hways. One pathway would be wild gourd (akin to pumpkin shape) scallop acorn; a 134 second pathway would be wild gourd marrow straightneck zucchini cocozelle 135 (Figure 1B). See also Figure 17 in (Paris 1989). We extracted contours from the 136 ‘contours.png’ file, based in (Paris 1989) and available in GitHub 137 (https://github.com/miguelperezenciso/dna2image/blob/main/images/contours.png), using 138 OpenCV library (Bradski 2000). Contours were centered and 500 pseudo-landmarks were 139 obtained with the algorithm in Zingaretti et al. (2021). Next, contours were aligned with a 140 generalized procrustes algorithm implemented in python package ‘procrustes’ (Meng et al. 141 2022
Open resource ↗https://github.com/miguelperezenciso/dna2image · contours.png · pdf-raw-page:5 lines:1-76Unverified paper record
Computer generation of fruit shapes from DNA sequence
bioRxiv · 22 Sept 2022 · 10.1101/2022.09.19.508595
Abstract
The generation of realistic plant and animal images from marker information could be a main contribution of artificial intelligence to genetics and breeding. Since morphological traits are highly variable and highly heritable, this must be possible. However, a suitable algorithm has not been proposed yet. This paper is a proof of concept demonstrating the feasibility of this proposal using ‘decoders’, a class of deep learning architecture. We apply it to Cucurbitaceae, perhaps the family harboring the largest variability in fruit shape in the plant kingdom, and to tomato, a species with high morphological diversity also. We generate Cucurbitaceae shapes assuming a hypothetical, but plausible, evolutive path along observed fruit shapes of C. melo . In tomato, we used 353 images from 129 crosses between 25 maternal and 7 paternal lines for which genotype data were available. In both instances, a simple decoder was able to recover expected shapes with large accuracy. For the tomato pedigree, we also show that the algorithm can be trained to generate offspring images from their parents’ shapes, bypassing genotype information. Data and code are available at https://github.com/miguelperezenciso/dna2image .
Plant phenotyping relevance
DNA配列や親の形状から植物果実形状画像を生成する深層学習手法の概念実証であり、植物形態の取得・推定が研究の中心です。
titleComputer generation of fruit shapes from DNA sequence
abstractThis paper is a proof of concept demonstrating the feasibility of this proposal using ‘decoders’, a class of deep learning architecture.
abstractIn both instances, a simple decoder was able to recover expected shapes with large accuracy.
Code and data availability
The paper's cucurbit shape phenotyping inputs and analysis code are publicly available in the authors' dna2image GitHub repository, explicitly cited in the methods and data availability statement.
y, we have shown that very simple networks can be successfully trained in small 322 datasets to accurately predict fruit images. Although much work remains to be done, this 323 research opens new possibilities in the area of prediction of complex traits. 324 325 Data availability statement 326 All data and code are available at https://github.com/miguelperezenciso/dna2image.327 328 . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted September 22, 2022. ; https://doi.org/10.1101/2022.09.19.
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