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- 01GJQDBH49EZX7HSVDZEZP7NP2 classification A1.
- 01GJQDBH49EZX7HSVDZEZP7NP2 date "2021".
- 01GJQDBH49EZX7HSVDZEZP7NP2 language "eng".
- 01GJQDBH49EZX7HSVDZEZP7NP2 type journalArticle.
- 01GJQDBH49EZX7HSVDZEZP7NP2 hasPart 01GJQDF21QTM74RF7VHEFGJN1Y.pdf.
- 01GJQDBH49EZX7HSVDZEZP7NP2 subject "Biology and Life Sciences".
- 01GJQDBH49EZX7HSVDZEZP7NP2 doi "10.1242/dev.199664".
- 01GJQDBH49EZX7HSVDZEZP7NP2 issn "0950-1991".
- 01GJQDBH49EZX7HSVDZEZP7NP2 issn "1477-9129".
- 01GJQDBH49EZX7HSVDZEZP7NP2 issue "21".
- 01GJQDBH49EZX7HSVDZEZP7NP2 volume "148".
- 01GJQDBH49EZX7HSVDZEZP7NP2 abstract "Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can automate segmentation tasks in various imaging modalities, and we quantify phenotypes of altered renal, neural and craniofacial development in Xenopus embryos in comparison with normal variability. We demonstrate the utility of this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). We highlight how in toto light-sheet microscopy facilitates accurate reconstruction of brain and craniofacial structures within X. tropicalis embryos upon dyrk1a and six1 loss of function or treatment with retinoic acid inhibitors. These tools increase the sensitivity and throughput of evaluating developmental malformations caused by chemical or genetic disruption. Furthermore, we provide a library of pre-trained networks and detailed instructions for applying deep learning to the reader's own datasets. We demonstrate the versatility, precision and scalability of deep neural network phenotyping on embryonic disease models. By combining light-sheet microscopy and deep learning, we provide a framework for higher-throughput characterization of embryonic model organisms.".
- 01GJQDBH49EZX7HSVDZEZP7NP2 author 2305E930-F0EE-11E1-A9DE-61C894A0A6B4.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:0854785c-c9bb-4dae-807e-4a7faaaeda2c.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:2ce2e43c-3988-4af9-a5ec-f91c45b5ce4f.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:31b47c57-f3e8-4932-a944-d1358d11fd8f.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:368b2237-edcc-4647-b171-ef97c35810d3.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:36f8099e-2860-4dfd-9cf7-0dcf1e3d372f.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:3cd17d88-ac65-49da-a6e8-24f1fda78df9.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:41bd00db-8e75-4231-9a07-9770dafbd05a.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:4382b6ba-aef5-47dc-808c-867dd29d72a5.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:462d3106-d7f2-41e3-b6d3-af54f951b0d4.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:5f29a1ca-ec2d-4c55-a1c1-a107732d56e0.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:717e081f-0ddf-46f1-be43-fdfae34e609e.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:720bbf4d-b4da-42c2-99d5-9972400e4c51.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:b8f2053e-8a28-4a39-83e7-449ae1e6ab8a.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:c359edea-a39e-40cc-87e5-c6eb152291fa.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:c5f4ed08-06d1-45dd-8cdf-9943d61ff482.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:c7233c45-9f5e-416c-8668-9981d86af418.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:cbe4c860-4d79-4819-8c99-7eeaf2d3fe01.
- 01GJQDBH49EZX7HSVDZEZP7NP2 author urn:uuid:d1320a7c-a05c-4234-83dc-6ffd362ba9e8.
- 01GJQDBH49EZX7HSVDZEZP7NP2 dateCreated "2022-11-25T12:49:20Z".
- 01GJQDBH49EZX7HSVDZEZP7NP2 dateModified "2024-07-09T07:41:22Z".
- 01GJQDBH49EZX7HSVDZEZP7NP2 name "Deep learning is widely applicable to phenotyping embryonic development and disease".
- 01GJQDBH49EZX7HSVDZEZP7NP2 pagination urn:uuid:a211c44b-5128-4ad7-a4b9-513e7b743b19.
- 01GJQDBH49EZX7HSVDZEZP7NP2 sameAs LU-01GJQDBH49EZX7HSVDZEZP7NP2.
- 01GJQDBH49EZX7HSVDZEZP7NP2 sourceOrganization urn:uuid:d190ada4-72c4-4279-b589-74d3c6926b5e.
- 01GJQDBH49EZX7HSVDZEZP7NP2 type A1.