Article ; Online: Maize Feature Store: A centralized resource to manage and analyze curated maize multi-omics features for machine learning applications.
Database : the journal of biological databases and curation
2023 Volume 2023
Abstract: 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 ... ...
| Abstract | 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 14 000 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. The MFS is publicly available through the maize genetics and genomics database. Database URL https://mfs.maizegdb.org/. |
|---|---|
| MeSH term(s) | Zea mays/genetics ; Multiomics ; Databases, Genetic ; Genomics ; Machine Learning |
| Language | English |
| Publishing date | 2023-11-08 |
| Publishing country | England |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. |
| ZDB-ID | 2496706-3 |
| ISSN | 1758-0463 ; 1758-0463 |
| ISSN (online) | 1758-0463 |
| ISSN | 1758-0463 |
| DOI | 10.1093/database/baad078 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.