Cancer genomics and diagnostics is a rapidly evolving field in which identifying which topics attract early citation prominence can inform laboratory investment, clinical translation, and research strategy. We developed a bibliometric framework to id...
Spatial transcriptomics enables the study of gene expression within its native tissue context, providing critical insights into cellular organization and microenvironment-driven biological processes. A key challenge in this field is spatial domain id...
Accurate immune receptor design requires modeling the coupled variation of amino-acid sequence, full-atom conformation, and target-binding geometry across antibodies, nanobodies, and T-cell receptors (TCRs). Existing methods often address only part o...
High-throughput transcriptomics has transformed disease biology, but its outputs often remain fragmented into gene and pathway lists that are difficult to compare across conditions or use for human-AI interpretation. We developed a five-dimensional (...
The African clawed frog Xenopus laevis is a widely utilized model organism in biomedical research; however, significant challenges in experimental reproducibility and colony management remain. A major obstacle lies in the reliable identification of i...
Clinical treatment planning of near-infrared (NIR) brain stimulation requires patient-specific light dosimetry to optimize fluence delivery to cortical targets. The gold-standard Monte Carlo (MC) photon transport forward solver is accurate but comput...
Background and objectives: In recent years, the need to develop analytical strategies for healthy aging has assumed great importance. In this study, we introduce DyViA, a generative artificial intelligence (genAI) platform that can construct personal...
We present the BCCWJ-Brain dataset, a multi-modal neuroimaging resource comprising functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and electroencephalography (EEG) data recorded from native Japanese speakers reading newspa...
Plant diseases remain a major challenge to global food production, and timely, accurate, and scalable detection of plant stress is critical to reducing these losses. Recent advances in digital imaging and artificial intelligence offer unprecedented o...
Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulnerabilities. Graph neural networks (GNNs) integrate molecular interaction networks with gene-level fea...
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