Applications and Prospect Analysis of Deep Learning in Plant Genomics and Crop Breeding

• Published in: Nongye tushu qingbao xuekan Vol. 34; no. 8; pp. 4 - 18
• Main Authors: HOU Xiangying, CUI Yunpeng, LIU, Juan
• Format: Journal Article
• Language: Chinese
• Published: Beijing Agricultural Information Institute of Chinese Academy of Agricultural Sciences 01.08.2022; Editorial Department of Journal of Library and Information Science in Agriculture
• Subjects: plant genomics|crop breeding|deep learning|graph deep learning|review
• ISSN: 1002-1248
• DOI: 10.13998/j.cnki.issn1002-1248.22-0101

[Purpose/Significance] Advances in single-cell sequencing and high-throughput technology have made it possible for plant genomics to accumulate large quantities of data describing multidimensional genomic-wide molecular phenotypes at low cost. As powerful data mining tools, deep learning techniques can be utilized to further predict and interpret the acquired molecular phenotypes. In recent studies, deep learning has been shown to yield significant results in plant genomics and crop breeding research. However, a complete review of deep learning applications in plant genomics is lacking. [Method/Process] The input to deep learning applied to genomics is usually biological sequences and molecular phenotypes as predictor and target variables, respectively. We introduced the workflow from four views: input data pre-processing includes retrieval, coding, and splitting; model construction and training includes the selection of model architecture and hyperparameters; model evaluation and interpretability. Specifical