Cross-dataset Validation

Cross-Dataset Validation is a validation methodology in pharmacophore modeling that assesses the generalizability and robustness of a pharmacophore model by evaluating its performance across distinct, independently curated datasets, thereby distinguishing it from single-dataset or internal validation approaches through its reliance on external data heterogeneity.

374

Publications

25.4K

Citations

1.4K

Authors

528

Institutions

Publications per year

2017–2026

314

Authors

1.4K

  1. SL
    Shengcai Liao

    Chinese Academy of Sciences, China

    4 publications · 196 citations

    4

    h-index

  2. EL
    Eduardo Luz

    Universidade Federal de Ouro Preto, Brazil

    4 publications · 290 citations

    4

    h-index

  3. IH
    Irtiza Hasan

    University of Verona, Italy

    4 publications · 196 citations

    4

    h-index

  4. XX
    Xuhai Xu

    University of Washington, United States

    4 publications · 156 citations

    4

    h-index

  5. SP
    Shwetak Patel

    University of Washington, United States

    4 publications · 156 citations

    4

    h-index

1–5 of 1.4K

Venues

  1. arXiv (Cornell University)

    Repository · 25 publications · 1.4K citations

    18

    h-index

  2. IEEE Transactions on Image Processing

    6 publications · 495 citations

    8

    h-index

  3. IEEE Access

    6 publications · 159 citations

    7

    h-index

  4. Scientific Reports

    6 publications · 374 citations

    7

    h-index

  5. 6

    h-index

1–5