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Wyszukujesz frazę ""Machine Learning"" wg kryterium: Temat


Tytuł :
Vulnerability of classifiers to evolutionary generated adversarial examples.
Autorzy :
Vidnerová P; The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 271/2, 182 07 Prague 8, Czechia. Electronic address: .
Neruda R; The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 271/2, 182 07 Prague 8, Czechia. Electronic address: .
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Źródło :
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2020 Jul; Vol. 127, pp. 168-181. Date of Electronic Publication: 2020 Apr 20.
Typ publikacji :
Journal Article
Journal Info :
Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
MeSH Terms :
Neural Networks, Computer*
Supervised Machine Learning*/trends
Pattern Recognition, Automated/*methods
Algorithms ; Humans ; Machine Learning/trends ; Pattern Recognition, Automated/trends
Czasopismo naukowe
Tytuł :
Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.
Autorzy :
Smith AM; Unlearn.AI, Inc., San Francisco, CA, USA. .
Walsh JR; Unlearn.AI, Inc., San Francisco, CA, USA.
Long J; Computational Sciences, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA.
Davis CB; Oncology Global Product Development, Pfizer Inc., San Diego, CA, USA.
Henstock P; Business Technology, Pfizer Inc., Cambridge, MA, USA.
Hodge MR; Inflammation and Immunology, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA.
Maciejewski M; Inflammation and Immunology, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA.
Mu XJ; Oncology Research & Development, Worldwide Research & Development, Pfizer Inc., San Diego, CA, USA.
Ra S; Computational Sciences, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA.
Zhao S; Computational Sciences, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA.
Ziemek D; Inflammation and Immunology, Worldwide Research & Development, Pfizer Pharma GmbH., Berlin, Germany.
Fisher CK; Unlearn.AI, Inc., San Francisco, CA, USA.
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Źródło :
BMC bioinformatics [BMC Bioinformatics] 2020 Mar 20; Vol. 21 (1), pp. 119. Date of Electronic Publication: 2020 Mar 20.
Typ publikacji :
Journal Article
Journal Info :
Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE
MeSH Terms :
Deep Learning*
Gene Expression Profiling*
Machine Learning*
Phenotype*
Disease/genetics ; Humans ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
Predicting Wait Times in Pediatric Ophthalmology Outpatient Clinic Using Machine Learning.
Autorzy :
Lin WC; Departments of Medical Informatics and Clinical Epidemiology and.
Goldstein IH; Ophthalmology, OHSU.
Hribar MR; Departments of Medical Informatics and Clinical Epidemiology and.
Sanders DS; Ophthalmology, OHSU.; Legacy Devers Eye Institute, Portland, OR.
Chiang MF; Departments of Medical Informatics and Clinical Epidemiology and.; Ophthalmology, OHSU.
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Źródło :
AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2020 Mar 04; Vol. 2019, pp. 1121-1128. Date of Electronic Publication: 2020 Mar 04 (Print Publication: 2019).
Typ publikacji :
Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Journal Info :
Publisher: American Medical Informatics Association Country of Publication: United States NLM ID: 101209213 Publication Model: eCollection Cited Medium: Internet ISSN: 1942-597X (Electronic) Linking ISSN: 15594076 NLM ISO Abbreviation: AMIA Annu Symp Proc Subsets: MEDLINE
MeSH Terms :
Ambulatory Care Facilities*/organization & administration
Machine Learning*
Ophthalmology*
Humans ; Linear Models ; Models, Statistical ; Patient Satisfaction ; Pediatrics ; ROC Curve ; Supervised Machine Learning ; Time Factors ; Time-to-Treatment
Czasopismo naukowe
Tytuł :
Machine learning for syndromic surveillance using veterinary necropsy reports.
Autorzy :
Bollig N; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, United States of America.; Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, United States of America.
Clarke L; Wisconsin Veterinary Diagnostic Laboratory, University of Wisconsin-Madison, Madison, WI, United States of America.
Elsmo E; Wisconsin Veterinary Diagnostic Laboratory, University of Wisconsin-Madison, Madison, WI, United States of America.
Craven M; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, United States of America.; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States of America.
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Źródło :
PloS one [PLoS One] 2020 Feb 05; Vol. 15 (2), pp. e0228105. Date of Electronic Publication: 2020 Feb 05 (Print Publication: 2020).
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Journal Info :
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS ONE Subsets: MEDLINE
MeSH Terms :
Machine Learning*
Gastrointestinal Diseases/*pathology
Lung Diseases/*pathology
Urologic Diseases/*pathology
Animals ; Area Under Curve ; Deep Learning ; Logistic Models ; ROC Curve ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
Machine learning in cardiovascular magnetic resonance: basic concepts and applications.
Autorzy :
Leiner T; Department of Radiology | E.01.132, Utrecht University Medical Center, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands. .
Rueckert D; Biomedical Image Analysis Group, Department of Computing, Imperial College, London, UK.
Suinesiaputra A; Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
Baeßler B; Department of Radiology, University Hospital of Cologne, Cologne, Germany.; Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Nezafat R; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA, USA.
Išgum I; Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands.
Young AA; Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.; Department of Biomedical Engineering, King's College London, London, UK.
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Źródło :
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance [J Cardiovasc Magn Reson] 2019 Oct 07; Vol. 21 (1), pp. 61. Date of Electronic Publication: 2019 Oct 07.
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Review
Journal Info :
Publisher: BioMed Central Country of Publication: England NLM ID: 9815616 Publication Model: Electronic Cited Medium: Internet ISSN: 1532-429X (Electronic) Linking ISSN: 10976647 NLM ISO Abbreviation: J Cardiovasc Magn Reson Subsets: MEDLINE
MeSH Terms :
Diagnosis, Computer-Assisted*
Image Interpretation, Computer-Assisted*
Machine Learning*
Magnetic Resonance Imaging, Cine*
Myocardial Perfusion Imaging*
Cardiovascular Diseases/*diagnostic imaging
Cardiovascular Diseases/pathology ; Cardiovascular Diseases/physiopathology ; Coronary Circulation ; Deep Learning ; Humans ; Myocardium/pathology ; Predictive Value of Tests ; Reproducibility of Results ; Supervised Machine Learning ; Unsupervised Machine Learning
Czasopismo naukowe
Tytuł :
A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets.
Autorzy :
Franks JM; Geisel School of Medicine at Dartmouth, Department of Molecular and Systems Biology, Hanover and Lebanon, New Hampshire.
Martyanov V; Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
Cai G; Arnold School of Public Health at University of South Carolina, Columbia.
Wang Y; Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
Li Z; Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
Wood TA; Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
Whitfield ML; Geisel School of Medicine at Dartmouth, Department of Molecular and Systems Biology, Hanover and Lebanon, New Hampshire.
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Źródło :
Arthritis & rheumatology (Hoboken, N.J.) [Arthritis Rheumatol] 2019 Oct; Vol. 71 (10), pp. 1701-1710. Date of Electronic Publication: 2019 Sep 02.
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Journal Info :
Publisher: Wiley Country of Publication: United States NLM ID: 101623795 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2326-5205 (Electronic) Linking ISSN: 23265191 Subsets: Core Clinical (AIM); MEDLINE
MeSH Terms :
Supervised Machine Learning*
Transcriptome*
Scleroderma, Systemic/*classification
Adult ; Aged ; Algorithms ; Datasets as Topic ; Female ; Gene Expression ; Gene Expression Profiling ; High-Throughput Nucleotide Sequencing ; Humans ; Machine Learning ; Male ; Middle Aged ; Scleroderma, Systemic/genetics ; Young Adult
Czasopismo naukowe
Tytuł :
Impact of Artificial Intelligence on Interventional Cardiology: From Decision-Making Aid to Advanced Interventional Procedure Assistance.
Autorzy :
Sardar P; Cardiovascular Institute, Warren Alpert Medical School at Brown University, Providence, Rhode Island.
Abbott JD; Cardiovascular Institute, Warren Alpert Medical School at Brown University, Providence, Rhode Island.
Kundu A; Division of Cardiovascular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
Aronow HD; Cardiovascular Institute, Warren Alpert Medical School at Brown University, Providence, Rhode Island.
Granada JF; Cardiovascular Research Foundation, Columbia University Medical Center, New York, New York.
Giri J; Penn Cardiovascular Outcomes, Quality and Evaluative Research Center, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania; Cardiovascular Medicine Division, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: .
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Źródło :
JACC. Cardiovascular interventions [JACC Cardiovasc Interv] 2019 Jul 22; Vol. 12 (14), pp. 1293-1303.
Typ publikacji :
Journal Article; Review
Journal Info :
Publisher: Elsevier Country of Publication: United States NLM ID: 101467004 Publication Model: Print Cited Medium: Internet ISSN: 1876-7605 (Electronic) Linking ISSN: 19368798 NLM ISO Abbreviation: JACC Cardiovasc Interv Subsets: MEDLINE
MeSH Terms :
Cardiac Catheterization*
Clinical Decision-Making*
Decision Support Techniques*
Diagnosis, Computer-Assisted*
Machine Learning*
Robotics*
Therapy, Computer-Assisted*
Cardiovascular Diseases/*diagnosis
Cardiovascular Diseases/*therapy
Cardiovascular Diseases/classification ; Deep Learning ; Diffusion of Innovation ; Humans ; Patient Selection ; Predictive Value of Tests ; Supervised Machine Learning ; Unsupervised Machine Learning ; Workflow
Czasopismo naukowe
Tytuł :
Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.
Autorzy :
Liu D; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.; Department of Pediatrics, Harvard Medical School, Boston, MA, United States.
Clemente L; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.; Department of Pediatrics, Harvard Medical School, Boston, MA, United States.; Tecnologico de Monterrey, Monterrey, Mexico.
Poirier C; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.; Department of Pediatrics, Harvard Medical School, Boston, MA, United States.
Ding X; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.; Harvard TH Chan School of Public Health, Boston, MA, United States.
Chinazzi M; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, United States.
Davis J; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, United States.
Vespignani A; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, United States.; ISI Foundation, Turin, Italy.
Santillana M; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.; Department of Pediatrics, Harvard Medical School, Boston, MA, United States.; Harvard TH Chan School of Public Health, Boston, MA, United States.
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Źródło :
Journal of medical Internet research [J Med Internet Res] 2020 Aug 17; Vol. 22 (8), pp. e20285. Date of Electronic Publication: 2020 Aug 17.
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural
Journal Info :
Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J. Med. Internet Res. Subsets: MEDLINE
MeSH Terms :
Data Analysis*
Machine Learning*
Models, Biological*
Coronavirus Infections/*epidemiology
Coronavirus Infections/*transmission
Forecasting/*methods
Pneumonia, Viral/*epidemiology
Pneumonia, Viral/*transmission
China/epidemiology ; Disease Outbreaks ; Humans ; Internet ; Mass Media ; Models, Statistical ; Pandemics ; Public Health/methods
SCR Disease Name :
COVID-19
Czasopismo naukowe
Tytuł :
Prediction of hERG potassium channel blockage using ensemble learning methods and molecular fingerprints.
Autorzy :
Liu M; School of Life Science, Liaoning University, Shenyang, 110036, China.
Zhang L; School of Life Science, Liaoning University, Shenyang, 110036, China; Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Shenyang, Liaoning University, Shenyang, 110036, China; Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Liaoning University, Shenyang, 110036, China.
Li S; School of Life Science, Liaoning University, Shenyang, 110036, China.
Yang T; School of Life Science, Liaoning University, Shenyang, 110036, China.
Liu L; School of Life Science, Liaoning University, Shenyang, 110036, China.
Zhao J; School of Life Science, Liaoning University, Shenyang, 110036, China.
Liu H; School of Life Science, Liaoning University, Shenyang, 110036, China; Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Shenyang, Liaoning University, Shenyang, 110036, China; Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Liaoning University, Shenyang, 110036, China. Electronic address: .
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Źródło :
Toxicology letters [Toxicol Lett] 2020 Oct 10; Vol. 332, pp. 88-96. Date of Electronic Publication: 2020 Jul 03.
Typ publikacji :
Journal Article
Journal Info :
Publisher: Elsevier Country of Publication: Netherlands NLM ID: 7709027 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-3169 (Electronic) Linking ISSN: 03784274 NLM ISO Abbreviation: Toxicol. Lett. Subsets: MEDLINE
MeSH Terms :
Machine Learning*
ERG1 Potassium Channel/*antagonists & inhibitors
Peptide Mapping/*methods
Potassium Channel Blockers/*pharmacology
Algorithms ; Animals ; Area Under Curve ; CHO Cells ; Cardiotoxicity ; Cricetinae ; Cricetulus ; Databases, Factual ; Humans ; Models, Molecular ; Predictive Value of Tests ; Support Vector Machine
Czasopismo naukowe
Tytuł :
Clinical Predictive Models for COVID-19: Systematic Study.
Autorzy :
Schwab P; F Hoffmann-La Roche Ltd, Basel, Switzerland.
DuMont Schütte A; Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland.
Dietz B; Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland.
Bauer S; Max Planck Institute for Intelligent Systems, Tübingen, Germany.
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Źródło :
Journal of medical Internet research [J Med Internet Res] 2020 Oct 06; Vol. 22 (10), pp. e21439. Date of Electronic Publication: 2020 Oct 06.
Typ publikacji :
Journal Article
Journal Info :
Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE
MeSH Terms :
Betacoronavirus*
Machine Learning*
Coronavirus Infections/*diagnosis
Intensive Care Units/*statistics & numerical data
Pneumonia, Viral/*diagnosis
Algorithms ; Area Under Curve ; Brazil ; Clinical Laboratory Techniques ; Hospitalization ; Humans ; Neural Networks, Computer ; Pandemics ; Predictive Value of Tests ; Public Health Informatics ; ROC Curve ; Respiration, Artificial ; Retrospective Studies ; Sensitivity and Specificity
SCR Disease Name :
COVID-19
SCR Protocol :
COVID-19 diagnostic testing
SCR Organism :
severe acute respiratory syndrome coronavirus 2
Czasopismo naukowe
Tytuł :
Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning.
Autorzy :
Anastasopoulos C; Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Weikert T; Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Yang S; Department of Research and Analysis, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Abdulkadir A; Department of Old Age Psychiatry and Psychotherapy, Universitäre Psychiatrische Dienste Bern (UPD), University of Bern, Bern, Switzerland; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: .
Schmülling L; Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Bühler C; Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Paciolla F; Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Sexauer R; Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Cyriac J; Department of Research and Analysis, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Nesic I; Department of Research and Analysis, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Twerenbold R; COVID-19 Research Coordinator, Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Bremerich J; Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Stieltjes B; Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland; Department of Research and Analysis, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Sauter AW; Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
Sommer G; Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland. Electronic address: .
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Źródło :
European journal of radiology [Eur J Radiol] 2020 Oct; Vol. 131, pp. 109233. Date of Electronic Publication: 2020 Aug 28.
Typ publikacji :
Journal Article
Journal Info :
Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 8106411 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7727 (Electronic) Linking ISSN: 0720048X NLM ISO Abbreviation: Eur J Radiol Subsets: MEDLINE
MeSH Terms :
Betacoronavirus*
Machine Learning*
Software*
Coronavirus Infections/*diagnostic imaging
Pneumonia, Viral/*diagnostic imaging
Humans ; Neural Networks, Computer ; Pandemics ; Tomography, X-Ray Computed/methods
SCR Disease Name :
COVID-19
SCR Organism :
severe acute respiratory syndrome coronavirus 2
Czasopismo naukowe
Tytuł :
Application of machine learning algorithms to identify cryptic reproductive habitats using diverse information sources.
Autorzy :
Brownscombe JW; Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, 1125 Colonel by Drive, Ottawa, ON, K1S 5B6, Canada. .; Department of Biology, Dalhousie University, 1355 Oxford Street, Halifax, NS, B4H 4R2, Canada. .
Griffin LP; Department of Environmental Conservation, University of Massachusetts Amherst, 160 Holdsworth Way, Amherst, MA, 01003, USA.
Morley D; Florida Fish and Wildlife Conservation Commission, 2796 Overseas Highway, Suite 119, Marathon, FL, 33050, USA.
Acosta A; Florida Fish and Wildlife Conservation Commission, 2796 Overseas Highway, Suite 119, Marathon, FL, 33050, USA.
Hunt J; Florida Fish and Wildlife Conservation Commission, 2796 Overseas Highway, Suite 119, Marathon, FL, 33050, USA.
Lowerre-Barbieri SK; Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute, 100 8th Avenue Southeast, St. Petersburg, FL, 33701, USA.; Fisheries and Aquatic Science Program, School of Forest Resources and Conservation, University of Florida, 7922 Northwest 71st Street, Gainesville, FL, 32653-3071, USA.
Adams AJ; Bonefish and Tarpon Trust, 135 San Lorenzo Ave., Suite 860, Coral Gables, FL, 33146, USA.; Florida Atlantic University Harbor Branch Oceanographic Institute, 5600 North Highway A1A, Fort Pierce, FL, USA.
Danylchuk AJ; Department of Environmental Conservation, University of Massachusetts Amherst, 160 Holdsworth Way, Amherst, MA, 01003, USA.
Cooke SJ; Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, 1125 Colonel by Drive, Ottawa, ON, K1S 5B6, Canada.
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Źródło :
Oecologia [Oecologia] 2020 Oct; Vol. 194 (1-2), pp. 283-298. Date of Electronic Publication: 2020 Oct 01.
Typ publikacji :
Journal Article
Journal Info :
Publisher: Springer Country of Publication: Germany NLM ID: 0150372 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1939 (Electronic) Linking ISSN: 00298549 NLM ISO Abbreviation: Oecologia Subsets: MEDLINE
MeSH Terms :
Ecosystem*
Machine Learning*
Algorithms ; Animals ; Florida ; Reproduction
Czasopismo naukowe
Tytuł :
Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients.
Autorzy :
Sharma S; Department of Engineering and Computing, Institute of Advanced Research, Gandhinagar, India. .
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Źródło :
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2020 Oct; Vol. 27 (29), pp. 37155-37163. Date of Electronic Publication: 2020 Jul 22.
Typ publikacji :
Journal Article
Journal Info :
Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE
MeSH Terms :
Coronavirus Infections*
Machine Learning*
Pandemics*
Pneumonia, Viral*
Tomography, X-Ray Computed*
Betacoronavirus ; China ; Humans ; India ; Italy ; Moscow
SCR Disease Name :
COVID-19
SCR Organism :
severe acute respiratory syndrome coronavirus 2
Czasopismo naukowe
Tytuł :
[Multimodal recognition of pain intensity and pain modality with machine learning].
Transliterated Title :
Multimodale Erkennung von Schmerzintensität und -modalität mit maschinellen Lernverfahren.
Autorzy :
Walter S; Sektion Medizinische Psychologie, Klinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Ulm, Frauensteige 6, 89075, Ulm, Deutschland. .
Al-Hamadi A; Fachgebiet Neuro-Informationstechnik, Institut für Informations- und Kommunikationstechnik, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Deutschland.
Gruss S; Sektion Medizinische Psychologie, Klinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Ulm, Frauensteige 6, 89075, Ulm, Deutschland.
Frisch S; Sektion Medizinische Psychologie, Klinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Ulm, Frauensteige 6, 89075, Ulm, Deutschland.; Praxis für Neurologie und Psychiatrie, Leutkirch im Allgäu, Deutschland.
Traue HC; Sektion Medizinische Psychologie, Klinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Ulm, Frauensteige 6, 89075, Ulm, Deutschland.
Werner P; Fachgebiet Neuro-Informationstechnik, Institut für Informations- und Kommunikationstechnik, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Deutschland.
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Źródło :
Schmerz (Berlin, Germany) [Schmerz] 2020 Oct; Vol. 34 (5), pp. 400-409.
Typ publikacji :
Journal Article
Journal Info :
Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 8906258 Publication Model: Print Cited Medium: Internet ISSN: 1432-2129 (Electronic) Linking ISSN: 0932433X NLM ISO Abbreviation: Schmerz Subsets: MEDLINE
MeSH Terms :
Artificial Intelligence*
Machine Learning*
Pain*/diagnosis
Pain Measurement*
Algorithms ; Humans
Czasopismo naukowe
Tytuł :
Leveraging TCGA gene expression data to build predictive models for cancer drug response.
Autorzy :
Clayton EA; Integrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
Pujol TA; School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
McDonald JF; Integrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
Qiu P; Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 950 Atlantic Dr NW, 30332-0230, Atlanta, GA, 404-385-1656, USA. .
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Źródło :
BMC bioinformatics [BMC Bioinformatics] 2020 Sep 30; Vol. 21 (Suppl 14), pp. 364. Date of Electronic Publication: 2020 Sep 30.
Typ publikacji :
Journal Article
Journal Info :
Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE
MeSH Terms :
Machine Learning*
Antineoplastic Agents/*pharmacology
Gene Expression Regulation, Neoplastic/*drug effects
Antineoplastic Agents/therapeutic use ; Area Under Curve ; Cluster Analysis ; Databases, Genetic ; Deoxycytidine/analogs & derivatives ; Deoxycytidine/pharmacology ; Deoxycytidine/therapeutic use ; Fluorouracil/therapeutic use ; Humans ; Neoplasms/drug therapy ; ROC Curve
Czasopismo naukowe
Tytuł :
Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.
Autorzy :
Li WT; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Ma J; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Shende N; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Castaneda G; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Chakladar J; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Tsai JC; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Apostol L; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Honda CO; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Xu J; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Wong LM; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Zhang T; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Lee A; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Gnanasekar A; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Honda TK; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA.; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Kuo SZ; Department of Medicine, Columbia University Medical Center, New York, NY, 10032, USA.
Yu MA; Department of Internal Medicine, Emory University School of Medicine, Atlanta, GA, 30322, USA.
Chang EY; Department of Radiology, University of California San Diego, San Diego, CA, 92093, USA.; Radiology Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Rajasekaran MR; Department of Urology, University of California San Diego, San Diego, CA, 92093, USA.; Urology Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
Ongkeko WM; Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, UC San Diego School of Medicine, San Diego, CA, 92093, USA. .; Research Service, VA San Diego Healthcare System, San Diego, CA, 92161, USA. .
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Źródło :
BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2020 Sep 29; Vol. 20 (1), pp. 247. Date of Electronic Publication: 2020 Sep 29.
Typ publikacji :
Journal Article; Meta-Analysis; Research Support, Non-U.S. Gov't; Systematic Review
Journal Info :
Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE
MeSH Terms :
Machine Learning*
Clinical Laboratory Techniques/*methods
Coronavirus Infections/*diagnosis
Influenza, Human/*diagnosis
Pneumonia, Viral/*diagnosis
Betacoronavirus ; Computer Simulation ; Coronavirus Infections/classification ; Datasets as Topic ; Diagnosis, Differential ; Female ; Humans ; Influenza A virus ; Male ; Pandemics/classification ; Pneumonia, Viral/classification ; Sensitivity and Specificity
SCR Disease Name :
COVID-19
SCR Protocol :
COVID-19 diagnostic testing
SCR Organism :
severe acute respiratory syndrome coronavirus 2
Czasopismo naukowe
Tytuł :
Novel scaffold of natural compound eliciting sweet taste revealed by machine learning.
Autorzy :
Bouysset C; Université Côte d'Azur, CNRS, Institut de Chimie de Nice UMR7272, 06108 Nice, France.
Belloir C; INRAE, CNRS, Université de Bourgogne-Franche Comté, AgroSup Dijon, Centre des Sciences du Goût et de l'Alimentation, 21000 Dijon, France.
Antonczak S; Université Côte d'Azur, CNRS, Institut de Chimie de Nice UMR7272, 06108 Nice, France.
Briand L; INRAE, CNRS, Université de Bourgogne-Franche Comté, AgroSup Dijon, Centre des Sciences du Goût et de l'Alimentation, 21000 Dijon, France.
Fiorucci S; Université Côte d'Azur, CNRS, Institut de Chimie de Nice UMR7272, 06108 Nice, France. Electronic address: .
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Źródło :
Food chemistry [Food Chem] 2020 Sep 15; Vol. 324, pp. 126864. Date of Electronic Publication: 2020 Apr 18.
Typ publikacji :
Journal Article
Journal Info :
Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 7702639 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-7072 (Electronic) Linking ISSN: 03088146 NLM ISO Abbreviation: Food Chem Subsets: MEDLINE
MeSH Terms :
Machine Learning*
Sweetening Agents/*analysis
Taste/*physiology
Humans ; Receptors, G-Protein-Coupled/agonists ; Receptors, G-Protein-Coupled/metabolism
Czasopismo naukowe

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