Trajectory-informed Clustering

Trajectory-Informed Clustering is a cell clustering method that incorporates temporal or pseudo-temporal trajectory information to guide the identification of cell populations, thereby distinguishing itself from conventional clustering approaches by integrating dynamic progression patterns inherent in single-cell sequencing data.

19

Publications

983

Citations

58

Authors

47

Institutions

Publications per year

2017–2026

8

Authors

58

  1. ÉF
    Éric Féron

    Massachusetts Institute of Technology, United States

    2 publications · 286 citations

    2

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  2. MG
    Maxime Gariel

    Georgia Institute of Technology, United States

    2 publications · 286 citations

    2

    h-index

  3. NK
    Nikola Kasabov

    Auckland University of Technology, New Zealand

    2 publications · 45 citations

    2

    h-index

  4. AN
    Ashok N. Srivastava

    Ames Research Center, United States

    2 publications · 286 citations

    2

    h-index

  5. ZI
    Zahedeh Izakian

    K.N.Toosi University of Technology, Iran

    1 publications · 54 citations

    1

    h-index

1–5 of 58

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 33 citations

    2

    h-index

  2. Expert Systems with Applications

    2 publications · 57 citations

    2

    h-index

  3. Machine Learning

    1 publications · 16 citations

    1

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5