Motif Features

Motif Features is a time series feature extraction method that identifies and quantifies recurrent, discriminative subsequences (motifs) within a time series to capture its underlying temporal patterns and structural characteristics.

352

Publications

21.6K

Citations

1.6K

Authors

569

Institutions

Publications per year

2017–2026

59

Authors

1.6K

  1. TK
    Teresa K. Attwood

    University of Manchester, United Kingdom

    3 publications · 131 citations

    3

    h-index

  2. PS
    Paul Schimmel

    Massachusetts Institute of Technology, United States

    3 publications · 170 citations

    3

    h-index

  3. DH
    De-Shuang Huang

    Tongji University, China

    3 publications · 172 citations

    3

    h-index

  4. AM
    Abdullah Mueen

    University of New Mexico, United States

    3 publications · 262 citations

    3

    h-index

  5. TK
    T. Keith Blackwell

    Harvard University, United States

    2 publications · 500 citations

    2

    h-index

1–5 of 1.6K

Venues

  1. Journal of Biological Chemistry

    JBC · 28 publications · 2K citations

    23

    h-index

  2. Proceedings of the National Academy of Sciences

    PNAS · 17 publications · 2.2K citations

    17

    h-index

  3. Nucleic Acids Research

    18 publications · 1.3K citations

    15

    h-index

  4. FEBS Letters

    12 publications · 452 citations

    12

    h-index

  5. Molecular and Cellular Biology

    MCB · 12 publications · 1.4K citations

    12

    h-index

1–5