IBCF.MTME

IBCF.MTME implements the item-based collaborative filtering (IBCF) algorithm to analyze continuous phenotypic data across multiple traits and environments for genomic-enabled prediction in plant breeding.

Key Features:

• Continuous Phenotypic Data Handling: Processes continuous phenotypic data, enabling IBCF application beyond binary and ordinary phenotypes.
• Multitrait and Multienvironment Analysis: Supports analysis of datasets collected across multiple traits and environments.
• Genomic Selection Integration: Facilitates evaluation of genomic-enabled prediction accuracy for multitrait and multienvironment datasets.

Scientific Applications:

• Plant Breeding Research: Enables analysis of continuous phenotypic data across traits and environments to improve crop performance prediction in plant breeding.
• Prediction Accuracy Studies: Evaluates prediction accuracy under phenotypic and genomic selection scenarios for multitrait and multienvironment data.

Methodology:

Employs the item-based collaborative filtering (IBCF) algorithm focusing on item-based similarities rather than user-based ones; includes example datasets Wheat_IBCF and Year_IBCF representing multienvironment and multitrait scenarios.