Výsledky vyhledávání - Autoencoder-based phenotyping

Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers

Autoři: Panagiotis I Sergouniotis Adam Diakite Kumar Gaurav a další

Zdroj: Bioinformatics
Sergouniotis, P I, Diakite, A, Gaurav, K, Birney, E, Fitzgerald, T, Allen, N, Aslam, T, Atan, D, Barman, S, Barrett, J, Bishop, P, Black, G, Braithwaite, T, Carare, R, Chakravarthy, U, Chan, M, Chua, S, Day, A, Desai, P, Dhillon, B, Dick, A, Doney, A, Egan, C, Ennis, S, Foster, P, Fruttiger, M, Gallacher, J, Garway-Heath, D, Gibson, J, Guggenheim, J, Hammond, C, Hardcastle, A, Harding, S, Hogg, R, Hysi, P, Keane, P, Khaw, P T, Khawaja, A, Lascaratos, G, Littlejohns, T, Lotery, A, Luben, R, Luthert, P, Macgillivray, T, Mcguinness, B, Mckay, G, Paterson, E, Peto, T, Steel, D, Woodside, J & UK Biobank Eye and Vision Consortium 2025, 'Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers', Bioinformatics, vol. 41, no. 1, btae732. https://doi.org/10.1093/bioinformatics/btae732
Sergouniotis, P, Diakite, A, Gaurav, K, Birney, E & Fitzgerald, T 2024, 'Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers', Bioinformatics. https://doi.org/10.1101/2023.06.15.23291410, https://doi.org/10.1093/bioinformatics/btae732

Témata: 0301 basic medicine, name=Computational Theory and Mathematics, 0206 medical engineering, 02 engineering and technology, 03 medical and health sciences, ophthalmic images, name=Biochemistry, Autoencoder-based phenotyping, name=Computer Science Applications, Original Paper, name=Molecular Biology, genetic loci, Autoencoder, name=Computational Mathematics, 3. Good health, name=Statistics and Probability

Popis souboru: application/pdf; text

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/39657956
https://research.manchester.ac.uk/en/publications/b48c3db7-6708-4450-b42b-657076674ecd
https://doi.org/10.1101/2023.06.15.23291410
https://pure.amsterdamumc.nl/en/publications/c8b99b28-759c-4b73-958b-2bf26800be7f
https://doi.org/10.1093/bioinformatics/btae732
https://pure.qub.ac.uk/en/publications/8e1bd91d-c990-4881-8a10-c693f4bcfe25

Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers

Autoři: Sergouniotis, Panagiotis I. Diakite, Adam Gaurav, Kumar a další

Zdroj: Sergouniotis, P I, Diakite, A, Gaurav, K, Birney, E, Fitzgerald, T, Allen, N, Aslam, T, Atan, D, Barman, S, Barrett, J, Bishop, P, Black, G, Braithwaite, T, Carare, R, Chakravarthy, U, Chan, M, Chua, S, Day, A, Desai, P, Dhillon, B, Dick, A, Doney, A, Egan, C, Ennis, S, Foster, P, Fruttiger, M, Gallacher, J, Garway-Heath, D, Gibson, J, Guggenheim, J, Hammond, C, Hardcastle, A, Harding, S, Hogg, R, Hysi, P, Keane, P, Khaw, P T, Khawaja, A, Lascaratos, G, Littlejohns, T, Lotery, A, Luben, R, Luthert, P, Macgillivray, T, Mcguinness, B, Mckay, G, Paterson, E, Peto, T, Steel, ....

Témata: Autoencoder, Autoencoder-based phenotyping, ophthalmic images, genetic loci, /dk/atira/pure/subjectarea/asjc/2600/2613, name=Statistics and Probability, /dk/atira/pure/subjectarea/asjc/1300/1303, name=Biochemistry, /dk/atira/pure/subjectarea/asjc/1300/1312, name=Molecular Biology, /dk/atira/pure/subjectarea/asjc/1700/1706, name=Computer Science Applications, /dk/atira/pure/subjectarea/asjc/1700/1703, name=Computational Theory and Mathematics, /dk/atira/pure/subjectarea/asjc/2600/2605, name=Computational Mathematics

Popis souboru: application/pdf

Dostupnost: https://pure.qub.ac.uk/en/publications/8e1bd91d-c990-4881-8a10-c693f4bcfe25
https://doi.org/10.1093/bioinformatics/btae732
https://pureadmin.qub.ac.uk/ws/files/631313619/btae732.pdf

Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers

Autoři: Sergouniotis, Panagiotis I. Diakite, Adam Gaurav, Kumar a další

Popis souboru: text

Relation: https://eprints.soton.ac.uk/498295/1/btae732.pdf; al, et , UK Biobank Eye and Vision Consortium (2024) Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers. Bioinformatics, 41 (1), [btae732]. (doi:10.1093/bioinformatics/btae732 ).

Dostupnost: https://eprints.soton.ac.uk/498295/
https://eprints.soton.ac.uk/498295/1/btae732.pdf

Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers.

Autoři: Sergouniotis, Panagiotis I Diakite, Adam Gaurav, Kumar a další

Zdroj: Bioinformatics; Jan2025, Vol. 41 Issue 1, p1-12, 12p

Témata: GENOME-wide association studies, OPTICAL coherence tomography, GENETIC variation, AUTOENCODERS, PHENOTYPES, DEEP learning

LSTM Autoencoder-Based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction

Autoři: Wang, Guanjin Xuan, Junyu Wang, Penghao a další

Přispěvatelé: Wang, Guanjin Xuan, Junyu Wang, Penghao a další

Témata: barley phenotyping, deep learning, phenotype prediction

Relation: ispartof: AI 2024: Advances in Artificial Intelligence spage 342 epage 353; 981960348X; 991005719360107891; alma:61MUN_INST/bibs/991005719360107891

Dostupnost: https://doi.org/10.1007/978-981-96-0348-0_25
https://researchportal.murdoch.edu.au/esploro/outputs/bookChapter/LSTM-Autoencoder-Based-Deep-Neural-Networks-forBarley/991005719360107891

LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction

Autoři: Wang, Guanjin Xuan, Junyu Wang, Penghao a další

Témata: deep learning, barley phenotyping, phenotype prediction

Popis souboru: pdf

Relation: ispartof: ArXiv.org; 991005689561107891; https://researchportal.murdoch.edu.au/view/delivery/61MUN_INST/12165955980007891/13169687240007891; alma:61MUN_INST/bibs/991005689561107891

Dostupnost: https://doi.org/10.48550/arxiv.2407.16709
https://researchportal.murdoch.edu.au/esploro/outputs/preprint/LSTM-Autoencoder-based-Deep-Neural-Networks-for/991005689561107891
https://researchportal.murdoch.edu.au/view/delivery/61MUN_INST/12165955980007891/13169687240007891

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Zdroj: International Journal of Data Science & Analytics; Oct2025, Vol. 20 Issue 4, p4067-4088, 22p

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Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers (Updated January 11, 2024).

Zdroj: Medical Imaging Week; 2/2/2024, p169-169, 1p

Témata: MORPHOLOGY, LOCUS (Genetics), BIOMARKERS

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Autoři: Anand, S. L. Visakh, R. Nalishma, R. a další

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Autoři: Wang, Chuangqi Choi, Hee June Woodbury, Lucy a další

Zdroj: Advanced Science; 11/6/2024, Vol. 11 Issue 41, p1-21, 21p

Témata: ARTIFICIAL neural networks, CELL imaging, MACHINE learning, FEATURE extraction, CELL migration

Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides epidemiologically informative biomarkers.

Zdroj: Medical Imaging Week; 7/7/2023, p148-148, 1p

Témata: MORPHOLOGY, LOCUS (Genetics), BIOMARKERS

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture.

Autoři: Cembrowska-Lech, Danuta Krzemińska, Adrianna Miller, Tymoteusz a další

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Autoři: Chuangqi Wang Hee June Choi Lucy Woodbury a další

Zdroj: Advanced Science, Vol 11, Iss 41, Pp n/a-n/a (2024)

Témata: cell migration, live cell imaging, machine learning, morphodynamics, phenotyping, Science

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2198-3844

Přístupová URL adresa: https://doaj.org/article/1b058e943c49498d95f2ab285935e486

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Autoři: Magness, Alastair Colliver, Emma Enfield, Katey S. S. a další

Zdroj: Nature Communications; 6/15/2024, Vol. 15 Issue 1, p1-20, 20p

Témata: IMAGE analysis, TISSUE fixation (Histology), DEEP learning, CYTOMETRY, DATA analysis, MANUSCRIPTS

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Zdroj: Nature Communications; 8/27/2025, Vol. 16 Issue 1, p1-12, 12p

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