 0.026). Additionally, each 1 cm increase in right ventricular (RV) diameter was linked to a 22% reduction in survival time (TR: 0.78, 95% CI: 0.61 – 0.99, p = 0.043). Furthermore, a 10 mL increase in right atrial (RA) volume was associated with a 12% decrease in survival time (TR: 0.88, 95% CI: 0.78 – 0.98, p = 0.017). Notably, a tenfold rise in troponin levels was linked to a 33% decrease in survival time (TR: 0.67, 95% CI: 0.48 – 0.93, p = 0.014). Conclusions: Our study emphasizes the significant associations between various echocardiographic parameters and troponin levels with reduced survival time among COVID-19 patients experiencing cardiovascular events. These findings highlight the potential utility of echocardiography and troponin assessment in predicting outcomes and guiding management strategies in this patient population. © 2024 Wuhan Institute of Intelligent Perioperative Medicine (WIIPM)

关键词： Brain natriuretic peptide Cardiovascular COVID-19 Echocardiography SARS-CoV-2 Troponin

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Polygenic risk modeling for prediction of epithelial ovarian cancer risk (vol 30, pg 349, 2021)

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EUROPEAN JOURNAL OF HUMAN GENETICS 2022年第5期30卷 630-631页

作者： Dareng, Eileen O. Tyrer, Jonathan P. Barnes, Daniel R. Jones, Michelle R. Yang, Xin Aben, Katja K. H. Adank, Muriel A. Agata, Simona Andrulis, Irene L. Anton-Culver, Hoda Antonenkova, Natalia N. Aravantinos, Gerasimos Arun, Banu K. Augustinsson, Annelie Balmana, Judith Bandera, Elisa V. Barkardottir, Rosa B. Barrowdale, Daniel Beckmann, Matthias W. Beeghly-Fadiel, Alicia Benitez, Javier Bermisheva, Marina Bernardini, Marcus Q. Bjorge, Line Black, Amanda Bogdanova, Natalia V. Bonanni, Bernardo Borg, Ake Brenton, James D. Budzilowska, Agnieszka Butzow, Ralf Buys, Saundra S. Cai, Hui Caligo, Maria A. Campbell, Ian Cannioto, Rikki Cassingham, Hayley Chang-Claude, Jenny Chanock, Stephen J. Chen, Kexin Chiew, Yoke-Eng Chung, Wendy K. Claes, Kathleen B. M. Colonna, Sarah Cook, Linda S. Couch, Fergus J. Daly, Mary B. Dao, Fanny Davies, Eleanor de la Hoya, Miguel de Putter, Robin Dennis, Joe DePersia, Allison Devilee, Peter Diez, Orland Ding, Yuan Chun Doherty, Jennifer A. Domchek, Susan M. Dork, Thilo du Bois, Andreas Durst, Matthias Eccles, Diana M. Eliassen, Heather A. Engel, Christoph Evans, Gareth D. Fasching, Peter A. Flanagan, James M. Fortner, Renee T. Machackova, Eva Friedman, Eitan Ganz, Patricia A. Garber, Judy Gensini, Francesca Giles, Graham G. Glendon, Gord Godwin, Andrew K. Goodman, Marc T. Greene, Mark H. Gronwald, Jacek Hahnen, Eric Haiman, Christopher A. Hakansson, Niclas Hamann, Ute Hansen, Thomas V. O. Harris, Holly R. Hartman, Mikael Heitz, Florian Hildebrandt, Michelle A. T. Hogdall, Estrid Hogdall, Claus K. Hopper, John L. Huang, Ruea-Yea Huff, Chad Hulick, Peter J. Huntsman, David G. Imyanitov, Evgeny N. Isaacs, Claudine Jakubowska, Anna James, Paul A. Janavicius, Ramunas Jensen, Allan Johannsson, Oskar Th. John, Esther M. Jones, Michael E. Kang, Daehee Karlan, Beth Y. Karnezis, Anthony Kelemen, Linda E. Khusnutdinova, Elza Kiemeney, Lambertus A. Kim, Byoung-Gie Kjaer, Susanne K. Komenaka, Ian Kupryjanczyk, Jolanta Kurian, Allison W. Kwong, Ava Lambrechts, Diether Larson, Melissa C. Lazaro, Conxi L University of Cambridge Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care Cambridge UK University of Cambridge Centre for Cancer Genetic Epidemiology Department of Oncology Cambridge UK Center for Bioinformatics and Functional Genomics Cedars-Sinai Medical Center Los Angeles CA USA Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen The Netherlands Netherlands Comprehensive Cancer Organisation Utrecht The Netherlands The Netherlands Cancer Institute—Antoni van Leeuwenhoek hospital Family Cancer Clinic Amsterdam The Netherlands Veneto Institute of Oncology IOV—IRCCS Immunology and Molecular Oncology Unit Padua Italy Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Fred A. Litwin Center for Cancer Genetics Toronto ON Canada University of Toronto Department of Molecular Genetics Toronto ON Canada University of California Irvine Department of Epidemiology Genetic Epidemiology Research Institute Irvine CA USA N.N. Alexandrov Research Institute of Oncology and Medical Radiology Minsk Belarus ‘Agii Anargiri’ Cancer Hospital Athens Greece University of Texas MD Anderson Cancer Center Department of Breast Medical Oncology Houston TX USA Lund University Department of Cancer Epidemiology Clinical Sciences Lund Sweden Vall d’Hebron Institute of Oncology Hereditary cancer Genetics Group Barcelona Spain University Hospital of Vall d’Hebron Department of Medical Oncology Barcelona Spain Rutgers Cancer Institute of New Jersey Cancer Prevention and Control Program New Brunswick NJ USA Landspitali University Hospital Department of Pathology Reykjavik Iceland University of Iceland BMC (Biomedical Centre) Faculty of Medicine Reykjavik Iceland University Hospital Erlangen Friedrich-Alexander-University Erlangen-Nuremberg Department of Gynecology and Obstetrics Comprehensive Cancer Center ER-EMN Erlangen Germany Vanderbilt University School of Medicine Division of Epidemiology Department

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

关键词： Clinical genetics Genetic markers Risk factors Biomedicine general Human Genetics bioinformatics Gene Expression Cytogenetics

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Climate-influenced vector-borne diseases in Africa:a call to empower the next generation of African researchers for sustainable solutions

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Infectious Diseases of Poverty 2024年第2期13卷 83-92页

作者： Judicaël Obame-Nkoghe Adjoavi Esse Agossou Gerald Mboowa Basile Kamgang Cyril Caminade Dawn C.Duke Andrew Karanja Githeko Obed M.Ogega Nestor Engone Elloué Fatou Bintou Sarr DieudonnéNkoghe Pierre Kengne Nicaise T.Ndam Christophe Paupy Moses Bockarie Patricks Voua Otomo Molecular and Cellular Biology Laboratory(LabMC) Biology DepartmentUniversity of Science and Technology of MasukuBP 901FrancevilleGabon Ecology and Health Research Unit Interdisciplinary Center for Medical Research of FrancevilleBP 769FrancevilleGabon Department of Zoology and Entomology Faculty of Natural and Agricultural SciencesUniversity of the Free StatePrivate Bag x13Phuthaditjhaba 9866Republic of South Africa Laboratory of Pharmacology and Improved Traditional Medicines Department of Animal PhysiologyFaculty of Science and TechnologyUniversity of Abomey-CalaviBP 526CotonouBenin The African Center of Excellence in Bioinformatics and Data-Intensive Sciences Infectious Diseases InstituteCollege of Health SciencesMakerere UniversityP.O Box 22418KampalaUganda Africa Centers for Disease Control and Prevention African Union CommissionRoosevelt StreetP.O.Box 3243W21 K19 Addis AbabaEthiopia Centre for Research in Infectious Diseases P.O.Box 13591YaoundéCameroon Earth System Physics Department The Abdus Salam International Centre for Theoretical PhysicsTriesteItaly Africa Research Excellence Fund 99 Charterhouse StreetLondon EC1M 6HRUK Community Health Support Program P.O.Box 872Kisumu 40100Kenya The African Academy of Sciences NairobiKenya Center for Phylosophical Studies and Research(CERP) Omar Bongo University(UOB)BP 13131LibrevilleGabon UMRED Health Training and Research UnitUniversity of Iba Der Thiam of ThièsBP 967ThièsSenegal National Parasitic Diseases Control Program Ministry of HealthLibrevilleGabon MIVEGEC Univ.MontpellierCNRSIRDMontpellierFrance MERIT IRDParis CitéUniversity75006 ParisFrance Department of Parasitology Noguchi Memorial Institute for Medical ResearchUniversity of GhanaLG 54AccraGhana School of Community Health Sciences Njala UniversityBoSierra Leone

We look at the link between climate change and vector-borne diseases in low-and middle-income countries in *** large endemicity and escalating threat of diseases such as malaria and arboviral diseases,intensified by climate change,disproportionately affects vulnerable communities *** highlight the urgency of prioritizing research and development,advocating for robust scientific inquiry to promote adaptation strategies,and the vital role that the next generation of African research leaders will play in addressing these *** significant challenges such as funding shortages within countries,various pan-African-oriented funding bodies such as the African Academy of sciences,the Africa research Excellence Fund,the Wellcome Trust,the *** Institutes of Health,and the Bill and Melinda Gates Foundation as well as initiatives such as the African research Initiative for Scientific Excellence and the Pan-African Mosquito Control Association,have empowered(or are empowering)these researchers by supporting capacity building activities,including continental and global networking,skill development,mentoring,and African-led *** article underscores the urgency of increased national investment in research,proposing the establishment of research government agencies to drive evidence-based *** between governments and scientific communities,sustained by pan-African funding bodies,is *** these efforts,African nations are likely to enhance the resilience and adaptive capacity of their systems and communities by navigating these challenges effectively,fostering scientific excellence and implementing transformative solutions against climate-sensitive vector-borne diseases.

关键词： Climate change Vector-borne diseases Africa Environmental justice research and development Pan-African funding initiatives

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Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study

arXiv

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arXiv 2023年

作者： Despotovic, Vladimir Kim, Sang-Yoon Hau, Ann-Christin Kakoichankava, Aliaksandra Klamminger, Gilbert Georg Borgmann, Felix Bruno Kleine Frauenknecht, Katrin B.M. Mittelbronn, Michel Nazarov, Petr V. Bioinformatics Platform Data Integration and Analysis Unit Luxembourg Institute of Health Strassen Luxembourg Dr. Senckenberg Institute of Neurooncology University Hospital Frankfurt Frankfurt am Main Germany Edinger Institute Institute of Neurology Goethe University Frankfurt am Main Germany Frankfurt Cancer Institute Goethe University Frankfurt am Main Germany University Cancer Center Frankfurt Frankfurt am Main Germany University Hospital Goethe University Frankfurt am Main Germany Laboratoire national de santé National Center of Pathology Dudelange Luxembourg Multi-Omics Data Science group Department of Cancer Research Luxembourg Institute of Health Strassen Luxembourg Luxembourg Centre of Neuropathology Dudelange Luxembourg Klinik für Frauenheilkunde Geburtshilfe und Reproduktionsmedizin Saarland University Homburg Germany Department of Cancer Research Luxembourg Institute of Health Strassen Luxembourg Luxembourg Centre for Systems Biomedicine University of Luxembourg Belval Luxembourg Department of Life Sciences and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg

We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist’s efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas;thus, providing insights to pathologists concerning the most informative parts of the WSI. © 2023, CC BY-NC-ND.

关键词： Tumors

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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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Visualization of Biomedical data

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Biomedical data science 1000年第1期1卷 275-304页

作者： Seán I. O'Donoghue Benedetta Frida Baldi Susan J. Clark Aaron E. Darling James M. Hogan Sandeep Kaur Lena Maier-Hein Davis J. McCarthy William J. Moore Esther Stenau Jason R. Swedlow Jenny Vuong James B. Procter 2Genomics and Epigenetics Division Garvan Institute of Medical Research Sydney NSW 2010 Australia 4The ithree Institute University of Technology Sydney Ultimo NSW 2007 Australia 5School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane QLD 4000 Australia 6School of Computer Science and Engineering University of New South Wales (UNSW) Kensington NSW 2033 Australia 7Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany 9St. Vincent's Institute of Medical Research Fitzroy VIC 3065 Australia 8European Bioinformatics Institute (EBI) European Molecular Biology Laboratory (EMBL) Wellcome Genome Campus Hinxton CB10 1SD United Kingdom 10School of Life Sciences University of Dundee Dundee DD1 5EH United Kingdom 1Data61 Commonwealth Scientific and Industrial Research Organisation (CSIRO) Eveleigh NSW 2015 Australia email: sean@***

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.

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From best hits to best matches

arXiv

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arXiv 2020年

作者： Stadler, Peter F. Geiß, Manuela Schaller, David Sánchez, Alitzel López González, Marcos E. Valdivia, Dulce I. Hellmuth, Marc Rosales, Maribel Hernández Bioinformatics Group Department of Computer Science Interdisciplinary Center of Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany CONACYT-Instituto de Matemáticas UNAM Juriquilla Blvd. Juriquilla 3001 Juriquilla Querétaro QRO76230 Mexico Centro de Investigación y de Estudios Avanzados del IPN CINVESTAV-Irapuato Km. 9.6 Libramiento Norte Carretera Irapuato-León Irapuato GTO36821 Mexico Institute of Mathematics and Computer Science University of Greifswald Walther-Rathenau-Straße 47 GreifswaldD-17487 Germany Center for Bioinformatics Saarland University Building E 2.1 P.O. Box 151150 SaarbrückenD-66041 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Leipzig Research Center for Civilization Diseases Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max-Planck-Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Inst. f. Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Ciudad Universitaria Bogotá D.C.111321 Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Background: Many of the commonly used methods for orthology detection start from mutually most similar pairs of genes (reciprocal best hits) as an approximation for evolutionary most closely related pairs of genes (reciprocal best matches). This approximation of best matches by best hits becomes exact for ultrametric dissimilarities, i.e., under the Molecular Clock Hypothesis. It fails, however, whenever there are large lineage specific rate variations among paralogous genes. In practice, this introduces a high level of noise into the input data for best-hit-based orthology detection methods. Results: If additive distances between genes are known, then evolutionary most closely related pairs can be identified by considering certain quartets of genes provided that in each quartet the outgroup relative to the remaining three genes is known. A priori knowledge of underlying species phylogeny greatly facilitates the identification of the required outgroup. Although the workflow remains a heuristic since the correct outgroup cannot be determined reliably in all cases, simulations with lineage specific biases and rate asymmetries show that nearly perfect results can be achieved. In a realistic setting, where distances data have to be estimated from sequence data and hence are noisy, it is still possible to obtain highly accurate sets of best matches. Conclusion: Improvements of tree-free orthology assessment methods can be expected from a combination of the accurate inference of best matches reported here and recent mathematical advances in the understanding of (reciprocal) best match graphs and orthology relations. Copyright © 2020, The Authors. All rights reserved.

关键词： Genes

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Cavity approach for the approximation of spectral density of graphs with heterogeneous structures

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Physical Review E 2024年第3期109卷 034303-034303页

作者： Grover E. C. Guzman Peter F. Stadler Andre Fujita Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 São Paulo - SP 05508-090 Brazil Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics School of Excellence in Embedded Composite AI Dresden/Leipzig (SECAI) Leipzig University Härtelstraße 16-18 D-04107 Leipzig Germany German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Puschstraße 4 04103 Leipzig Germany Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Humboldtstraße 25 04105 Leipzig Germany Leipzig University Härtelstraße 16-18 D-04107 Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 D-04103 Leipzig Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 A-1090 Wien Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá 111321 Colombia and The Santa Fe Institute 1399 Hyde Park Road Santa Fe New Mexico 87501 USA Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 São Paulo - SP 05508-090 Brazil and Division of Network AI Statistics Medical Institute of Bioregulation Kyushu University Fukuoka 812-8582 Japan

Graphs have become widely used to represent and study social, biological, and technological systems. Statistical methods to analyze empirical graphs were proposed based on the graph's spectral density. However, their running time is cubic in the number of vertices, precluding direct application to large instances. Thus, efficient algorithms to calculate the spectral density become necessary. For sparse graphs, the cavity method can efficiently approximate the spectral density of locally treelike undirected and directed graphs. However, it does not apply to most empirical graphs because they have heterogeneous structures. Thus, we propose methods for undirected and directed graphs with heterogeneous structures using a new vertex's neighborhood definition and the cavity approach. Our methods' time and space complexities are O(|E|hmax3t) and O(|E|hmax2t), respectively, where |E| is the number of edges, hmax is the size of the largest local neighborhood of a vertex, and t is the number of iterations required for convergence. We demonstrate the practical efficacy by estimating the spectral density of simulated and real-world undirected and directed graphs.

关键词： Network structure Directed networks Monte Carlo methods Network Models Topological tools

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A novel tropical geometry-based interpretable machine learning method: Application in prognosis of advanced heart failure

arXiv

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arXiv 2021年

作者： Yao, Heming Derksen, Harm Golbus, Jessica R. Zhang, Justin Aaronson, Keith D. Gryak, Jonathan Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Department of Mathematics Northeastern University BostonMA United States Division of Cardiovascular Medicine Department of Internal Medicine University of Michigan Ann ArborMI United States Electrical and Computer Engineering College of Engineering University of Michigan Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Michigan Center for Integrative Research in Critical Care University of Michigan Ann ArborMI United States

A model's interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to investigate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, the proposed method was applied to a clinical application that identified heart failure patients that would benefit from advanced therapies such as heart transplant or durable mechanical circulatory support. Experimental results show that the proposed network achieved great performance on the classification tasks. In addition to learning humanly understandable rules from the dataset, existing fuzzy domain knowledge can be easily transferred into the network and used to facilitate model training. From our results, the proposed model and the ability of learning existing domain knowledge can significantly improve the model generalizability. The characteristics of the proposed network make it promising in applications requiring model reliability and justification. Copyright © 2021, The Authors. All rights reserved.

关键词： Domain Knowledge

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Detecting Hemileia vastatrix using Vision AI as Supporting to Food Security for Smallholder Coffee Commodities

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Teddy Suparyanto Erick Firmansyah Tjeng Wawan Cenggoro Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The loss of food crops during the COVID-19 pandemic threatens food security in Indonesia as one of the world's top coffee producers. This affects the insecurity of coffee commodities which is influenced by several factors other than the pandemic such as pests, plant diseases, and extreme weather. Plant diseases, such as leaf rust, are a significant factor in the insecurity problem in coffee commodities. The fungus Hemileia vastatrix B et Br causes leaf rust disease, which is a pest that frequently damages coffee plants. This disease not only interferes with plant growth but also causes a decrease in coffee quality and quantity. This initial research aims to carry out early prevention of these diseases as supporting to food security in smallholder coffee commodities. An AI-based visual detection application is the result of this research. We collected 100 images of coffee leaves from various coffee plants. The image is reshaped to 256 x 256 pixels and randomly trimmed to 224 x 224 pixels to fulfill the size requirements of a standard Deep Learning technique. Each image was classified into two classes by a plant pest and disease specialist. The dataset was divided into training, validation, and testing series with a ratio of 60:20:20 for training procedures. The Convolutional Neural Network (CNN) research method used a variation of the ResNet CNN model with 18 layers. The best model validation was 59%.

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