Search Results - "Autoencoder-based phenotyping"

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

Authors: Panagiotis I Sergouniotis Adam Diakite Kumar Gaurav et al.

Source: 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

Subject Terms: 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

File Description: application/pdf; text

Access URL: 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

Authors: Sergouniotis, Panagiotis I. Diakite, Adam Gaurav, Kumar et al.

Source: 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, ....

Subject Terms: 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

File Description: application/pdf

Availability: 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

Authors: Sergouniotis, Panagiotis I. Diakite, Adam Gaurav, Kumar et al.

File Description: 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 ).

Availability: 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.

Authors: Sergouniotis, Panagiotis I Diakite, Adam Gaurav, Kumar et al.

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

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

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

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

Subject Terms: MORPHOLOGY, LOCUS (Genetics), BIOMARKERS

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

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

Subject Terms: MORPHOLOGY, LOCUS (Genetics), BIOMARKERS

Phenotypic screening and genetic insights for predicting major vascular-related diseases using retinal imaging.

Authors: Lu, Menglin Mao, Yiheng Zhu, Hui et al.

Source: NPJ Digital Medicine; 7/14/2025, Vol. 8 Issue 1, p1-16, 16p

Subject Terms: VASCULAR disease diagnosis, RETINA abnormalities, RISK assessment, MYOCARDIAL infarction, RESEARCH funding, OPTICAL coherence tomography, MENDELIAN randomization, QUANTITATIVE research, DESCRIPTIVE statistics, GENETIC risk score, CHRONIC kidney failure, RETINA, MEDICAL screening, ATTRIBUTION (Social psychology), STROKE, DATA analysis software, VASCULAR diseases, PHENOTYPES, DISEASE risk factors

Company/Entity: UK Biobank Ltd.

Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.

Authors: Lan, Chiao-Hsin Chiu, Ta-Hung Yen, Wei-Ting et al.

Source: International Journal of Molecular Sciences; May2025, Vol. 26 Issue 10, p4473, 20p

Subject Terms: TRANSFORMER models, GENERATIVE adversarial networks, RECURRENT neural networks, CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, DEEP learning