AIMC Topic: Zea mays

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A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.

Scientific reports Sep 23, 2021
Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA-protein Interactions (LPIs) is significantly important to well characterize the biological functions...
Zea mays Datasets as Topic Arabidopsis Humans Machine Learning Computational Biology RNA-Binding Proteins RNA, Long Noncoding
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LightGBM: accelerated genomically designed crop breeding through ensemble learning.

Genome biology Sep 20, 2021
LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines. LightGBM exhibits superior performa...
Machine Learning Software Genome-Wide Association Study Zea mays Genomics Crops, Agricultural Polymorphism, Single Nucleotide Plant Breeding Decision Trees
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Predicting phenotypes from genetic, environment, management, and historical data using CNNs.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Aug 27, 2021
Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and man...
Data Mining Crops, Agricultural Phenotype Genomics Zea mays Neural Networks, Computer Machine Learning
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Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses.

The Plant journal : for cell and molecular biology Aug 10, 2021
Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments ar...
Machine Learning Phenotype Seedlings Zea mays Equipment Design Soil Image Processing, Computer-Assisted Robotics Brassinosteroids Droughts Protein Kinases Arabidopsis Proteins Triazoles Arabidopsis
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Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Aug 3, 2021
Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture. Lack of high-throughpu...
Cooking Genetic Association Studies Zea mays Machine Learning Spectroscopy, Near-Infrared Water Genotype
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3D Reconstruction of Non-Rigid Plants and Sensor Data Fusion for Agriculture Phenotyping.

Sensors (Basel, Switzerland) Jun 15, 2021
Technology has been promoting a great transformation in farming. The introduction of robotics; the use of sensors in the field; and the advances in computer vision; allow new systems to be developed to assist processes, such as phenotyping, of crop's...
Farms Imaging, Three-Dimensional Agriculture Robotics Zea mays
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A spectral characteristic analysis method for distinguishing heavy metal pollution in crops: VMD-PCA-SVM.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 9, 2021
Exploring the characteristics and types of heavy metal pollution in crops has important implications for food security and human health. In this study, a method for distinguishing heavy metal-polluted elements in corn leaves was proposed. Based on th...
Humans China Metals, Heavy Principal Component Analysis Environmental Monitoring Zea mays Support Vector Machine Environmental Pollution
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Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt.

Scientific reports Jan 15, 2021
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions...
Climate Crop Production United States Zea mays Machine Learning
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iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network.

Computational and mathematical methods in medicine Jan 5, 2021
A promoter is a short DNA sequence near to the start codon, responsible for initiating transcription of a specific gene in genome. The accurate recognition of promoters has great significance for a better understanding of the transcriptional regulati...
Promoter Regions, Genetic Sequence Analysis, DNA Neural Networks, Computer DNA, Plant Zea mays Computational Biology TATA Box Species Specificity Databases, Nucleic Acid Genome, Plant
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sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.

Plant molecular biology Jan 1, 2021
We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding. As an important component of the CRISPR/Cas9 system, single-guide RNA (s...
Sorghum Gene Editing Glycine max Humans RNA, Guide, CRISPR-Cas Systems Crops, Agricultural Zea mays HCT116 Cells CRISPR-Cas Systems Neural Networks, Computer HEK293 Cells Algorithms Internet Triticum Computational Biology HeLa Cells
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