Searching journal content for articles similar to Gettins 10 (12): 1833.

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  1. Corrigendum: Corrigendum: Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer
    • Milad Razavi-Mohseni,
    • Weitai Huang,
    • Yu A. Guo,
    • Dustin Shigaki,
    • Shamaine Wei Ting Ho,
    • Patrick Tan,
    • Anders J. Skanderup,
    • and Michael A. Beer
    Genome Res. February 2026 36: 432; doi:10.1101/gr.281294.125
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  2. Research: Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer
    • Milad Razavi-Mohseni,
    • Weitai Huang,
    • Yu A. Guo,
    • Dustin Shigaki,
    • Shamaine Wei Ting Ho,
    • Patrick Tan,
    • Anders J. Skanderup,
    • and Michael A. Beer
    Genome Res. May 2024 34: 680-695; Published in Advance May 22, 2024, doi:10.1101/gr.278565.123
    .... Using the ATAC-seq data, we developed a machine learning approach to determine the transcription factors (TFs) regulating the subtypes of GC. We identified TFs driving the mesenchymal (RUNX2, ZEB1, SNAI2, AP-1 dimer) and the epithelial (GATA4, GATA6, KLF5, HNF4A, FOXA2, GRHL2) states in GC. We...
  3. Method: Nematode gene annotation by machine-learning-assisted proteotranscriptomics enables proteome-wide evolutionary analysis
    • Alejandro Ceron-Noriega,
    • Miguel V. Almeida,
    • Michal Levin,
    • and Falk Butter
    Genome Res. January 2023 33: 112-128; Published in Advance January 18, 2023, doi:10.1101/gr.277070.122
    ...of nematodes is believed to date back to ∼650–750 million years, generating a large and phylogenetically diverse group to be explored. However, for most species high-quality gene annotations are incomprehensive or missing. Combining short-read RNA sequencing with mass spectrometry–based proteomics and machine...
  4. Method: A somatic hypermutation–based machine learning model stratifies individuals with Crohn's disease and controls
    • Modi Safra,
    • Lael Werner,
    • Ayelet Peres,
    • Pazit Polak,
    • Naomi Salamon,
    • Michael Schvimer,
    • Batia Weiss,
    • Iris Barshack,
    • Dror S. Shouval,
    • and Gur Yaari
    Genome Res. January 2023 33: 71-79; Published in Advance December 16, 2022, doi:10.1101/gr.276683.122
    ...A somatic hypermutation–based machine learning model stratifies individuals with Crohn's disease and controls Modi Safra1,2,7, Lael Werner3,4,7, Ayelet Peres1,2, Pazit Polak1,2, Naomi Salamon5, Michael Schvimer6, Batia Weiss4,5, Iris Barshack4,6, Dror S. Shouval3,4,8 and Gur Yaari1,2,8 1The...
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  5. 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
    ...technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known...
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  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: A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing
    • Brian Aevermann,
    • Yun Zhang,
    • Mark Novotny,
    • Mohamed Keshk,
    • Trygve Bakken,
    • Jeremy Miller,
    • Rebecca Hodge,
    • Boudewijn Lelieveldt,
    • Ed Lein,
    • and Richard H. Scheuermann
    Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
    ...research, from defining immune cell types by surface protein expression to defining diseases by their molecular drivers. Here, we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the nonlinear attributes of random forest feature selection...
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  8. Method: A machine-learning approach for accurate detection of copy number variants from exome sequencing
    • Vijay Kumar Pounraja,
    • Gopal Jayakar,
    • Matthew Jensen,
    • Neil Kelkar,
    • and Santhosh Girirajan
    Genome Res. July 2019 29: 1134-1143; Published in Advance June 6, 2019, doi:10.1101/gr.245928.118
    ...not have consensus from multiple callers. Here, we present CN-Learn, a machine-learning framework that integrates calls from multiple CNV detection algorithms and learns to accurately identify true CNVs using caller-specific and genomic features from a small subset of validated CNVs. Using CNVs predicted...
  9. Method: Automated interpretable artificial intelligence genomic prediction with AIGP
    • Lei Wei,
    • Ziqin Jiang,
    • Baoliang Fan,
    • Yidan Yan,
    • Zhenqiang Xu,
    • Xiaoxiang Hu,
    • and Yuzhe Wang
    Genome Res. April 2026 36: 814-826; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
    ....edu.cnAbstractPredicting phenotypes from genomic mutations remains a major genetic challenge. Traditional statistical methods (such as GBLUP and BayesR) have limitations, including reliance on artificial prior assumptions, and hard to capture epistatic effects. Machine learning (ML) has emerged as a powerful alternative for genomic...
  10. Method: Robust and efficient annotation of cell states through gene signature scoring
    • Laure Ciernik,
    • Agnieszka Kraft,
    • Florian Barkmann,
    • Josephine Yates,
    • and Valentina Boeva
    Genome Res. March 2026 36: 630-644; Published in Advance February 18, 2026, doi:10.1101/gr.280926.125
    ...Laure Ciernik1,2,3,8, Agnieszka Kraft1,4,5,8,9,10, Florian Barkmann1, Josephine Yates1,4,6,11 and Valentina Boeva1,4,6,7 1ETH Zurich, Department of Computer Science, Institute for Machine Learning, 8092 Zurich, Switzerland; 2Machine Learning Group, Technische Universität Berlin, 10587 Berlin...
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