Supervised Tumor Classification

Supervised Tumor Classification is a machine learning approach in cancer genomics that belongs to the genus of classification methods and is differentiated by its reliance on labeled molecular profiles to train predictive models for assigning tumor samples to predefined biological or clinical categories.

117

Publications

12.8K

Citations

612

Authors

312

Institutions

Publications per year

2017–2026

63

Authors

612

  1. MR
    Markus Ringnér

    Lund University, Sweden

    3 publications · 2.7K citations

    3

    h-index

  2. AI
    Ahmed Idbaïh

    Sorbonne Université, France

    2 publications · 95 citations

    2

    h-index

  3. CH
    Christoph Harms

    Charité - Universitätsmedizin Berlin, Germany

    2 publications · 95 citations

    2

    h-index

  4. MA
    Malek Alzaqebah

    Imam Abdulrahman Bin Faisal University, Saudi Arabia

    2 publications · 61 citations

    2

    h-index

  5. CP
    Carsten Peterson

    Lund University, Sweden

    2 publications · 2.7K citations

    2

    h-index

1–5 of 612

Venues

  1. Computers in Biology and Medicine

    7 publications · 821 citations

    7

    h-index

  2. Expert Systems with Applications

    4 publications · 1.1K citations

    4

    h-index

  3. Artificial Intelligence in Medicine

    AIM · 4 publications · 235 citations

    4

    h-index

  4. BMC Bioinformatics

    4 publications · 298 citations

    4

    h-index

  5. Neural Computing and Applications

    3 publications · 402 citations

    3

    h-index

1–5