Computational Isoform Expression Modeling

Computational Isoform Expression Modeling is a computational framework that quantitatively predicts or infers the expression levels of alternatively spliced transcript isoforms from high-throughput sequencing data by integrating statistical models, algorithmic inference, and genomic annotations to resolve isoform-level transcriptional activity.

37

Publications

3.6K

Citations

149

Authors

70

Institutions

Publications per year

2017–2026

12

Authors

149

  1. TJ
    Tao Jiang

    University of California, Riverside, United States

    3 publications · 164 citations

    3

    h-index

  2. CN
    Colin N. Dewey

    University of Wisconsin–Madison, United States

    2 publications · 1.4K citations

    2

    h-index

  3. YP
    Yudi Pawitan

    Karolinska Institutet, Sweden

    2 publications · 36 citations

    2

    h-index

  4. RS
    Ron Stewart

    Morgridge Institute for Research, United States

    2 publications · 1.4K citations

    2

    h-index

  5. CC
    Chung-Che Chang

    Methodist Hospital, United States

    2 publications · 47 citations

    2

    h-index

1–5 of 149

Venues

  1. Bioinformatics

    12 publications · 2.2K citations

    11

    h-index

  2. BMC Bioinformatics

    5 publications · 176 citations

    5

    h-index

  3. BMC Genomics

    3 publications · 312 citations

    3

    h-index

  4. Nucleic Acids Research

    2 publications · 97 citations

    2

    h-index

  5. Cell Systems

    1 publications · 10 citations

    1

    h-index

1–5