首页|Investigators from U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) Target Machine Learning (Maize Feature Store: a Centralized Resource To Manage and Analyze Curated Maize Multi-omics Features for Machine Learning ...)
Investigators from U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) Target Machine Learning (Maize Feature Store: a Centralized Resource To Manage and Analyze Curated Maize Multi-omics Features for Machine Learning ...)
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NETL
NSTL
Oxford Univ Press
New research on Machine Learning is the subject of a report. According to news reporting originating from Ames, Iowa, by NewsRx correspondents, research stated, “The big-data analysis of complex data associated with maize genomes accelerates genetic research and improves agronomic traits. As a result, efforts have increased to integrate diverse datasets and extract meaning from these measurements.” Financial supporters for this research include USDA Agricultural Research Service, Iowa State University, USDA-ARS, Corn Insects and Crop Genetics Research Unit. Our news editors obtained a quote from the research from the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS), “Machine learning models are a powerful tool for gaining knowledge from large and complex datasets. However, these models must be trained on high-quality features to succeed. Currently, there are no solutions to host maize multi-omics datasets with end-to-end solutions for evaluating and linking features to target gene annotations. Our work presents the Maize Feature Store (MFS), a versatile application that combines features built on complex data to facilitate exploration, modeling and analysis. Feature stores allow researchers to rapidly deploy machine learning applications by managing and providing access to frequently used features. We populated the MFS for the maize reference genome with over 14 000 gene-based features based on published genomic, transcriptomic, epigenomic, variomic and proteomics datasets. Using the MFS, we created an accurate pan-genome classification model with an AUC-ROC score of 0.87.”
AmesIowaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningU.S. Department of Agriculture (USDA) Agricultural Research Service (ARS)
NETL
NSTL
Oxford Univ Press
