Searching journal content for articles similar to Aravind 10 (8): 1074.

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  1. 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. April 2026 36: 769-784; Published in Advance March 26, 2026, doi:10.1101/gr.281350.125
    ...by dynamically weighing feature interactions based on contextual relevance, thereby improving the capacity to capture subtle regulatory relationships. Moreover, a feature recalibration module is applied to adaptively recalibrate feature importance, selectively amplifying biologically informative dimensions while...
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  2. Method: Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet
    • Yidi Deng,
    • Jiadong Mao,
    • Jarny Choi,
    • and Kim-Anh Lê Cao
    Genome Res. April 2026 36: 785-801; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
    ...Yidi Deng1,2, Jiadong Mao1,4, Jarny Choi3,4 and Kim-Anh Lê Cao1,4 1Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia; 2Research School of Finance, Actuarial Studies & Statistics, The Australian National University...
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  3. 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
    ...overlapping with unmappable regions, and ones were assigned otherwise.Adding this mappability information stabilized CNN and LSTM models but had no significant effect on dCNN or ConvNeXt-based models (Supplemental Fig. S7). This suggests that architectures capturing long-range contextual information...
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  4. Review: A systematic guide for identifying transcription factors that directly regulate the expression of a gene of interest
    • Andrew D. Bates,
    • Dawid Grzela,
    • Maciej Studzian,
    • Louise Brennan,
    • Moli Williams,
    • Conor Fawcett,
    • Beth Hammond,
    • Manreen Grewal,
    • Marcin Ratajewski,
    • Lukasz Pulaski,
    • and Urszula L. McClurg
    Genome Res. March 2026 36: 433-459; Published in Advance February 17, 2026, doi:10.1101/gr.281154.125
    ...are prone to false positives, whereas fully endogenous systems, although biologically informative, carry a higher risk of false negatives owing to contextual dependencies such as chromatin state, cell cycle stage, and transcriptional noise. It is critical to carefully select experimental configurations...
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  5. Resource: The SynMall resource for characterizing the functional impact of synonymous variation
    • Chen Ye,
    • Xiaoyan Li,
    • Na Cheng,
    • Yansen Su,
    • and Junfeng Xia
    Genome Res. February 2026 36: 421-431; Published in Advance December 17, 2025, doi:10.1101/gr.281257.125
    ...non-human species. For each human sSNV, SynMall provides multilevel annotations that combine American College of Medical Genetics and Genomics (ACMG)–aligned variant interpretation information, such as allele frequencies and functional effects, with more than 100 descriptors at the DNA, RNA...
  6. 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
    ...an overly large window size, which might lose fine-grained structure) and encoding efficiency (not using an excessively small window size, which would require a large bottleneck to capture all local information). In our analysis, w = 2500 results in a good compromise between capturing local genomic...
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  7. Method: Long-read reconstruction of many diverse haplotypes with devider
    • Jim Shaw,
    • Christina Boucher,
    • Yun William Yu,
    • Noelle Noyes,
    • and Heng Li
    Genome Res. December 2025 35: 2637-2649; Published in Advance September 23, 2025, doi:10.1101/gr.280510.125
    ...and Information Science and Engineering, University of Florida, Gainesville, Florida 32611, USA; 4Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA; 5Department of Veterinary Population Medicine, University of Minnesota, St. Paul, Minnesota...
  8. Method: Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method
    • Quanyou Cai,
    • Lihui Lin,
    • Xin Liu,
    • and Jiekai Chen
    Genome Res. November 2025 35: 2513-2526; Published in Advance October 22, 2025, doi:10.1101/gr.280532.125
    ...comprising millions of cells. Here, we introduce Polyomino, an intelligent region-allocation method inspired by the region-of-interest (ROI) concept from image processing. By using gradient descent, Polyomino allocates cells to structured spatial regions that match the most significant biological information...
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  9. 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
    ...information based on the RNA-seq data from The Cancer Genome Atlas (TCGA) (Weinstein et al. 2013). Here, we utilized the imputed profiles to select marker genes across the samples from the HEST-1k data set with different disease states and included the overlapped genes between the marker genes and genes from...
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  10. 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
    ...prediction and emphasize the importance of explainable ML approaches, integration of prior information, and parameter optimization. The AIGP toolkit enables automated model optimization and interpretability, making ML-driven genomic selection more accessible and providing new tools to support genomic...
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