Machine Learning For Microbiome Data

Machine Learning for Microbiome Data is a computational methodology that applies machine learning algorithms to microbiome datasets to infer patterns, relationships, or predictions specific to microbial community structure and function, distinguished by its integration of high-dimensional biological data with statistical learning techniques for hypothesis generation, classification, or regression in microbiome research.

123

Publications

5.7K

Citations

762

Authors

403

Institutions

Publications per year

2017–2026

114

Authors

762

  1. AT
    Alice Tang

    University of California, San Francisco, United States

    3 publications · 67 citations

    3

    h-index

  2. AR
    Alennie Roldan

    University of California, San Francisco, United States

    3 publications · 67 citations

    3

    h-index

  3. MS
    Marina Sirota

    University of California, San Francisco, United States

    3 publications · 67 citations

    3

    h-index

  4. XJ
    Xingpeng Jiang

    Central China Normal University, China

    3 publications · 91 citations

    3

    h-index

  5. SS
    Samuel S. Minot

    Fred Hutch Cancer Center, United States

    3 publications · 67 citations

    3

    h-index

1–5 of 762

Venues

  1. Scientific Reports

    8 publications · 519 citations

    8

    h-index

  2. Frontiers in Microbiology

    6 publications · 305 citations

    6

    h-index

  3. Briefings in Bioinformatics

    5 publications · 148 citations

    5

    h-index

  4. Frontiers in Genetics

    5 publications · 166 citations

    5

    h-index

  5. BMC Bioinformatics

    5 publications · 162 citations

    5

    h-index

1–5