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Tytuł:
CPSS: Fusing consistency regularization and pseudo-labeling techniques for semi-supervised deep cardiovascular disease detection using all unlabeled electrocardiograms.
Autorzy:
Shi J; School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
Liu W; School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
Zhang H; School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
Chang S; School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
Wang H; School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
He J; School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
Huang Q; School of Physics and Technology, Wuhan University, Wuhan, 430072, China. Electronic address: .
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Źródło:
Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2024 Sep; Vol. 254, pp. 108315. Date of Electronic Publication: 2024 Jul 04.
Typ publikacji:
Journal Article
MeSH Terms:
Electrocardiography*/methods
Cardiovascular Diseases*/diagnosis
Algorithms*
Supervised Machine Learning*
Deep Learning*
Humans ; Unsupervised Machine Learning ; Databases, Factual
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping.
Autorzy:
Nasiri Khiavi A; Department of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, 46414-356, Iran.
Vafakhah M; Department of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, 46414-356, Iran. .
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Źródło:
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Aug; Vol. 31 (40), pp. 52740-52757. Date of Electronic Publication: 2024 Aug 19.
Typ publikacji:
Journal Article
MeSH Terms:
Floods*
Algorithms*
Support Vector Machine*
Supervised Machine Learning*
Game Theory*
Iran ; Machine Learning
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity.
Autorzy:
Mansouri K; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Taylor K; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Auerbach S; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Ferguson S; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Frawley R; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Hsieh JH; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Jahnke G; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Kleinstreuer N; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Mehta S; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Moreira-Filho JT; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Parham F; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Rider C; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Rooney AA; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Wang A; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Sutherland V; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
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Źródło:
Environmental health perspectives [Environ Health Perspect] 2024 Aug; Vol. 132 (8), pp. 85002. Date of Electronic Publication: 2024 Aug 06.
Typ publikacji:
Journal Article
MeSH Terms:
Machine Learning*
Cluster Analysis ; Unsupervised Machine Learning ; Toxicology/methods ; Algorithms
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
DREAMER: a computational framework to evaluate readiness of datasets for machine learning.
Autorzy:
Ahangaran M; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Zhu H; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Li R; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Yin L; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Jang J; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Chaudhry AP; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Farrer LA; Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.; Boston University Alzheimer's Disease Research Center, Boston, MA, USA.; The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Au R; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.; Boston University Alzheimer's Disease Research Center, Boston, MA, USA.; The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Kolachalama VB; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA. .; Boston University Alzheimer's Disease Research Center, Boston, MA, USA. .; Department of Computer Science, Boston University, Boston, MA, USA. .; Faculty of Computing & Data Sciences, Boston University, Boston, MA, 02215, USA. .
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Źródło:
BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Jun 04; Vol. 24 (1), pp. 152. Date of Electronic Publication: 2024 Jun 04.
Typ publikacji:
Journal Article
MeSH Terms:
Machine Learning*
Humans ; Datasets as Topic ; Unsupervised Machine Learning ; Algorithms ; Supervised Machine Learning ; Software
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction.
Autorzy:
Kim E; Institute of Life Sciences & Resources and Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea.
Yang SM; Institute of Life Sciences & Resources and Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea.
Ham JH; Institute of Life Sciences & Resources and Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea.
Lee W; Institute of Life Sciences & Resources and Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea.
Jung DH; Department of Smart Farm Science, Kyung Hee University, Yongin 17104, Republic of Korea.
Kim HY; Institute of Life Sciences & Resources and Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea. Electronic address: .
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Źródło:
Food chemistry [Food Chem] 2025 Jan 01; Vol. 462, pp. 140931. Date of Electronic Publication: 2024 Aug 20.
Typ publikacji:
Journal Article; Comparative Study
MeSH Terms:
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization*/methods
Enterococcus*/classification
Enterococcus*/chemistry
Enterococcus*/isolation & purification
Enterococcus*/genetics
Supervised Machine Learning*
Algorithms ; Support Vector Machine ; Bacterial Typing Techniques/methods ; Machine Learning
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Immune-based Machine learning Prediction of Diagnosis and Illness State in Schizophrenia and Bipolar Disorder.
Autorzy:
Skorobogatov K; Scientific Initiative for Neuropsychiatric and Psychopharmacological Studies (SINAPS), University Psychiatric Hospital Campus Duffel (UPCD), Rooienberg 19, 2570 Duffel, Belgium; Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Campus Drie Eiken, S.003, Universiteitsplein 1, 2610 Wilrijk, Belgium. Electronic address: .
De Picker L; Scientific Initiative for Neuropsychiatric and Psychopharmacological Studies (SINAPS), University Psychiatric Hospital Campus Duffel (UPCD), Rooienberg 19, 2570 Duffel, Belgium; Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Campus Drie Eiken, S.003, Universiteitsplein 1, 2610 Wilrijk, Belgium.
Wu CL; Université Paris Est Créteil (UPEC), Inserm U955, IMRB Translational Neuropsychiatry Laboratory, AP-HP, Hôpitaux Universitaires H Mondor, DMU IMPACT, FHU ADAPT, Fondation FondaMental, Créteil, France.
Foiselle M; Université Paris Est Créteil (UPEC), Inserm U955, IMRB Translational Neuropsychiatry Laboratory, AP-HP, Hôpitaux Universitaires H Mondor, DMU IMPACT, FHU ADAPT, Fondation FondaMental, Créteil, France.
Richard JR; Université Paris Est Créteil (UPEC), Inserm U955, IMRB Translational Neuropsychiatry Laboratory, AP-HP, Hôpitaux Universitaires H Mondor, DMU IMPACT, FHU ADAPT, Fondation FondaMental, Créteil, France.
Boukouaci W; Université Paris Est Créteil (UPEC), Inserm U955, IMRB Translational Neuropsychiatry Laboratory, AP-HP, Hôpitaux Universitaires H Mondor, DMU IMPACT, FHU ADAPT, Fondation FondaMental, Créteil, France.
Bouassida J; Université Paris Est Créteil (UPEC), Inserm U955, IMRB Translational Neuropsychiatry Laboratory, AP-HP, Hôpitaux Universitaires H Mondor, DMU IMPACT, FHU ADAPT, Fondation FondaMental, Créteil, France.
Laukens K; Biomedical Informatics Research Center Antwerp (BIOMINA), University of Antwerp, Campus Middelheim, M.G.111, Middelheimlaan 1, 2020 Antwerp, Belgium; Department of Mathematics and Computer Science, University of Antwerp, Campus Middelheim, M.G.105, Antwerp, Belgium.
Meysman P; Biomedical Informatics Research Center Antwerp (BIOMINA), University of Antwerp, Campus Middelheim, M.G.111, Middelheimlaan 1, 2020 Antwerp, Belgium; Department of Mathematics and Computer Science, University of Antwerp, Campus Middelheim, M.G.105, Antwerp, Belgium.
le Corvoisier P; Inserm, Centre d'Investigation Clinique 1430, AP-HP, Hôpital Henri Mondor, Université Paris Est Créteil, Faculté de Médecine de Créteil 8, Rue Du Général Sarrail 94010, Créteil, France.
Barau C; Plateforme de Ressources Biologiques, Hôpital Henri Mondor, 51 Avenue due Maréchal de Lattre de Tassigny, 94010 Créteil, France.
Morrens M; Scientific Initiative for Neuropsychiatric and Psychopharmacological Studies (SINAPS), University Psychiatric Hospital Campus Duffel (UPCD), Rooienberg 19, 2570 Duffel, Belgium; Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Campus Drie Eiken, S.003, Universiteitsplein 1, 2610 Wilrijk, Belgium.
Tamouza R; Université Paris Est Créteil (UPEC), Inserm U955, IMRB Translational Neuropsychiatry Laboratory, AP-HP, Hôpitaux Universitaires H Mondor, DMU IMPACT, FHU ADAPT, Fondation FondaMental, Créteil, France.
Leboyer M; Université Paris Est Créteil (UPEC), Inserm U955, IMRB Translational Neuropsychiatry Laboratory, AP-HP, Hôpitaux Universitaires H Mondor, DMU IMPACT, FHU ADAPT, Fondation FondaMental, Créteil, France.
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Źródło:
Brain, behavior, and immunity [Brain Behav Immun] 2024 Nov; Vol. 122, pp. 422-432. Date of Electronic Publication: 2024 Aug 14.
Typ publikacji:
Journal Article
MeSH Terms:
Schizophrenia*/diagnosis
Schizophrenia*/blood
Schizophrenia*/immunology
Bipolar Disorder*/diagnosis
Bipolar Disorder*/immunology
Bipolar Disorder*/blood
Cytokines*/blood
Kynurenine*/blood
Machine Learning*
Biomarkers*/blood
Humans ; Male ; Female ; Adult ; Cross-Sectional Studies ; Middle Aged ; Supervised Machine Learning ; Tryptophan/blood ; Tryptophan/metabolism
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Supervised machine learning for the prediction of post-operative clinical outcomes of hip and knee replacements: a review.
Autorzy:
Ghadirinejad K; The Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, South Australia, Australia.
Milimonfared R; The Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, South Australia, Australia.
Taylor M; The Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, South Australia, Australia.
Solomon LB; Department of Orthopaedics and Trauma, Royal Adelaide Hospital, Adelaide, South Australia, Australia.; Centre for Orthopaedic & Trauma Research, University of Adelaide, Adelaide, South Australia, Australia.
Graves S; Department of Surgery, Epworth HealthCare, The University of Melbourne, Parkville, Victoria, Australia.
Pratt N; The Australian Orthopaedic Association National Joint Replacement Registry, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
de Steiger R; Quality Use of Medicines and Pharmacy Research Centre, School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia.; Department of Surgery, Epworth HealthCare, The University of Melbourne, Parkville, Victoria, Australia.
Hashemi R; The Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, South Australia, Australia.
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Źródło:
ANZ journal of surgery [ANZ J Surg] 2024 Jul-Aug; Vol. 94 (7-8), pp. 1228-1233. Date of Electronic Publication: 2024 Apr 10.
Typ publikacji:
Journal Article; Review
MeSH Terms:
Arthroplasty, Replacement, Knee*/methods
Arthroplasty, Replacement, Hip*/methods
Supervised Machine Learning*
Humans ; Treatment Outcome ; Machine Learning ; Postoperative Complications/epidemiology
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Construction of prediction models for novel subtypes in patients with arteriosclerosis obliterans undergoing endovascular therapy: an unsupervised machine learning study.
Autorzy:
Li X; Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, No.6 of Shuangyong Road, Nanning, Guangxi, 530021, P. R. China.
Zhang L; Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, No.6 of Shuangyong Road, Nanning, Guangxi, 530021, P. R. China.
Li Q; Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, No.6 of Shuangyong Road, Nanning, Guangxi, 530021, P. R. China.
Zhang J; Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, No.6 of Shuangyong Road, Nanning, Guangxi, 530021, P. R. China.
Qin X; Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, No.6 of Shuangyong Road, Nanning, Guangxi, 530021, P. R. China. dr_.
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Źródło:
Journal of cardiothoracic surgery [J Cardiothorac Surg] 2024 Jun 25; Vol. 19 (1), pp. 370. Date of Electronic Publication: 2024 Jun 25.
Typ publikacji:
Journal Article
MeSH Terms:
Endovascular Procedures*/methods
Unsupervised Machine Learning*
Arteriosclerosis Obliterans*/surgery
Humans ; Female ; Male ; Retrospective Studies ; Aged ; Middle Aged ; Nomograms ; Prognosis ; Machine Learning
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Leveraging permutation testing to assess confidence in positive-unlabeled learning applied to high-dimensional biological datasets.
Autorzy:
Xu S; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, USA.
Ackerman ME; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, USA. .; Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH, USA. .; Thayer School of Engineering, Dartmouth College, 14 Engineering Dr., Hanover, NH, 03755, USA. .
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Źródło:
BMC bioinformatics [BMC Bioinformatics] 2024 Jun 19; Vol. 25 (1), pp. 218. Date of Electronic Publication: 2024 Jun 19.
Typ publikacji:
Journal Article
MeSH Terms:
Machine Learning*
Supervised Machine Learning ; Humans ; Computational Biology/methods ; Algorithms
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification.
Autorzy:
Wang Q; Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden; Department of Radiology, Karolinska University Hospital Huddinge, Stockholm, Sweden.
Nilsson H; Division of Surgery, Department of Clinical Sciences, Karolinska Institutet at Danderyd Hospital, Stockholm, Sweden.
Xu K; Centre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Wei X; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Chen D; Department of Gastroenterology and Hepatology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Zhao D; Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Hu X; Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Wang A; Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Interventional Therapy, People's Hospital of Dianjiang County, Chongqing, China. Electronic address: .
Bai G; Department of Radiology, Tianjin Beichen Traditional Chinese Medicine Hospital, Tianjin, China. Electronic address: guojie_.
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Źródło:
European journal of radiology [Eur J Radiol] 2024 Jun; Vol. 175, pp. 111459. Date of Electronic Publication: 2024 Apr 10.
Typ publikacji:
Journal Article
MeSH Terms:
Colorectal Neoplasms*/pathology
Colorectal Neoplasms*/diagnostic imaging
Liver Neoplasms*/diagnostic imaging
Liver Neoplasms*/secondary
Unsupervised Machine Learning*
Tomography, X-Ray Computed*/methods
Humans ; Male ; Female ; Middle Aged ; Prognosis ; Retrospective Studies ; Aged ; Adult ; Survival Rate ; Aged, 80 and over ; Machine Learning ; Radiomics
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.
Autorzy:
Adhikary S; Spiraldevs Automation Industries Pvt. Ltd., Raiganj, West Bengal, India.
Tiwari SP; King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.
Banerjee S; Wingbiotics, Baghajatin, Kolkata, West Bengal, India.
Dwivedi AD; Cybersecurity Section, Aalborg University, Copenhagen, Denmark.
Rahman SM; King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.
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Źródło:
PeerJ [PeerJ] 2024 May 08; Vol. 12, pp. e17361. Date of Electronic Publication: 2024 May 08 (Print Publication: 2024).
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
Phytoplankton*
Remote Sensing Technology*/methods
Remote Sensing Technology*/instrumentation
Machine Learning*
Oceans and Seas ; Environmental Monitoring/methods ; Supervised Machine Learning
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Applications of machine learning in phylogenetics.
Autorzy:
Mo YK; Department of Computer Science, Indiana University, Bloomington, IN 47405, USA.
Hahn MW; Department of Computer Science, Indiana University, Bloomington, IN 47405, USA; Department of Biology, Indiana University, Bloomington, IN 47405, USA.
Smith ML; Department of Biological Sciences, Mississippi State University, Starkville, MS 39762, USA. Electronic address: .
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Źródło:
Molecular phylogenetics and evolution [Mol Phylogenet Evol] 2024 Jul; Vol. 196, pp. 108066. Date of Electronic Publication: 2024 Mar 31.
Typ publikacji:
Journal Article; Review
MeSH Terms:
Phylogeny*
Machine Learning*
Supervised Machine Learning ; Models, Genetic
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Distribution-free Bayesian regularized learning framework for semi-supervised learning.
Autorzy:
Ma J; School of Mathematics and Information Sciences, North Minzu University, Yinchuan Ningxia 750021, PR China. Electronic address: jun_.
Yu G; School of Mathematics and Information Sciences, North Minzu University, Yinchuan Ningxia 750021, PR China. Electronic address: .
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Źródło:
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2024 Jun; Vol. 174, pp. 106262. Date of Electronic Publication: 2024 Mar 20.
Typ publikacji:
Journal Article
MeSH Terms:
Algorithms*
Supervised Machine Learning*
Bayes Theorem ; Reproducibility of Results ; Machine Learning
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Evaluation metrics and statistical tests for machine learning.
Autorzy:
Rainio O; Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland. .
Teuho J; Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland.
Klén R; Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland.
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Źródło:
Scientific reports [Sci Rep] 2024 Mar 13; Vol. 14 (1), pp. 6086. Date of Electronic Publication: 2024 Mar 13.
Typ publikacji:
Journal Article
MeSH Terms:
Image Processing, Computer-Assisted*/methods
Machine Learning*
Neural Networks, Computer ; Supervised Machine Learning ; Positron-Emission Tomography
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.
Autorzy:
Tutsoy O; Adana Alparslan Turkes Science and Technology University, Adana, Turkey. .
Koç GG; Adana Alparslan Turkes Science and Technology University, Adana, Turkey.
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Źródło:
BMC bioinformatics [BMC Bioinformatics] 2024 Mar 08; Vol. 25 (1), pp. 103. Date of Electronic Publication: 2024 Mar 08.
Typ publikacji:
Journal Article
MeSH Terms:
Algorithms*
Machine Learning*
Supervised Machine Learning
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Segmenting mechanically heterogeneous domains via unsupervised learning.
Autorzy:
Nguyen Q; Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA.
Lejeune E; Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA. .
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Źródło:
Biomechanics and modeling in mechanobiology [Biomech Model Mechanobiol] 2024 Feb; Vol. 23 (1), pp. 349-372. Date of Electronic Publication: 2024 Jan 13.
Typ publikacji:
Journal Article
MeSH Terms:
Unsupervised Machine Learning*
Robotics*/methods
Machine Learning ; Computer Simulation ; Biomechanical Phenomena
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.
Autorzy:
Geuenich MJ; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1×5, Canada. .; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada. .
Gong DW; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1×5, Canada.
Campbell KR; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1×5, Canada. .; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada. .; Department of Statistical Sciences, University of Toronto, Toronto, ON, M5S 3G3, Canada. .; Department of Computer Science, University of Toronto, Toronto, ON, M5T 3A1, Canada. .; Ontario Institute of Cancer Research, Toronto, ON, M5G 1M1, Canada. .; Vector Institute, Toronto, ON, M5G 1M1, Canada. .
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Źródło:
Nature communications [Nat Commun] 2024 Feb 03; Vol. 15 (1), pp. 1014. Date of Electronic Publication: 2024 Feb 03.
Typ publikacji:
Journal Article
MeSH Terms:
Machine Learning*
Algorithms*
Technology ; Awareness ; Supervised Machine Learning ; Single-Cell Analysis
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces.
Autorzy:
Koh RGL
Ribeiro M
Jabban L
Fang B
Nesovic K
Bayat S
Metcalfe BW
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Źródło:
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society [IEEE Trans Neural Syst Rehabil Eng] 2024; Vol. 32, pp. 3689-3698. Date of Electronic Publication: 2024 Oct 07.
Typ publikacji:
Journal Article; Review
MeSH Terms:
Machine Learning*
Peripheral Nerves*/physiology
Neural Networks, Computer*
Algorithms*
Humans ; Supervised Machine Learning ; Unsupervised Machine Learning ; Reinforcement, Psychology
Czasopismo naukowe
Full Text Finder  Link otwiera się w nowym oknieSzczegóły
Tytuł:
MLpronto: A tool for democratizing machine learning.
Autorzy:
Tjaden J; Computer Science Department, Colby College, Waterville, ME, United States of America.
Tjaden B; Department of Computer Science, Wellesley College, Wellesley, MA, United States of America.
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Źródło:
PloS one [PLoS One] 2023 Nov 30; Vol. 18 (11), pp. e0294924. Date of Electronic Publication: 2023 Nov 30 (Print Publication: 2023).
Typ publikacji:
Journal Article
MeSH Terms:
Algorithms*
Machine Learning*
Supervised Machine Learning
Czasopismo naukowe
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Tytuł:
Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.
Autorzy:
Mo Z; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.; School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.
Siepel A; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.; School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.
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Źródło:
PLoS genetics [PLoS Genet] 2023 Nov 07; Vol. 19 (11), pp. e1011032. Date of Electronic Publication: 2023 Nov 07 (Print Publication: 2023).
Typ publikacji:
Journal Article
MeSH Terms:
Machine Learning*
Neural Networks, Computer*
Supervised Machine Learning ; Computer Simulation ; Genetics, Population
Czasopismo naukowe
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Wyświetlanie 1-20 z 557983

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