مكتبة

Item request has been placed!

Item request cannot be made.

Processing Request

Academic Journal

DREAMER: a computational framework to evaluate readiness of datasets for machine learning.

Authors : Ahangaran M; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Zhu H

Subjects: Machine Learning*; Humans ; Datasets as Topic

Source: 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.Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947

تفاصيل العنوان

اقرأ أكثر حفظ في قائمتي

Academic Journal

Leveraging permutation testing to assess confidence in positive-unlabeled learning applied to high-dimensional biological datasets.

Authors : Xu S; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, USA.; Ackerman ME

Subjects: Machine Learning*; Supervised Machine Learning ; Humans

Source: BMC bioinformatics [BMC Bioinformatics] 2024 Jun 19; Vol. 25 (1), pp. 218. Date of Electronic Publication: 2024 Jun 19.Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105

تفاصيل العنوان

اقرأ أكثر حفظ في قائمتي

Academic Journal

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

Authors : Adhikary S; Spiraldevs Automation Industries Pvt. Ltd., Raiganj, West Bengal, India.; Tiwari SP

Subjects: Phytoplankton* ; Remote Sensing Technology*/Remote Sensing Technology*/Remote Sensing Technology*/methods ; Remote Sensing Technology*/Remote Sensing Technology*/Remote Sensing Technology*/instrumentation

Source: PeerJ [PeerJ] 2024 May 08; Vol. 12, pp. e17361. Date of Electronic Publication: 2024 May 08 (Print Publication: 2024).Publisher: PeerJ Inc Country of Publication: United States NLM ID: 101603425 Publication Model: eCollection Cited Medium: Internet ISSN:

تفاصيل العنوان

اقرأ أكثر حفظ في قائمتي

Academic Journal

Evaluation metrics and statistical tests for machine learning.

Authors : Rainio O; Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland. .; Teuho J

Subjects: Image Processing, Computer-Assisted*/Image Processing, Computer-Assisted*/Image Processing, Computer-Assisted*/methods ; Machine Learning*; Neural Networks, Computer

Source: Scientific reports [Sci Rep] 2024 Mar 13; Vol. 14 (1), pp. 6086. Date of Electronic Publication: 2024 Mar 13.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

تفاصيل العنوان

اقرأ أكثر حفظ في قائمتي

Academic Journal

Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.

Authors : Tutsoy O; Adana Alparslan Turkes Science and Technology University, Adana, Turkey. .; Koç GG

Subjects: Algorithms* ; Machine Learning*; Supervised Machine Learning

Source: BMC bioinformatics [BMC Bioinformatics] 2024 Mar 08; Vol. 25 (1), pp. 103. Date of Electronic Publication: 2024 Mar 08.Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105

تفاصيل العنوان

اقرأ على الانترنت اقرأ أكثر حفظ في قائمتي

Academic Journal

The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.

Authors : 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. .

Subjects: Machine Learning* ; Algorithms*; Technology

Source: Nature communications [Nat Commun] 2024 Feb 03; Vol. 15 (1), pp. 1014. Date of Electronic Publication: 2024 Feb 03.Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN:

تفاصيل العنوان

اقرأ أكثر حفظ في قائمتي

Academic Journal

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

Authors : Bomrah S; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No. 291, Zhongzheng Rd, Zhonghe District, New Taipei City, 235, Taiwan.; International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, 235, Taiwan.

Subjects: Sepsis*/Sepsis*/Sepsis*/diagnosis ; Sepsis*/Sepsis*/Sepsis*/therapy ; Machine Learning*/Machine Learning*/Machine Learning*/trends

Source: Critical care (London, England) [Crit Care] 2024 May 28; Vol. 28 (1), pp. 180. Date of Electronic Publication: 2024 May 28.Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 9801902 Publication Model: Electronic Cited Medium: Internet ISSN:

تفاصيل العنوان

اقرأ أكثر حفظ في قائمتي

Academic Journal

MLpronto: A tool for democratizing machine learning.

Authors : Tjaden J; Computer Science Department, Colby College, Waterville, ME, United States of America.; Tjaden B

Subjects: Algorithms* ; Machine Learning*; Supervised Machine Learning

Source: PloS one [PLoS One] 2023 Nov 30; Vol. 18 (11), pp. e0294924. Date of Electronic Publication: 2023 Nov 30 (Print Publication: 2023).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

تفاصيل العنوان

اقرأ على الانترنت اقرأ أكثر حفظ في قائمتي

Academic Journal

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.

Authors : 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.

Subjects: Machine Learning* ; Neural Networks, Computer*; Supervised Machine Learning

Source: PLoS genetics [PLoS Genet] 2023 Nov 07; Vol. 19 (11), pp. e1011032. Date of Electronic Publication: 2023 Nov 07 (Print Publication: 2023).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101239074 Publication Model: eCollection Cited Medium: Internet

تفاصيل العنوان

اقرأ على الانترنت اقرأ أكثر حفظ في قائمتي

Academic Journal

Evaluating the prognostic value of tumor deposits in non-metastatic lymph node-positive colon adenocarcinoma using Cox regression and machine learning.

Authors : Zheng Z; Department of Chemoradiation Oncology, The Affiliated Lihuili Hospital of Ningbo University, 57 Xingning RoadZhejiang Province, Ningbo, China.; Luo H

Subjects: Colonic Neoplasms*/Colonic Neoplasms*/Colonic Neoplasms*/pathology ; Colonic Neoplasms*/Colonic Neoplasms*/Colonic Neoplasms*/mortality ; Machine Learning*

Source: International journal of colorectal disease [Int J Colorectal Dis] 2024 Jun 26; Vol. 39 (1), pp. 97. Date of Electronic Publication: 2024 Jun 26.Publisher: Springer International Country of Publication: Germany NLM ID: 8607899 Publication Model: Electronic Cited Medium: Internet ISSN:

تفاصيل العنوان

اقرأ أكثر حفظ في قائمتي

نتائج البحث

فلترة النتائج

DREAMER: a computational framework to evaluate readiness of datasets for machine learning.

Leveraging permutation testing to assess confidence in positive-unlabeled learning applied to high-dimensional biological datasets.

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

Evaluation metrics and statistical tests for machine learning.

Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.

The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

MLpronto: A tool for democratizing machine learning.

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.

Evaluating the prognostic value of tumor deposits in non-metastatic lymph node-positive colon adenocarcinoma using Cox regression and machine learning.