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    Identifying keystone species in microbial communities is challenging. A new deep learning framework (DKI) effectively identifies these crucial species by learning community assembly rules, enabling better microbiome management.

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    Area of Science:

    • Community Ecology
    • Microbiome Research
    • Computational Biology

    Background:

    • Keystone species significantly influence microbial community structure and function.
    • Identifying keystone species is difficult due to limited knowledge of microbial dynamics and experimental challenges.

    Approach:

    • Propose a Data-driven Keystone species Identification (DKI) framework using deep learning.
    • Train a deep learning model on microbiome samples to learn habitat-specific assembly rules.
    • Quantify species keystoneness via simulated species removal ('thought experiment').

    Key Points:

    • DKI framework validated using synthetic data from ecological models.
    • Applied DKI to human gut, oral, soil, and coral microbiomes.
    • High keystoneness taxa showed community specificity and matched literature findings.

    Conclusions:

    • DKI offers an efficient, data-driven method for identifying keystone species.
    • Demonstrates machine learning's potential in community ecology.
    • Paves the way for data-driven management of microbial communities.