Multimodal Phenotyping

Multimodal Phenotyping is a computational methodology within Computational Phenotyping that integrates heterogeneous data types—such as genomic, clinical, imaging, and behavioral data—to derive more comprehensive and accurate patient phenotypes by capturing the complementary information inherent in multiple data sources.

280

Publications

19.9K

Citations

3.1K

Authors

953

Institutions

Publications per year

2017–2026

227

Authors

3.1K

  1. LS
    Li Shen

    Indiana University – Purdue University Indianapolis, United States

    10 publications · 726 citations

    9

    h-index

  2. VD
    Vince D. Calhoun

    Mind Research Network, United States

    8 publications · 1.4K citations

    8

    h-index

  3. JP
    Jyotishman Pathak

    Cornell University, United States

    5 publications · 458 citations

    5

    h-index

  4. AJ
    Andrew J. Saykin

    Indiana University – Purdue University Indianapolis, United States

    6 publications · 398 citations

    5

    h-index

  5. DB
    Danilo Bzdok

    McGill University, Canada

    5 publications · 423 citations

    5

    h-index

1–5 of 3.1K

Venues

  1. IEEE Transactions on Medical Imaging

    11 publications · 561 citations

    11

    h-index

  2. Scientific Reports

    10 publications · 358 citations

    10

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 9 publications · 139 citations

    9

    h-index

  4. NeuroImage

    9 publications · 1.4K citations

    9

    h-index

  5. PLoS ONE

    6 publications · 337 citations

    5

    h-index

1–5