Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

Autor: Andreas Tjärnberg, Maggie Beheler-Amass, Christopher A. Jackson, Lionel A. Christiaen, David Gresham, Richard Bonneau

Publikováno v: Genome Biology, Vol 25, Iss 1, Pp 1-28 (2024)

Abstract Background Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of re

Externí odkaz: https://doaj.org/article/2d0a305582554742970ec4bccf370855

Knowledge of the perturbation design is essential for accurate gene regulatory network inference

Autor: Deniz Seçilmiş, Thomas Hillerton, Andreas Tjärnberg, Sven Nelander, Torbjörn E. M. Nordling, Erik L. L. Sonnhammer

Publikováno v: Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)

Abstract The gene regulatory network (GRN) of a cell executes genetic programs in response to environmental and internal cues. Two distinct classes of methods are used to infer regulatory interactions from gene expression: those that only use observe

Externí odkaz: https://doaj.org/article/79637b11433a4eada747b6b0a92cb23c

Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

Autor: Daniel Morgan, Matthew Studham, Andreas Tjärnberg, Holger Weishaupt, Fredrik J. Swartling, Torbjörn E. M. Nordling, Erik L. L. Sonnhammer

Publikováno v: Scientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)

Abstract The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore h

Externí odkaz: https://doaj.org/article/82d92cd771e2430780af2d03d54742cb

Cell cycle-driven transcriptome maturation confers multilineage competence to cardiopharyngeal progenitors.

Autor: Bernadskaya YY; Department of Biology, New York University, New York, NY, USA., Kuan A; Department of Biology, New York University, New York, NY, USA., Tjärnberg A; Department of Biology, New York University, New York, NY, USA., Brandenburg J; Michael Sars Centre, University of Bergen, Bergen, Norway., Zheng P; Fang Centre, Ocean University of China, Qingdao, China., Wiechecki K; Department of Biology, New York University, New York, NY, USA., Kaplan N; Department of Biology, New York University, New York, NY, USA., Failla M; Michael Sars Centre, University of Bergen, Bergen, Norway.; Department of Biology, New York University, New York, NY, USA., Bikou M; Department of Biology, New York University, New York, NY, USA., Madilian O; Department of Biology, New York University, New York, NY, USA., Wang W; Department of Biology, New York University, New York, NY, USA.; Fang Centre, Ocean University of China, Qingdao, China., Christiaen L; Michael Sars Centre, University of Bergen, Bergen, Norway.; Department of Biology, New York University, New York, NY, USA.

Publikováno v: BioRxiv : the preprint server for biology [bioRxiv] 2024 Jul 23. Date of Electronic Publication: 2024 Jul 23.

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data.

Autor: Andreas Tjärnberg, Omar Mahmood, Christopher A Jackson, Giuseppe-Antonio Saldi, Kyunghyun Cho, Lionel A Christiaen, Richard A Bonneau

Publikováno v: PLoS Computational Biology, Vol 17, Iss 1, p e1008569 (2021)

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for

Externí odkaz: https://doaj.org/article/a8a4056d539749db956875c60e591791