State-space Trajectory Modeling

State-Space Trajectory Modeling is a computational framework within trajectory inference that represents dynamic cellular processes as stochastic trajectories evolving over time in a latent state space, distinguishing itself by explicitly modeling the probabilistic transitions between hidden states to reconstruct temporal progression from cross-sectional data.

47

Publications

2.4K

Citations

148

Authors

83

Institutions

Publications per year

2017–2026

13

Authors

148

  1. HS
    Henry Shu-Hung Chung

    City University of Hong Kong, Hong Kong SAR China

    3 publications · 185 citations

    3

    h-index

  2. PN
    P.N. Nikiforuk

    University of Saskatchewan, Canada

    2 publications · 130 citations

    2

    h-index

  3. JL
    Jin Liang

    University of Science and Technology of China, China

    2 publications · 130 citations

    2

    h-index

  4. TE
    Todd E. Dennis

    University of Auckland, New Zealand

    2 publications · 96 citations

    2

    h-index

  5. JM
    Joanna Mills Flemming

    Dalhousie University, Canada

    2 publications · 360 citations

    2

    h-index

1–5 of 148

Venues

  1. PLoS ONE

    2 publications · 96 citations

    2

    h-index

  2. Neural Networks

    2 publications · 74 citations

    2

    h-index

  3. Methods in Ecology and Evolution

    2 publications · 87 citations

    2

    h-index

  4. IEEE Transactions on Power Electronics

    2 publications · 155 citations

    2

    h-index

  5. 1

    h-index

1–5