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  1. 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. February 3, 2026 ; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...), whether usingGNN to encode spot embeddings, whether using variation auto-encoder, whether modeling the decoder output with distribution other than Normal distribution. The results of these settings are shown in Supplemental Figure 3A–C. We found that making the current model more complicated does...
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  2. Perspective: Applications of single-cell genomics and computational strategies to study common disease and population-level variation
    • Benjamin J. Auerbach,
    • Jian Hu,
    • Muredach P. Reilly,
    • and Mingyao Li
    Genome Res. October 2021 31: 1728-1741; doi:10.1101/gr.275430.121
    ...–based methods have been developed for this purpose. For example, scVI (Lopez et al. 2018) removes batch effects by conditioning on batch information in a variational autoencoder, which learns a nonlinear embedding of cells; SAVERCAT (Huang et al. 2020) uses a conditional variational autoencoder to remove batch...
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  3. Method: Unified integration of spatial transcriptomics across platforms with LLOKI
    • Ellie Haber,
    • Ajinkya Deshpande,
    • Jian Ma,
    • and Spencer Krieger
    Genome Res. December 2025 35: 2722-2733; Published in Advance November 13, 2025, doi:10.1101/gr.280803.125
    ...sets, confirming the importance of this step in our workflow.Batch integration with LLOKI-CAECell embeddings from LLOKI-FP effectively capture biological variation within each ST data set but still exhibit large batch effects between technologies. To address this, we develop a conditional autoencoder...
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  4. Method: High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND
    • Kehan Lang,
    • Chenyang Jia,
    • Siyu Li,
    • Yi Guo,
    • Dingjun Hu,
    • and Shengquan Chen
    Genome Res. March 26, 2026 ; Published in Advance March 26, 2026, doi:10.1101/gr.281350.125
    ...developed to enable cross-modality translation between scRNA-seq and scDNAm data. For example, scCross leverages variational autoencoders (VAEs), generative adversarial networks, and the mutual nearest neighbors technique for modality alignment (Yang et al. 2024), whereas MAPLE utilizes an ensemble...
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  5. 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
    .... 2012), making it sufficient to capture relevant regulatory variation. The 2048-bp input window also provides adequate genomic context for the models to learn both local and distal sequence features.View larger version: In this window In a new window Figure 1. Overview of the benchmarking setup. (A...
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  6. Method: Quantifying pathological progression from single-cell transcriptomic data with scPSS
    • Samin Rahman Khan,
    • M Saifur Rahman,
    • M. Sohel Rahman,
    • and Md Abul Hassan Samee
    Genome Res. February 2026 36: 375-386; Published in Advance January 14, 2026, doi:10.1101/gr.280411.125
    ...) of disease-relevant cellular states. Among them, scIDIST (Wehbe et al. 2025) integrates autoencoder-based dimensionality reduction with weak supervision, producing probabilistic disease labels. The labels are then used to train a neural network that assigns continuous disease progression scores to individual...
  7. Method: Deep structural clustering reveals hidden systematic biases in RNA sequencing data
    • Qiang Su,
    • Yi Long,
    • Deming Gou,
    • Junmin Quan,
    • Xiaoming Zhou,
    • and Qizhou Lian
    Genome Res. November 2025 35: 2563-2577; Published in Advance September 19, 2025, doi:10.1101/gr.280713.125
    ...-dimensional RNA structures on local sequencing efficiency using an innovative unsupervised variational autoencoder-Gaussian mixture model (VAE-GMM). The VAE-GMM effectively captures intricate high-dimensional k-mer structural similarities by learning compact latent representations, which reduces dimensionality...
  8. Method: Graph-based deep reinforcement learning for haplotype assembly with Ralphi
    • Enzo Battistella,
    • Anant Maheshwari,
    • Barış Ekim,
    • Bonnie Berger,
    • and Victoria Popic
    Genome Res. December 2025 35: 2617-2625; Published in Advance November 14, 2025, doi:10.1101/gr.280569.125
    ..., it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.References ↵The 1000 Genomes Project Consortium. 2015. A global reference for human genetic variation. Nature 526: 68–74. doi:10.1038/nature15393 ↵Adey A...
  9. Method: Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach
    • Fabien Wehbe,
    • Levi Adams,
    • Jordan Babadoudou,
    • Samantha Yuen,
    • Yoon-Seong Kim,
    • and Yoshiaki Tanaka
    Genome Res. January 2025 35: 135-146; Published in Advance December 2, 2024, doi:10.1101/gr.278812.123
    ...the dimensionality reduction from a pretrained autoencoder.Dimensional reduction by variational autoencoder: variational_autoencoder.pyAs an alternative way for the dimensional reduction, we provide another Python script, variational_autoencoder.py. This script implements a variational autoencoder that employs...
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  10. Method: BayesRVAT enhances rare-variant association testing through Bayesian aggregation of functional annotations
    • Antonio Nappi,
    • Liubov Shilova,
    • Theofanis Karaletsos,
    • Na Cai,
    • and Francesco Paolo Casale
    Genome Res. December 2025 35: 2682-2690; Published in Advance October 24, 2025, doi:10.1101/gr.280689.125
    ...of -wide variants. Nat Biotechnol doi:10.1038/s41587-024-02511-w ↵Brandes N, Linial N, Linial M. 2020. PWAS: proteome-wide association study—linking genes and phenotypes by functional variation in proteins. Genome Biol 21: 173. doi:10.1186/s13059-020-02089-x ↵Brandes N, Goldman G, Wang CH, Ye CJ, Ntranos V...
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