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    A new R package, IBCF.MTME, enables item-based collaborative filtering for continuous plant breeding data. This tool enhances prediction accuracy for multitrait and multienvironment scenarios, aiding breeder decisions.

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    Area of Science:

    • Plant breeding and genetics
    • Bioinformatics and computational biology
    • Statistical genetics

    Background:

    • Traditional item-based collaborative filtering (IBCF) algorithms are limited to binary or ordinary phenotypes.
    • Plant breeding research often involves complex continuous phenotypic data across multiple traits and environments.
    • Existing IBCF implementations cannot handle the nuances of continuous multitrait and multienvironmental data.

    Purpose of the Study:

    • To introduce the R package IBCF.MTME for applying IBCF to continuous phenotypic data in plant breeding.
    • To demonstrate the package's utility in assessing prediction accuracy for multitrait and multienvironmental data under genomic selection.
    • To provide a tool that addresses the limitations of current IBCF methods for plant breeding applications.

    Main Methods:

    • Development and implementation of the Item-Based Collaborative Filtering for Multitrait and Multienvironment Data (IBCF.MTME) R package.
    • Application of the IBCF algorithm to continuous phenotypic datasets.
    • Evaluation of genomic-enabled prediction accuracy using multitrait and multienvironmental data.
    • Illustration of package usage through seven diverse examples, including Wheat_IBCF and Year_IBCF datasets.

    Main Results:

    • The IBCF.MTME package successfully applies IBCF to continuous phenotypic data, overcoming limitations of existing packages.
    • The package facilitates the study of prediction accuracy in complex multitrait and multienvironmental breeding scenarios.
    • Demonstrated effectiveness in analyzing both individual and combined multitrait and multienvironmental data structures.

    Conclusions:

    • The IBCF.MTME package provides a valuable tool for plant breeders to improve genomic-enabled prediction accuracy.
    • Its ability to handle continuous data in multitrait and multienvironmental contexts offers significant advantages for decision-making.
    • This package enhances the application of IBCF in plant breeding research, particularly for genomic selection.