Machine Learning Integration

Machine Learning Integration is a computational methodology that incorporates machine learning algorithms into metabolomics data analysis to enable automated pattern recognition, predictive modeling, and interpretation of complex metabolic profiles, distinguishing it from conventional statistical approaches by its ability to adaptively learn from high-dimensional, non-linear datasets.

141

Publications

8.5K

Citations

796

Authors

411

Institutions

Publications per year

2017–2026

128

Authors

796

  1. EK
    Ece Kamar

    Microsoft (United States), United States

    2 publications · 105 citations

    2

    h-index

  2. DK
    Donald Kossmann

    ETH Zurich, Switzerland

    2 publications · 105 citations

    2

    h-index

  3. TR
    Theodoros Rekatsinas

    University of Wisconsin–Madison, United States

    2 publications · 117 citations

    2

    h-index

  4. EH
    Eric Horvitz

    Microsoft (United States), United States

    2 publications · 105 citations

    2

    h-index

  5. JA
    Jude A. Okolie

    University of Oklahoma, United States

    2 publications · 82 citations

    2

    h-index

1–5 of 796

Venues

  1. PLoS ONE

    2 publications · 122 citations

    2

    h-index

  2. Scientific Reports

    2 publications · 47 citations

    2

    h-index

  3. Journal of Cleaner Production

    2 publications · 82 citations

    2

    h-index

  4. Journal of Industrial Information Integration

    2 publications · 69 citations

    2

    h-index

  5. BioMed Research International

    2 publications · 87 citations

    2

    h-index

1–5