Imbalanced Phenotype Classification

Imbalanced Phenotype Classification is a supervised classification task in which the goal is to predict phenotypic labels from biological data while addressing the inherent challenge posed by highly skewed class distributions that disproportionately favor certain phenotypes over others.

4

Publications

283

Citations

25

Authors

11

Institutions

Publications per year

2017–2026

2

Authors

25

  1. BO
    Bianca Oliva

    Azienda ospedaliera "Bianchi-Melacrino-Morelli", Italy

    1 publications · 17 citations

    1

    h-index

  2. AC
    Angelo Cardellicchio

    Polytechnic University of Bari, Italy

    1 publications · 99 citations

    1

    h-index

  3. FS
    Firozeh Solimani

    University of Bari Aldo Moro, Italy

    1 publications · 99 citations

    1

    h-index

  4. FM
    Fortunato Morabito

    Azienda Ospedaliera di Cosenza, Italy

    1 publications · 17 citations

    1

    h-index

  5. CY
    Cheng‐Hong Yang

    I-Shou University, Taiwan

    1 publications · 35 citations

    1

    h-index

1–5 of 25

Venues

  1. Cancer

    1 publications · 17 citations

    1

    h-index

  2. Computers and Electronics in Agriculture

    1 publications · 99 citations

    1

    h-index

  3. PLoS ONE

    1 publications · 35 citations

    1

    h-index

  4. Expert Systems with Applications

    1 publications · 132 citations

    1

    h-index

  5. Frontiers in Physiology

    1 publications · 10 citations

1–5