Loss Inference

Loss Inference is a methodological contribution in paralogy inference that identifies and reconstructs the absence or deletion of structural, functional, or evolutionary elements within a system by distinguishing patterns of missing information from conserved or retained features.

24

Publications

953

Citations

85

Authors

33

Institutions

Publications per year

2017–2026

6

Authors

85

  1. TZ
    Tuo Zhang

    Northwestern Polytechnical University, China

    3 publications · 104 citations

    3

    h-index

  2. XZ
    Xin Zhang

    National University of Defense Technology, China

    3 publications · 104 citations

    3

    h-index

  3. BG
    Bao Ge

    Shaanxi Normal University, China

    3 publications · 104 citations

    3

    h-index

  4. TL
    Tianming Liu

    University of Georgia, United States

    3 publications · 104 citations

    3

    h-index

  5. TH
    Tianle Han

    Shaanxi Normal University, China

    3 publications · 104 citations

    3

    h-index

1–5 of 85

Venues

  1. Risks

    1 publications · 25 citations

    1

    h-index

  2. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 1 publications · 17 citations

    1

    h-index

  3. IEEE Transactions on Information Theory

    1 publications · 27 citations

    1

    h-index

  4. IEEE Transactions on Industrial Electronics

    1 publications · 32 citations

    1

    h-index

  5. PLoS ONE

    1 publications · 10 citations

    1

    h-index

1–5