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Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for Brassica Campestris ssp. Chinensis L. SENSORS 2019; 19:s19112448. [PMID: 31146350 PMCID: PMC6603544 DOI: 10.3390/s19112448] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 05/24/2019] [Accepted: 05/24/2019] [Indexed: 11/25/2022]
Abstract
Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this study, texture- and geometry-related phenotyping combined with color properties were investigated for their potential use in evaluating N in pakchoi (Brassica campestris ssp. chinensis L.). Potted pakchoi treated with four levels of N were cultivated in a greenhouse. Their top-view images were acquired using a camera at six growth stages. The corresponding plant N concentration was determined destructively. The quantitative relationships between the nitrogen nutrition index (NNI) and the image-based phenotyping features were established using the following algorithms: random forest (RF), support vector regression (SVR), and neural network (NN). The results showed the full model based on the color, texture, and geometry-related features outperforms the model based on only the color-related feature in predicting the NNI. The RF full model exhibited the most robust performance in both the seedling and harvest stages, reaching prediction accuracies of 0.823 and 0.943, respectively. The high prediction accuracy of the model allows for a low-cost, non-destructive monitoring of N in the field of precision crop management.
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Liao CC, Chen LJ, Lo SF, Chen CW, Chu YW. EAT-Rice: A predictive model for flanking gene expression of T-DNA insertion activation-tagged rice mutants by machine learning approaches. PLoS Comput Biol 2019; 15:e1006942. [PMID: 31067213 PMCID: PMC6505892 DOI: 10.1371/journal.pcbi.1006942] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 03/09/2019] [Indexed: 11/17/2022] Open
Abstract
T-DNA activation-tagging technology is widely used to study rice gene functions. When T-DNA inserts into genome, the flanking gene expression may be altered using CaMV 35S enhancer, but the affected genes still need to be validated by biological experiment. We have developed the EAT-Rice platform to predict the flanking gene expression of T-DNA insertion site in rice mutants. The three kinds of DNA sequences including UPS1K, DISTANCE, and MIDDLE were retrieved to encode and build a forecast model of two-layer machine learning. In the first-layer models, the features nucleotide context (N-gram), cis-regulatory elements (Motif), nucleotide physicochemical properties (NPC), and CG-island (CGI) were used to build SVM models by analysing the concealed information embedded within the three kinds of sequences. Logistic regression was used to estimate the probability of gene activation which as feature-encoding weighting within first-layer model. In the second-layer models, the NaiveBayesUpdateable algorithm was used to integrate these first layer-models, and the system performance was 88.33% on 5-fold cross-validation, and 79.17% on independent-testing finally. In the three kinds of sequences, the model constructed by Middle had the best contribution to the system for identifying the activated genes. The EAT-Rice system provided better performance and gene expression prediction at further distances when compared to the TRIM database. An online server based on EAT-rice is available at http://predictor.nchu.edu.tw/EAT-Rice.
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Affiliation(s)
- Chi-Chou Liao
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Liang-Jwu Chen
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan.,Advanced Plant Biotechnology Center National Chung Hsing University, Taichung, Taiwan
| | - Shuen-Fang Lo
- Agricultural Biotechnology Center, National Chung Hsing University, Taichung, Taiwan.,Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
| | - Chi-Wei Chen
- Department of Computer Science and Engineering, National Chung Hsing University, Taichung, Taiwan
| | - Yen-Wei Chu
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan.,Agricultural Biotechnology Center, National Chung Hsing University, Taichung, Taiwan.,Biotechnology Center, National Chung Hsing University, Taichung, Taiwan.,Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung, Taiwan.,Rong Hsing Research Center For Translational Medicine, National Chung Hsing University, Taichung, Taiwan
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Banf M, Rhee SY. Computational inference of gene regulatory networks: Approaches, limitations and opportunities. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2016; 1860:41-52. [PMID: 27641093 DOI: 10.1016/j.bbagrm.2016.09.003] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 09/08/2016] [Accepted: 09/08/2016] [Indexed: 10/21/2022]
Abstract
Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.
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Affiliation(s)
- Michael Banf
- Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford 93405, United States.
| | - Seung Y Rhee
- Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford 93405, United States.
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