bioRxiv

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Illuminating the Virosphere’s Dark Matter using Hierarchical Deep Learning

bioRxiv Jan 1, 2025
Systematic discovery of novel viruses is essential for pandemic preparedness, understanding tumor-associated viruses, developing viral delivery systems, and advancing biomedical applications. Yet, the majority of sequences in metagenomic datasets lac...
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A Benchmark of Evo2 Genomic AI Models for Efficient and Practical Deployment

bioRxiv Jan 1, 2025
The rapid advancement of DNA foundation language models has brought about a transformative shift in genomics, allowing for the deciphering of intricate patterns and regulatory mechanisms embedded within DNA sequences. The genomic foundation model Evo...
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Learning the native-like codons with a 5’UTR and RNA secondary structure aided species-informed transformer model

bioRxiv Jan 1, 2025
Efficient protein expression across heterologous hosts remains a major challenge in synthetic biology, largely due to species-specific differences in codon usage and regulatory sequence context. A key difficulty lies in reconstructing the codon lands...
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Regularized Single-cell Imaging Enables Generalizable AI models for Stain-free Cell Viability Screening

bioRxiv Jan 1, 2025
Cell viability assays are essential tools in biomedical research and drug development. Artificial intelligence (AI) offers the potential to simplify these assays by predicting cell viability directly from brightfield microscopy images, but current mo...
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Octopamine signaling from clock neurons plays dual roles in Drosophila long-term memory

bioRxiv Jan 1, 2025
Circadian clock genes are best known for regulating circadian rhythms, but they also play crucial roles in memory processes. This suggests that memory is modulated by neural networks containing clock neurons, although the underlying mechanisms remain...
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DELPHAI, AI Agent for Predicting Drug Response and Resistance

bioRxiv Jan 1, 2025
Patient-derived organoids preserve critical tumor features and drug sensitivity patterns that mirror patient clinical responses, enabling single-cell RNA sequencing analysis of drug responses. Analyzing these perturbation data presents significant co...
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Non-Invasive Diagnostic Evaluation of Urinary Exosomal Let-7c Cluster Expression in Bladder Cancer Using Machine Learning Approaches

bioRxiv Jan 1, 2025
Bladder cancer (BCa) diagnosis typically relies on invasive cystoscopy, which is effective but costly and uncomfortable. Urinary microRNAs (miRNAs), especially exosomal ones, are promising non-invasive biomarkers due to their stability in biological ...
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Structured Multimodal Deep Learning improves Genomic Prediction in Future Environments

bioRxiv Jan 1, 2025
The development of prediction models for phenotypes as functions of genetics and environmental inputs is a long-standing challenge in genetics and plant breeding. Deep neural networks form a promising approach to this task, due to their capacity to a...
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Predicting the unseen: a diffusion-based debiasing framework for transcriptional response prediction at single-cell resolution

bioRxiv Jan 1, 2025
Predicting cellular responses to genetic perturbations is critical for advancing our understanding of gene regulation. While single-cell CRISPR perturbation assays such as Perturb-seq provide direct measurements of gene function, the scale of these e...
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Leveraging learned representations and multitask learning for lysine methylation site discovery

bioRxiv Jan 1, 2025
Lysine methylation is a dynamic and reversible post-translational modification of proteins carried out by lysine methyltransferase enzymes. The role of this modification in epigenetics and gene regulation is relatively well understood, but our unders...
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