Searching journal content for articles similar to Mott et al. 12 (8): 1168.

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  1. Method: A scalable computational framework for predicting gene expression from candidate cis-regulatory elements
    • Qinhu Zhang,
    • Siguo Wang,
    • Zhipeng Li,
    • Wenzheng Bao,
    • Wenjian Liu,
    • and De-Shuang Huang
    Genome Res. February 2026 36: 361-374; Published in Advance January 16, 2026, doi:10.1101/gr.281219.125
    ...for predicting gene expression (ScPGE) from discrete candidate CREs (cCREs). ScPGE assembles DNA sequences, transcription factor (TF) binding scores, and epigenomic tracks from discrete cCREs into three-dimensional tensors, and then models the relationships between cCREs and genes by combining convolutional...
    OPEN ACCESS ARTICLE
  2. Method: Modeling and predicting cancer clonal evolution with reinforcement learning
    • Stefan Ivanovic and
    • Mohammed El-Kebir
    Genome Res. July 2023 33: 1078-1088; Published in Advance June 21, 2023, doi:10.1101/gr.277672.123
    ...@illinois.eduAbstractCancer results from an evolutionary process that typically yields multiple clones with varying sets of mutations within the same tumor. Accurately modeling this process is key to understanding and predicting cancer evolution. Here, we introduce clone to mutation (CloMu), a flexible and low-parameter tree...
  3. Method: Predicting genotype-specific gene regulatory networks
    • Deborah Weighill,
    • Marouen Ben Guebila,
    • Kimberly Glass,
    • John Quackenbush,
    • and John Platig
    Genome Res. March 2022 32: 524-533; Published in Advance February 22, 2022, doi:10.1101/gr.275107.120
    ...individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, expression quantitative trait locus (eQTL) data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses a technique known...
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  4. Method: SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection
    • Shibiao Wan,
    • Junil Kim,
    • and Kyoung Jae Won
    Genome Res. February 2020 30: 205-213; Published in Advance January 28, 2020, doi:10.1101/gr.254557.119
    ...Sciences, University of Copenhagen, 2200 Copenhagen North, Denmark Corresponding author: kyoung.won@bric.ku.dkAbstractTo process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection...
  5. Method: Predicting transfer RNA gene activity from sequence and genome context
    • Bryan P. Thornlow,
    • Joel Armstrong,
    • Andrew D. Holmes,
    • Jonathan M. Howard,
    • Russell B. Corbett-Detig,
    • and Todd M. Lowe
    Genome Res. January 2020 30: 85-94; Published in Advance December 19, 2019, doi:10.1101/gr.256164.119
    ...Predicting transfer RNA gene activity from sequence and context Bryan P. Thornlow1, Joel Armstrong1,2, Andrew D. Holmes1, Jonathan M. Howard1, Russell B. Corbett-Detig1,2 and Todd M. Lowe1,2 1Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA; 2...
  6. Method: Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model
    • Li Tang,
    • Matthew C. Hill,
    • Jun Wang,
    • Jianxin Wang,
    • James F. Martin,
    • and Min Li
    Genome Res. December 2020 30: 1835-1845; Published in Advance November 12, 2020, doi:10.1101/gr.264606.120
    ...Predicting unrecognized enhancer-mediated topology by an ensemble machine learning model Li Tang1,2, Matthew C. Hill3, Jun Wang4, Jianxin Wang1, James F. Martin2,3,5,6 and Min Li1 1Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University...
  7. Method: Predicting human genes susceptible to genomic instability associated with Alu/Alu-mediated rearrangements
    • Xiaofei Song,
    • Christine R. Beck,
    • Renqian Du,
    • Ian M. Campbell,
    • Zeynep Coban-Akdemir,
    • Shen Gu,
    • Amy M. Breman,
    • Pawel Stankiewicz,
    • Grzegorz Ira,
    • Chad A. Shaw,
    • and James R. Lupski
    Genome Res. August 2018 28: 1228-1242; Published in Advance June 15, 2018, doi:10.1101/gr.229401.117
    ...and predicted ∼18% of them are potentially susceptible to AAMR. We further determined the relative risk of AAMR in 12,074 OMIM genes using the count of predicted CNV-Alu pairs and experimentally validated the predictions with 89 samples selected by correlating predicted hotspots with a database of CNVs...
  8. Method: Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints
    • Ron Schwessinger,
    • Maria C. Suciu,
    • Simon J. McGowan,
    • Jelena Telenius,
    • Stephen Taylor,
    • Doug R. Higgs,
    • and Jim R. Hughes
    Genome Res. October 2017 27: 1730-1742; Published in Advance September 13, 2017, doi:10.1101/gr.220202.117
    ...Institute of Molecular Medicine, Oxford OX3 9DS, United Kingdom In the era of -wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory...
  9. Method: Automated interpretable artificial intelligence genomic prediction with AlGP
    • Lei Wei,
    • Ziqin Jiang,
    • Baoliang Fan,
    • Yidan Yan,
    • Zhenqiang Xu,
    • Xiaoxiang Hu,
    • and Yuzhe Wang
    Genome Res. March 5, 2026 ; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
    ...that the feature negatively reduced the predicted value. Dimensionality reduction method Dimensionality reduction involves projecting genotype files using twomethods: PCA and PHATE. PCA transforms raw data into a set of linearly independent representations in each dimension using a linear transformation. PHATE...
  10. Method: Early feature extraction drives model performance in high-resolution chromatin accessibility prediction
    • Aayush Grover,
    • Till Muser,
    • Liine Kasak,
    • Lin Zhang,
    • Ekaterina Krymova,
    • and Valentina Boeva
    Genome Res. March 2026 36: 619-629; Published in Advance January 12, 2026, doi:10.1101/gr.281042.125
    ..., 8092 Zurich, Switzerland; 4Institut Cochin, Inserm U1016, CNRS UMR 8104, Université Paris Cité, 75014 Paris, France Corresponding authors: ekaterina.krymova@sdsc.ethz.ch, valentina.boeva@inf.ethz.chAbstractFine-grained prediction of chromatin accessibility from DNA sequence is a foundational step...
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