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Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

G3 (Bethesda, Md.)
Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows along the proximodistal axis of the adult leaf blade. Bulliform cell patterning affects leaf angle and ...

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level.

Global change biology
Understanding large-scale crop growth and its responses to climate change are critical for yield estimation and prediction, especially under the increased frequency of extreme climate and weather events. County-level corn phenology varies spatially a...

DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.

Nature methods
We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves t...

Potential use of machine learning methods in assessment of Fusarium culmorum and Fusariumproliferatum growth and mycotoxin production in treatments with antifungal agents.

Fungal biology
Fusarium-controlling fungicides are necessary to limit crop loss. Little is known about the effect of antifungal formulations at sub-lethal doses, and their interaction with abiotic factors, on Fusarium culmorum and F. proliferatum development and on...

Effects of corn straw on dissipation of polycyclic aromatic hydrocarbons and potential application of backpropagation artificial neural network prediction model for PAHs bioremediation.

Ecotoxicology and environmental safety
In order to provide a viable option for remediation of PAHs-contaminated soils, a greenhouse experiment was conducted to assess the effect of corn straw amendment (1%, 2%, 4% or 6%, w/w) on dissipation of aged polycyclic aromatic hydrocarbons (PAHs) ...

Identification of the expressome by machine learning on omics data.

Proceedings of the National Academy of Sciences of the United States of America
Accurate annotation of plant genomes remains complex due to the presence of many pseudogenes arising from whole-genome duplication-generated redundancy or the capture and movement of gene fragments by transposable elements. Machine learning on genome...

Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.

G3 (Bethesda, Md.)
Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of ge...

Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture.

G3 (Bethesda, Md.)
Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of gen...

A novel nanobody and mimotope based immunoassay for rapid analysis of aflatoxin B1.

Talanta
Mimotopes could replace mycotoxins and their conjugates to develop immunoassay methods. The mimotopes obtained by phage display technology were mainly using monoclonal antibodies or polyclonal antibodies as targets. However, the mimotope of recombina...

Gene Regulatory Relationship Mining Using Improved Three-Phase Dependency Analysis Approach.

IEEE/ACM transactions on computational biology and bioinformatics
How to mine the gene regulatory relationship and construct gene regulatory network (GRN) is of utmost interest within the whole biological community, however, which has been consistently a challenging problem since the tremendous complexity in cellul...