Search Results - Autoencoder-based phenotyping

Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers by Sergouniotis, Panagiotis I, Diakite, Adam, Gaurav, Kumar, Birney, Ewan, Fitzgerald, Tomas

“…Motivation Genome-wide association studies (GWAS) have been remarkably successful in identifying associations between genetic variants and imaging-derived…”
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ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets by Shen, Yafei, Zhang, Tao, Liu, Zhiwei, Kostelidou, Kalliopi, Xu, Ying, Yang, Ling

“…), which disrupt latent correlations between dimensions, and high leverage points (HLP), which deviate from the norm but go undetected by traditional autoencoder-based methods…”
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Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel by Tross, Michael C., Grzybowski, Marcin W., Jubery, Talukder Z., Grove, Ryleigh J., Nishimwe, Aime V., Torres‐Rodriguez, J. Vladimir, Sun, Guangchao, Ganapathysubramanian, Baskar, Ge, Yufeng, Schnable, James C.

“…Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor…”
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Interpretable Fine‐Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning by Wang, Chuangqi, Choi, Hee June, Woodbury, Lucy, Lee, Kwonmoo

“… To tackle these challenges, a self‐training deep learning framework designed for fine‐grained and interpretable phenotyping is presented…”
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Deep learning for genomic selection of aquatic animals by Wang, Yangfan, Ni, Ping, Sturrock, Marc, Zeng, Qifan, Wang, Bo, Bao, Zhenmin, Hu, Jingjie

“… in phenotyping, genotyping and genomic estimated breeding value (GEBV) prediction of GS. It can be seen from this article that CNNs obtain phenotype data of aquatic animals efficiently, and without injury…”
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ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets by Shen, Yafei, Zhang, Tao, Liu, Zhiwei, Kostelidou, Kalliopi, Xu, Ying, Yang, Ling

“…), which disrupt latent correlations between dimensions, and high leverage points (HLP), which deviate from the norm but go undetected by traditional autoencoder-based methods…”
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Uncovering Interpretable Fine-Grained Phenotypes of Subcellular Dynamics through Unsupervised Self-Training of Deep Neural Networks by Wang, Chuangqi, Choi, Hee June, Woodbury, Lucy, Lee, Kwonmoo

“…Live cell imaging provides unparallel insights into dynamic cellular processes across spatiotemporal scales. Despite its potential, the inherent spatiotemporal…”
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Deep Phenotyping of Obesity: Electronic Health Record–Based Temporal Modeling Study by Ruan, Xiaoyang, Lu, Shuyu, Wang, Liwei, Wen, Andrew, Murali, Sameer, Liu, Hongfang

“… the feasibility, data requirements, clustering patterns, and challenges associated with EHR-based obesity deep phenotyping…”
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Deep phenotyping obesity using EHR data: Promise, Challenges, and Future Directions by Ruan, Xiaoyang, Lu, Shuyu, Wang, Liwei, Wen, Andrew, Sameer, Murali, Liu, Hongfang

“…) vary significantly, highlighting the need for developing approaches to obesity deep phenotyping and associated precision medicine…”
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Bumblebee Your Way to Recovery: Transforming The Approach to Detection of Mental Health Relapses by Gorzynski, Kamil, Ples, Anna, Ryzhankow, Ivan, Zych, Bartlomiej

“…: Psychotic and Non-Psychotic Relapse Detection using Wearable-Based Digital Phenotyping [1], First Track - detection of non-psychotic relapses, containing mainly depression episodes…”
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