{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T02:55:59Z","timestamp":1774407359115,"version":"3.50.1"},"reference-count":73,"publisher":"Oxford University Press (OUP)","funder":[{"name":"Department of Agriculture, Agricultural Research Service","award":["5030-21000-068-00-D"],"award-info":[{"award-number":["5030-21000-068-00-D"]}]},{"name":"Department of Agriculture, Agricultural Research Service","award":["5030-21000-068-00-D"],"award-info":[{"award-number":["5030-21000-068-00-D"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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. 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\u2009000 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. The MFS is publicly available through the maize genetics and genomics database.<\/jats:p><jats:p>Database URL \u00a0https:\/\/mfs.maizegdb.org\/<\/jats:p>","DOI":"10.1093\/database\/baad078","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T16:40:17Z","timestamp":1699548017000},"source":"Crossref","is-referenced-by-count":8,"title":["Maize Feature Store: A centralized resource to manage and\u00a0analyze curated maize multi-omics features for\u00a0machine learning applications"],"prefix":"10.1093","volume":"2023","author":[{"given":"Shatabdi","family":"Sen","sequence":"first","affiliation":[{"name":"Department of Plant Pathology & Microbiology, Iowa State University , 1344 Advanced Teaching & Research Bldg, 2213 Pammel Dr, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margaret R","family":"Woodhouse","sequence":"additional","affiliation":[{"name":"Corn Insects and Crop Genetics Research Unit USDA-ARS, , 819 Wallace Road, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"II","given":"John L","family":"Portwood","sequence":"additional","affiliation":[{"name":"Corn Insects and Crop Genetics Research Unit USDA-ARS, , 819 Wallace Road, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2380-6704","authenticated-orcid":false,"given":"Carson M","family":"Andorf","sequence":"additional","affiliation":[{"name":"USDA-ARS, Corn Insects and Crop Genetics Research Unit , 819 Wallace Road, Ames, IA 50011, USA"},{"name":"Department of Computer Science, Iowa State University , Atanasoff Hall, 2434 Osborn Dr, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"key":"2023110916393248500_R1","doi-asserted-by":"crossref","DOI":"10.1002\/tpg2.20015","article-title":"Non-homology-based prediction of\u00a0gene functions in\u00a0maize (Zea mays ssp. mays)","volume":"13","author":"Dai","year":"2020","journal-title":"Plant Genom."},{"key":"2023110916393248500_R2","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1105\/tpc.15.00051","article-title":"Characteristics of\u00a0Plant Essential Genes Allow for\u00a0within- and\u00a0between-Species Prediction of\u00a0Lethal Mutant Phenotypes","volume":"27","author":"Lloyd","year":"2015","journal-title":"Plant Cell."},{"key":"2023110916393248500_R3","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.tplants.2015.10.015","article-title":"Machine Learning for\u00a0High-Throughput Stress Phenotyping in\u00a0Plants","volume":"21","author":"Singh","year":"2016","journal-title":"Trends Plant Sci."},{"key":"2023110916393248500_R4","doi-asserted-by":"crossref","DOI":"10.3390\/s21113758","article-title":"Machine Learning in\u00a0Agriculture: A Comprehensive Updated Review","volume":"21","author":"Benos","year":"2021","journal-title":"Sensors. 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