Searching journal content for articles similar to Vrček et al. 35 (4): 839.

Displaying results 1-7 of 7
For checked items
  1. Method: Polishing copy number variant calls on exome sequencing data via deep learning
    • Furkan Özden,
    • Can Alkan,
    • and A. Ercüment Çiçek
    Genome Res. June 2022 32: 1170-1182; Published in Advance June 13, 2022, doi:10.1101/gr.274845.120
    ..., and sample batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data, and learns to correct the copy number variations reported by any off-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show...
  2. Method: Characterization of network hierarchy reflects cell state specificity in genome organization
    • Jingyao Wang,
    • Yue Xue,
    • Yueying He,
    • Hui Quan,
    • Jun Zhang,
    • and Yi Qin Gao
    Genome Res. February 2023 33: 247-260; Published in Advance February 24, 2023, doi:10.1101/gr.277206.122
    ...of transcription program in crucial processes including cancer and cell development, but a unified framework for characterizing chromatin structural evolution remains to be established. Here, we performed graph inferences on Hi-C data sets and derived the chromatin contact networks. We discovered significant...
  3. Method: A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction
    • Yuchun Guo,
    • Kevin Tian,
    • Haoyang Zeng,
    • Xiaoyun Guo,
    • and David Kenneth Gifford
    Genome Res. June 2018 28: 891-900; Published in Advance April 13, 2018, doi:10.1101/gr.226852.117
    ...motif models in predicting in vitro binding sites. KMAC also identifies correct motifs in more experiments than five state-of-the-art motif discovery methods. In addition, KSM-derived features outperform both PWM and deep learning model derived sequence features in predicting differential regulatory...
  4. Method: Diffusion-enhanced characterization of 3D chromatin structure reveals its linkage to gene regulatory networks and the interactome
    • Yueying He,
    • Yue Xue,
    • Jingyao Wang,
    • Yupeng Huang,
    • Lu Liu,
    • Yanyi Huang,
    • and Yi Qin Gao
    Genome Res. August 2023 33: 1354-1368; Published in Advance July 25, 2023, doi:10.1101/gr.277737.123
    ...are heavily involved in the establishment of different cell states. We have developed a diffusion-based method, Hi-C to geometry (CTG), to obtain reliable geometric information on the chromatin from Hi-C data. CTG produces a consistent and reproducible framework for the 3D genomic structure and provides...
  5. Method: Anchor: trans-cell type prediction of transcription factor binding sites
    • Hongyang Li,
    • Daniel Quang,
    • and Yuanfang Guan
    Genome Res. February 2019 29: 281-292; Published in Advance December 19, 2018, doi:10.1101/gr.237156.118
    ...and Kanehisa 1992). Recent development of deep learning algorithms takes advantage of large omics-scale data sets and predicts -wide regulatory function de novo from sequences (Alipanahi et al. 2015; Zhou and Troyanskaya 2015; Kelley et al. 2016; Quang and Xie 2016). Unfortunately, these computational methods...
  6. Editorial: Long reads decipher genomes and transcriptomes and offer novel insights into biology and diseases
    • Ana Conesa,
    • Alexander Hoischen,
    • and Fritz J. Sedlazeck
    Genome Res. April 2025 35: xi-xiv; doi:10.1101/gr.280665.125
    ...syndrome. Genome Res (this issue) 35: 786–797. doi:10.1101/gr.279331.124 ↵Vrček L, Bresson X, Laurent T, Schmitz M, Kawaguchi K, Šikić M. 2025. Geometric deep learning framework for de novo assembly. Genome Res (this issue) 35: 839–849. doi:10.1101/gr.279307.124 ↵Wang M, Li Y, Wang J, Oh SH, Cao Y, Chen R...
  7. Method: Dissecting multilayer cell–cell communications with signaling feedback loops from spatial transcriptomics data
    • Lulu Yan,
    • Jinyu Cheng,
    • Qing Nie,
    • and Xiaoqiang Sun
    Genome Res. June 2025 35: 1400-1414; Published in Advance April 22, 2025, doi:10.1101/gr.279857.124
    ...expressions using explainable machine learning. Together, our method provides a unified framework for multiscale inference of spatial CCC through a model and data codriven approach.ResultsOverview of stMLnetstMLnet requires a gene expression matrix, a cell location matrix, and cell type annotations from ST...
For checked items

Search Results

New Search

Modify Results

Citation format:
Results / page:
Results order:

This search

  • Alert me when new articles matching this search are published
  • Download all citations on this page to my citation manager

Preprint Server



Current Issue

  1. March 2026, 36 (3)
  1. Current Issue
  1. Alert me to new issues of Genome Research