Searching journal content for articles similar to Wei et al. 36 (4): 814.

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  1. 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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  2. Method: Autoencoders for genomic variation analysis
    • Margarita Geleta,
    • Daniel Mas Montserrat,
    • Xavier Giro-i-Nieto,
    • and Alexander G. Ioannidis
    Genome Res. February 2026 36: 348-360; Published in Advance January 20, 2026, doi:10.1101/gr.280086.124
    ...are needed in order to properly capture the genetic composition of populations. Here, we explore deep learning techniques, namely, variational autoencoders (VAEs), to process genomic data from a population perspective. We show the power of VAEs for a variety of tasks relating to the interpretation...
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  3. 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
    ...), King Abdullah University of Science and Technology (KAUST), Thuwal, 239556900, Saudi Arabia 3Centre for Artificial Intelligence driven Drug Discovery, Faculty of Applied Science, Macao Polytechnic University, Macao SAR, China 4Institute of Fundamental and Frontier Sciences, University of Electronic...
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  4. Method: Interpretable phenotype decoding from multicondition sequencing data with ALPINE
    • Wei-Hao Lee,
    • Lechuan Li,
    • Ruth Dannenfelser,
    • and Vicky Yao
    Genome Res. December 2025 35: 2756-2769; Published in Advance October 22, 2025, doi:10.1101/gr.280566.125
    ...information while also providing interpretable insights into the genetic effects of these conditions. Current methods typically excel in only one of these areas. To address this gap, we have developed ALPINE, a supervised nonnegative matrix factorization (NMF) framework that effectively separates both...
  5. Method: Automated chromatin profiling with spa-ChIP-seq uncovers the impacts of condition variations
    • Yuwei Cao,
    • Lauren Patel,
    • Lauren Alcoser,
    • Eric Mendenhall,
    • Christopher Benner,
    • Sven Heinz,
    • and Alon Goren
    Genome Res. January 2026 36: 129-141; Published in Advance December 12, 2025, doi:10.1101/gr.281320.125
    ...Yuwei Cao1,2, Lauren Patel1,3, Lauren Alcoser4, Eric Mendenhall5, Christopher Benner6, Sven Heinz6 and Alon Goren1 1Department of Medicine, Division of Genomics & Precision Medicine, University of California San Diego, La Jolla, California 92093, USA; 2Bioinformatics and Systems Biology Graduate...
  6. Research: ACTB methylation regulates SMARCA4 genomic occupancy to promote translation and reduce adhesion in colorectal cancer cells
    • Elina Abaev-Schneiderman,
    • Linh Nguyen,
    • Raz Shalev,
    • Tzofit Elbaz Biton,
    • Anand Chopra,
    • Giritharan Jagadeesan,
    • Daniel Sevilla-Sanchez,
    • Nili Tickotsky Moskovitz,
    • Liron Levin,
    • Michal Feldman,
    • Capucine Van Rechem,
    • and Dan Levy
    Genome Res. March 25, 2026 ; Published in Advance March 25, 2026, doi:10.1101/gr.280534.125
    ...show that the genomic distribution of ACTB is SETD3-dependent and that this regulation modulates the transcription of genes involved in cell adhesion and mRNA translation in colorectal cancer cells. Proteomic analyses reveal that ACTB and SETD3 interact with multiple large protein complexes, including...
  7. Method: spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
    • Tianyu Liu,
    • Tinglin Huang,
    • Wengong Jin,
    • Tinyi Chu,
    • Rex Ying,
    • and Hongyu Zhao
    Genome Res. April 2026 36: 754-768; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...et al. 2024) to better predict survival or disease states of patients from The Cancer Genome Atlas (TCGA) database (Weinstein et al. 2013). Our results show the strong capacity of spRefine in transferring the learned knowledge to biomedical data with different resolutions and improving our...
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  8. 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
    ...performer, better preserving the signal’s shape. Our codebase and benchmarks provide practical tools for high-resolution chromatin modeling.Predicting chromatin accessibility from DNA sequence is one of the central challenges in regulatory genomics, with applications including the interpretation...
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  9. Research: Genomic regulatory landscape underlying the antiviral response of Atlantic salmon
    • Shahmir Naseer,
    • Thomas C Clark,
    • Bertrand Collet,
    • Pooran Dewari,
    • Damir Baranasic,
    • Daniel J Macqueen,
    • Pierre Boudinot,
    • and Samuel A.M. Martin
    Genome Res. October 22, 2025 gr.280579.125; Published in Advance October 22, 2025, doi:10.1101/gr.280579.125
    ...provides evidence for conserved transcription factors 26 (TFs) driving the interferon response by binding ISG promoters, including IRF8, IRF9, 27 STAT1, and STAT2. Regulatory elements within differentially expressed genes were 28 enriched for predicted binding sites for STAT6, PRDM1, IRF6, JDP2, NR2E1...
    OPEN ACCESS ARTICLEACCEPTED MANUSCRIPT
  10. Mini-Review: A Hitchhiker's Guide to long-read genomic analysis
    • Medhat Mahmoud,
    • Daniel P. Agustinho,
    • and Fritz J. Sedlazeck
    Genome Res. April 2025 35: 545-558; doi:10.1101/gr.279975.124
    ...initial data processing to variant calling and annotation, focusing on how these methods improve our ability to interpret a wide array of genomic variants. Additionally, we discuss the current challenges, limitations, and future directions in the field, offering a detailed examination of the state...
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