Search Results - "Sample classification"

Building flexible and robust analysis frameworks for molecular subtyping of cancers

Authors: Christina Bligaard Pedersen Benito Campos Lasse Rene et al.

Source: Mol Oncol
Molecular Oncology, Vol 18, Iss 3, Pp 606-619 (2024)
Pedersen, C B, Campos, B, Rene, L, Wegener, H S, Krishnan, N M, Panda, B, Vitting-Seerup, K, Rossing, M, Bagger, F O & Olsen, L R 2024, ' Building flexible and robust analysis frameworks for molecular subtyping of cancers ', Molecular Oncology, vol. 18, no. 3, pp. 606-619 . https://doi.org/10.1002/1878-0261.13580

Subject Terms: 0301 basic medicine, 0303 health sciences, Gene Expression Profiling, Sample classification, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Computational Biology, sample classification, Breast Neoplasms, bioinformatics workflows, Clinical bioinformatics, 3. Good health, Gene Expression Regulation, Neoplastic, molecular subtyping, 03 medical and health sciences, Bioinformatics workflows, Humans, RNA, Female, name=SDG 3 - Good Health and Well-being, Molecular subtyping, clinical bioinformatics, RC254-282, Research Articles

File Description: application/pdf

Access URL: https://pubmed.ncbi.nlm.nih.gov/38158740
https://doaj.org/article/91af8075ca0347618bf363e33adca13d
https://orbit.dtu.dk/en/publications/26c5857a-2c8c-46d1-aec5-bd093285566b

Hyperspectral Image Classification Based on Multiscale Feature Search Graph Convolutional Network With Meta Pseudolabels

Authors: Suyi Li Ke Wu Huize Liu et al.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 23485-23504 (2025)

Subject Terms: Graph convolutional network (GCN), hyperspectral image classification (HSIC), limited sample classification, meta pseudolabel (MPL), semisupervised learning, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/11134039/; https://doaj.org/toc/1939-1404; https://doaj.org/toc/2151-1535

Access URL: https://doaj.org/article/02720c2601b54a868dbc1c4c3823a34d

Color Space Comparison of Isolated Cervix Cells for Morphology Classification

Authors: Irari Jiménez-López José E. Valdez-Rodríguez Marco A. Moreno-Armendáriz

Source: AI, Vol 6, Iss 10, p 261 (2025)

Subject Terms: cytology sample classification, color space comparison, deep learning, Electronic computers. Computer science, QA75.5-76.95

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Relation: https://www.mdpi.com/2673-2688/6/10/261; https://doaj.org/toc/2673-2688

Access URL: https://doaj.org/article/b30753ddcd41496391c80ebfab46a6cf

A Metareinforcement-Learning-Based Hyperspectral Image Classification With a Small Sample Set

Authors: Prince Yaw Owusu Amoako Guo Cao Di Yang et al.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 3091-3107 (2024)

Subject Terms: Ocean engineering, Hyperspectral image (HSI), metareinforcement learning (Meta-RL), QC801-809, Geophysics. Cosmic physics, 11. Sustainability, 0211 other engineering and technologies, 0207 environmental engineering, 02 engineering and technology, small sample classification, TC1501-1800

Access URL: https://doaj.org/article/c9767aeca0474a668a0ccaf6ad7698e0

BLSF: Adaptive Learning for Small-Sample Medical Data With Broad Learning System Forest Integration

Authors: Dimas Chaerul Ekty Saputra Khamron Sunat Tri Ratnaningsih

Source: IEEE Access, Vol 12, Pp 180844-180863 (2024)

Subject Terms: small-sample classification, feature mapping, Electrical engineering. Electronics. Nuclear engineering, adaptive learning, Broad learning system forest, enhanced predictive modeling, medical data classification, TK1-9971

Access URL: https://doaj.org/article/067e24d13f274a4c936d3fa85d953651

Radial-Hierarchical Chromatomimetic E‑Nose for Spatiotemporal VOC Diffusion Mapping

Authors: Xingchun Zhai Junjie Li Weiwei Cheng et al.

Subject Terms: Science Policy, Space Science, Environmental Sciences not elsewhere classified, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, volatile organic compound, planar spatial variance, intelligent signal processing, integrated microchamber paper, enabling precise discrimination, discriminating diverse vocs, authentic tobacco samples, achieving unprecedented resolution, level classification accuracy, based chromatomimetic e, complex voc mixtures, fidelity voc analytics, sample classification, binary mixtures, advance e, work establishes, study introduces, sensing plane, scalable blueprint, g ), driven design, analysis via

Availability: https://doi.org/10.1021/acs.analchem.5c04223.s001
https://figshare.com/articles/journal_contribution/Radial-Hierarchical_Chromatomimetic_E_Nose_for_Spatiotemporal_VOC_Diffusion_Mapping/30006520

Orthogonal Capsule Network with Meta-Reinforcement Learning for Small Sample Hyperspectral Image Classification

Authors: Prince Yaw Owusu Amoako Guo Cao Boshan Shi et al.

Source: Remote Sensing ; Volume 17 ; Issue 2 ; Pages: 215

Subject Terms: hyperspectral image, capsule network, orthogonal layer, small sample classification

Subject Geographic: agris

File Description: application/pdf

Relation: https://dx.doi.org/10.3390/rs17020215

Availability: https://doi.org/10.3390/rs17020215

A Multi-Scale Feature Attention Image Recognition Algorithm

Authors: MingYuan Xin Ling Weay Ang Sellappan Palaniappan

Source: Journal of Informatics and Web Engineering, Vol 2, Iss 2, Pp 1-7 (2023)

Subject Terms: small-sample classification, meta-learning, multi-scale, feature extraction, Electronic computers. Computer science, QA75.5-76.95, Information technology, attention mechanism, T58.5-58.64

Access URL: https://doaj.org/article/bc453c93fa6344189734173df5030e47

Transformation-Based Data Synthesis for Limited Sample Scenario

Authors: Chang-Hwa Lee Sang Wan Lee

Source: IEEE Access, Vol 12, Pp 184841-184852 (2024)

Subject Terms: Image synthesis, small sample classification, computer vision, deep learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/10781377/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/058a4a6afc79497587b09dcb73ace66e

On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples

Authors: Machicao, Jeaneth Craighero, Francesco Maspero, Davide et al.

Source: Curr Genomics
Current genomics (Online) 22 (2021): 88–97. doi:10.2174/1389202922666210301084151
info:cnr-pdr/source/autori:Machicao J.; Craighero F.; Maspero D.; Angaroni F.; Damiani C.; Graudenzi A.; Antoniotti M.; Bruno O.M./titolo:On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples/doi:10.2174%2F1389202922666210301084151/rivista:Current genomics (Online)/anno:2021/pagina_da:88/pagina_a:97/intervallo_pagine:88–97/volume:22

Subject Terms: 0301 basic medicine, 03 medical and health sciences, machine learning, 0206 medical engineering, network pruning, Metabolic networks, 02 engineering and technology, RNA-seq data, topological properties, Cancer sample classification, Machine learning, Network pruning, Topological properties, Article, cancer sample classification, 3. Good health

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Access URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188584
https://pubmed.ncbi.nlm.nih.gov/34220296
https://pubmed.ncbi.nlm.nih.gov/34220296/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188584
https://idp.eurekaselect.com/191876/article
https://boa.unimib.it/handle/10281/314097
https://www.eurekaselect.com/191876/article
https://www.eurekaselect.com/191876/article
https://hdl.handle.net/20.500.14243/397742
https://doi.org/10.2174/1389202922666210301084151
https://hdl.handle.net/10281/314097
https://doi.org/10.2174/1389202922666210301084151

Hyperspectral imaging for classification of bulk grain samples with deep convolutional neural networks

Authors: Erik Schou Dreier Klavs Martin Sorensen Toke Lund-Hansen et al.

Source: Journal of Near Infrared Spectroscopy. 30:107-121

Subject Terms: Hyperspectral imaging, near infrared spectroscopy, bulk grain sample classification, PREDICTION, convolutional neural networks, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, artificial intelligence, VARIETIES

Refining biome labeling for large-scale microbial community samples: Leveraging neural networks and transfer learning

Authors: Nan Wang Teng Wang Kang Ning

Source: Environmental Science and Ecotechnology, Vol 17, Iss , Pp 100304- (2024)

Subject Terms: Microbial community, Transfer learning, Sample classification, Environmental scientific research, Novel knowledge discovery, Environmental sciences, GE1-350, Environmental technology. Sanitary engineering, TD1-1066

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Relation: http://www.sciencedirect.com/science/article/pii/S2666498423000698; https://doaj.org/toc/2666-4984

Access URL: https://doaj.org/article/f8ef9c067d264761863dba19ef40e69f

Combined Metabolomic and NIRS Analyses Reveal Biochemical and Metabolite Changes in Goat Milk Kefir under Different Heat Treatments and Fermentation Times

Authors: Sánchez-Rodríguez, Rubén Terriente-Palacios, Carlos García-Olmo, Juan et al.

Contributors: Sánchez-Rodríguez, Rubén Terriente-Palacios, Carlos García-Olmo, Juan et al.

Subject Terms: fermented milk, metabolites, metabolome, near-infrared spectroscopy, sample classification

Relation: #PLACEHOLDER_PARENT_METADATA_VALUE#; AGR-164 group; Central Facilities for Research Support (SCAI) of the University of Córdoba; GC-ToF-MS was performed at the Metabolomics Unit of the IHSM-CSIC-UMA; Biomolecules; https://www.mdpi.com/2218-273X/14/7/816; Sí; https://hdl.handle.net/10261/370014; https://api.elsevier.com/content/abstract/scopus_id/85199600819

Availability: https://hdl.handle.net/10261/370014
https://doi.org/10.3390/biom14070816
https://api.elsevier.com/content/abstract/scopus_id/85199600819

Evaluating Bioinformatics Processing of Somatic Variant Detection in cfDNA Using Targeted Sequencing with UMIs

Authors: Lin, Yixin Rasmussen, Mads Heilskov Christensen, Mikkel Hovden et al.

Source: Lin, Y, Rasmussen, M H, Christensen, M H, Frydendahl, A, Maretty, L, Andersen, C L & Besenbacher, S 2024, 'Evaluating Bioinformatics Processing of Somatic Variant Detection in cfDNA Using Targeted Sequencing with UMIs', International Journal of Molecular Sciences , vol. 25, no. 21, 11439. https://doi.org/10.3390/ijms252111439

Subject Terms: benchmarking, cancer sample classification, cell-free DNA, low-frequency variant calling, UMI sequencing

Availability: https://pure.au.dk/portal/en/publications/d8e70511-4b66-4626-98bd-3facfd21bdf5
https://doi.org/10.3390/ijms252111439
http://www.scopus.com/inward/record.url?scp=85208548854&partnerID=8YFLogxK

OSLNet: Deep Small-Sample Classification With an Orthogonal Softmax Layer

Authors: Xiaoxu, Li Dongliang, Chang Zhanyu, Ma et al.

Source: Li, X, Chang, D, Ma, Z, Tan, Z-H, Xue, J-H, Cao, J, Yu, J & Guo, J 2020, 'OSLNet : Deep Small-Sample Classification with an Orthogonal Softmax Layer', I E E E Transactions on Image Processing, vol. 29, 9088302, pp. 6482-6495. https://doi.org/10.1109/TIP.2020.2990277

Subject Terms: Orthogonal softmax layer, FOS: Computer and information sciences, small-sample classification, overfitting, Small-sample classification, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, Overfitting, 02 engineering and technology, Deep neural network

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Access URL: http://arxiv.org/pdf/2004.09033
https://pubmed.ncbi.nlm.nih.gov/32386152
http://arxiv.org/abs/2004.09033
https://vbn.aau.dk/da/publications/316f0b17-2610-479f-a8de-5de0f256d056
https://vbn.aau.dk/ws/files/337140945/OSLNet_Deep_Small_Sample_Classification_with_an_Orthogonal_Softmax_Layer.pdf
https://doi.org/10.1109/TIP.2020.2990277
http://www.scopus.com/inward/record.url?scp=85087547443&partnerID=8YFLogxK
https://arxiv.org/pdf/2004.09033.pdf
https://arxiv.org/abs/2004.09033
https://ieeexplore.ieee.org/document/9088302
https://www.arxiv-vanity.com/papers/2004.09033/
https://pubmed.ncbi.nlm.nih.gov/32386152/
https://dblp.uni-trier.de/db/journals/corr/corr2004.html#abs-2004-09033
https://arxiv.org/pdf/2004.09033
https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/122198
https://discovery-pp.ucl.ac.uk/id/eprint/10096964/

More appropriate DenseNetBL classifier for small sample tree species classification using UAV-based RGB imagery

Authors: Ni Wang Tao Pu Yali Zhang et al.

Source: Heliyon, Vol 9, Iss 10, Pp e20467- (2023)

Subject Terms: DenseNetBL, Forests tree species classification, Small sample classification, SLIC, Science (General), Q1-390, Social sciences (General), H1-99

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2405844023076752; https://doaj.org/toc/2405-8440

Access URL: https://doaj.org/article/05396edcc084402ab62535ac89256c5c

Kernel difference maximisation‐based sparse representation for more accurate face recognition

Authors: Zhongwei Cui Zhao Jianchuan Lian Wu et al.

Source: The Journal of Engineering (2020)

Subject Terms: optimisation, feature extraction, l(2) regularisation-based sparse solution, training sample linear combinations, 02 engineering and technology, Engineering (General). Civil engineering (General), feature space, novel kernel difference maximisation-based sparse representation method, 03 medical and health sciences, 0302 clinical medicine, test sample classification, 0202 electrical engineering, electronic engineering, information engineering, learning (artificial intelligence), image representation, TA1-2040, face recognition, image classification

Access URL: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/joe.2019.1003
https://doaj.org/article/9a00b410819f4b458032b5a25c1c8fef
https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1003
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/joe.2019.1003

Computational advances of tumor marker selection and sample classification in cancer proteomics

Authors: Yunqing Qiu Yi Li Yan Lou et al.

Source: Comput Struct Biotechnol J
Computational and Structural Biotechnology Journal, Vol 18, Iss, Pp 2012-2025 (2020)

Subject Terms: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Tumor marker selection, Cancer proteomics, Computational methods, Sample classification, Review Article, TP248.13-248.65, Biotechnology, 3. Good health

Access URL: https://pubmed.ncbi.nlm.nih.gov/32802273
https://doaj.org/article/ec7c83a0929542f08756f0b657e2dd9b
https://www.sciencedirect.com/science/article/abs/pii/S200103702030341X
https://pubmed.ncbi.nlm.nih.gov/32802273/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403885
https://www.sciencedirect.com/science/article/pii/S200103702030341X

Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders

Authors: Marta F. Nabais Simon M. Laws Tian Lin et al.

Source: Genome Biology, Vol 22, Iss 1, Pp 1-30 (2021)

Subject Terms: Neurodegenerative disorders, DNA methylation, Mixed-linear models, Methylation profile score, Out-of-sample classification, Inflammatory markers, Biology (General), QH301-705.5, Genetics, QH426-470

File Description: electronic resource

Relation: https://doaj.org/toc/1474-760X

Access URL: https://doaj.org/article/201eefeb04d04cf0871060b72f55eedd

MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning

Authors: Eliza Dhungel Yassin Mreyoud Ho-Jin Gwak et al.

Source: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-12 (2021)

Subject Terms: Metagenomics, Machine learning, R-package, Phenotype prediction, Sample classification, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

File Description: electronic resource

Relation: https://doaj.org/toc/1471-2105

Access URL: https://doaj.org/article/633391f64b294c45a88a6ce22d5cd37b