AIMC Journal:
bioRxiv

Showing 2581 to 2590 of 4979 articles

BEACON: predicting side effects and therapeutics outcomes to drugs by Bridging knowlEdge grAph with CONtextual language model

bioRxiv Jan 30, 2026
Biomedical knowledge graphs encode millions of relationships between drugs, proteins, pathways, and diseases, yet translating this structured knowledge into accurate predictions remains challenging. Existing deep learning approaches, including graph ...
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Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-art Solutions for Biomedical Machine Learning Tasks

bioRxiv Jan 30, 2026
Motivation: Extracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeutics. The quantity, quality, and resolution of biomedical data constantly evolves, requiring the autom...
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A systematic assessment of machine learning for structural variant filtering

bioRxiv Jan 30, 2026
Background: Accurate discrimination of true structural variants (SVs) from artifacts in long-read sequencing data remains a critical bottleneck. Numerous machine learning solutions have been proposed, ranging from classical models using engineered fe...
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DVPNet: A New XAI-Based Interpretable Genetic Profiling Framework Using Nucleotide Transformer and Probabilistic Circuits

bioRxiv Jan 30, 2026
This research provides an XAI-driven genetic profiling approach that may contribute to scientific discoveries in genetic research. We propose a new explainable AI (XAI) classification algorithm that combines probabilistic circuits with the Nucleotide...
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clinTALL: machine learning-driven multimodal subtypeclassification and treatment outcome prediction in pediatric T-ALL

bioRxiv Jan 30, 2026
Background: Childhood T-lineage acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy with poor prognosis. Differently from B-cell precursor ALL, T-ALL lacks effective risk stratification strategies. A recent study has integrat...
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Zero-shot biological reasoning with open-weights large language models reproduces CRISPR screen based prediction of synthetic lethal interactions.

bioRxiv Jan 30, 2026
Identifying clinically relevant synthetic lethal interactions has great potential for uncovering novel therapeutic vulnerabilities in cancer. Current approaches rely on machine learning models that estimate probabilities of synthetic lethal interacti...
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ProMeta: A meta-learning framework for robust disease diagnosis and prediction from plasma proteomics

bioRxiv Jan 30, 2026
The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled dat...
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AI-enhanced cardiac digital twins extend drug proarrhythmic risk assessment through experimental data uncertainty propagation and overdose exploration: a loperamide case study

bioRxiv Jan 30, 2026
Drug-induced QT interval prolongation is a key biomarker of proarrhythmic risk and central to drug cardiac safety evaluation alongside in vitro assays and animal studies. Current preclinical frameworks, however, provide limited insight into how exper...
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Deep learning-enabled speckle reduction for cleared-sample coherent scattering tomography

bioRxiv Jan 30, 2026
Clearing Assisted Scattering Tomography (CAST) extends coherent scattering tomography to whole-brain imaging, enabling visualization of fine-scale brain-wide connectivity. As a coherent optical tomography modality, CAST is inherently affected by spec...
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Do eyes say it all? Assessing the utility of physiological signals for predicting systemic cognitive states

bioRxiv Jan 29, 2026
Robust estimation of systemic human cognitive states is critical for many applications, from simply detecting inefficiencies in human task performance to adapting the behaviors of artificial agents to improve team performance in mixed-initiative huma...
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