Searching journal content for articles similar to Murvai et al. 11 (8): 1410.

Displaying results 1-10 of 399
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  1. Method: Incorporating valuable prior knowledge to improve deep learning prediction of genetic perturbation responses
    • Xiuhao Fu,
    • Chao Yang,
    • Chunyan Cui,
    • Aoyun Geng,
    • Yidi Sun,
    • Chao Zha,
    • Feifei Cui,
    • Leyi Wei,
    • Quan Zou,
    • Xin Gao,
    • and Zilong Zhang
    Genome Res. March 26, 2026 gr.281523.125; Published in Advance March 26, 2026, doi:10.1101/gr.281523.125
    ...and gene regulatory networks. 588 Nat Methods 20: 1355-1367. 589 Bunne C, Roohani Y, Rosen Y, Gupta A, Zhang X, Roed M, Alexandrov T, AlQuraishi 590 M, Brennan P, Burkhardt DB et al. 2024. How to build the virtual cell with 591 artificial intelligence: Priorities and opportunities. Cell 187: 7045-7063. 592...
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  2. Method: Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN
    • Wenhao Zhang,
    • Lan Cao,
    • Xiaoxuan Gu,
    • Yongyu Long,
    • and Ying Wang
    Genome Res. January 2026 36: 142-158; Published in Advance October 9, 2025, doi:10.1101/gr.280757.125
    ...to comprehensively map GRNs (Badia-i-Mompel et al. 2023). High-throughput RNA sequencing (RNA-seq), which enables -wide analysis of the cellular transcriptome on bulk cells (Ozsolak and Milos 2011), has revolutionized GRN inference by enabling the computational derivation of regulatory networks from gene expression...
  3. Review: Artificial intelligence and machine learning in cell-free-DNA-based diagnostics
    • W.H. Adrian Tsui,
    • Spencer C. Ding,
    • Peiyong Jiang,
    • and Y.M. Dennis Lo
    Genome Res. January 2025 35: 1-19; doi:10.1101/gr.278413.123
    ..., including inter-pulse duration (IPD) and pulse width (PW), are affected by base modifications. (Middle) The HK model, an AI-based method that employs a convolutional neural network (CNN). This model is trained using combined kinetic signals and sequence context from a large amount of measurement windows...
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  4. Method: Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet
    • Chuanyuan Wang and
    • Zhi-Ping Liu
    Genome Res. February 2025 35: 340-354; Published in Advance December 18, 2024, doi:10.1101/gr.279551.124
    ...and genetic contexts. Here, we propose a discrete diffusion generation model, called DigNet, capable of generating corresponding GRNs from high-throughput single-cell RNA sequencing (scRNA-seq) data. DigNet embeds the network generation process into a multistep recovery procedure with Markov properties. Each...
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  5. Research: Analyzing super-enhancer temporal dynamics reveals potential critical enhancers and their gene regulatory networks underlying skeletal muscle development
    • Song Zhang,
    • Chao Wang,
    • Shenghua Qin,
    • Choulin Chen,
    • Yongzhou Bao,
    • Yuanyuan Zhang,
    • Lingna Xu,
    • Qingyou Liu,
    • Yunxiang Zhao,
    • Kui Li,
    • Zhonglin Tang,
    • and Yuwen Liu
    Genome Res. December 2024 34: 2190-2202; Published in Advance October 21, 2024, doi:10.1101/gr.278344.123
    ...networks during PSM development. (1) CUT&Tag libraries from frozen PSM samples across five developmental stages are produced and sequenced. (PSM) porcine skeletal muscle. (2) H3K27ac peaks are identified at each developmental stage and filtered for intragroup consistency. (IDR) irreproducibility discovery...
  6. Method: Sequential regulatory activity prediction across chromosomes with convolutional neural networks
    • David R. Kelley,
    • Yakir A. Reshef,
    • Maxwell Bileschi,
    • David Belanger,
    • Cory Y. McLean,
    • and Jasper Snoek
    Genome Res. May 2018 28: 739-750; Published in Advance March 27, 2018, doi:10.1101/gr.227819.117
    ...profiles in large mammalian s from DNA sequence alone. By use of convolutional neural networks, this system identifies promoters and distal regulatory elements and synthesizes their content to make effective gene expression predictions. We show that model predictions for the influence of genomic variants...
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  7. Method: Semisupervised adversarial neural networks for single-cell classification
    • Jacob C. Kimmel and
    • David R. Kelley
    Genome Res. October 2021 31: 1781-1793; Published in Advance February 24, 2021, doi:10.1101/gr.268581.120
    ...gradients (colored arrows) encourage cells of the same type to mix in the scNym embedding.The scNym classifier learns a representation of cell identity in the hidden neural network layers where cell types are linearly separable. Alongside, we train an adversarial model to predict the domain of origin...
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  8. Review: Advancing genomic technologies and clinical awareness accelerates discovery of disease-associated tandem repeat sequences
    • Terence Gall-Duncan,
    • Nozomu Sato,
    • Ryan K.C. Yuen,
    • and Christopher E. Pearson
    Genome Res. January 2022 32: 1-27; Published in Advance December 29, 2021, doi:10.1101/gr.269530.120
    .... For example, the 2009 identification of the mutation that causes SCA31 was achieved solely by “traditional” methods: bacterial artificial chromosome (BAC)-based cloning, Sanger sequencing, and Southern blot, with targeted shotgun resequencing (Sato et al. 2009).The techniques employed by the Human Genome...
  9. Method: Haplotype and population structure inference using neural networks in whole-genome sequencing data
    • Jonas Meisner and
    • Anders Albrechtsen
    Genome Res. August 2022 32: 1542-1552; Published in Advance July 6, 2022, doi:10.1101/gr.276813.122
    ...Haplotype and population structure inference using neural networks in whole- sequencing data Jonas Meisner and Anders Albrechtsen Department of Biology, Bioinformatics Center, University of Copenhagen, DK-2200 Copenhagen, Denmark Corresponding author: jonas...
  10. Method: Identification of phenotype-specific networks from paired gene expression–cell shape imaging data
    • Charlie George Barker,
    • Eirini Petsalaki,
    • Girolamo Giudice,
    • Julia Sero,
    • Emmanuel Nsa Ekpenyong,
    • Chris Bakal,
    • and Evangelia Petsalaki
    Genome Res. April 2022 32: 750-765; Published in Advance February 23, 2022, doi:10.1101/gr.276059.121
    ...the proteins PAX7, PTEN, and PPARGC1A.Small-molecule inhibitors targeting kinases in our network significantly perturb cell morphologyTo validate our network, we used an independent data set to evaluate whether perturbing the function of kinases within our predicted network would produce a significant effect...
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