AIMC Journal:
bioRxiv

Showing 1681 to 1690 of 4956 articles

Uncertainty-aware localization microscopy by variational diffusion

bioRxiv May 5, 2026
Fast extraction of physically relevant information from images using deep neural networks has led to significant advances in fluorescence microscopy and its application to the study of biological systems. For example, the application of deep networks...
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A Pilot Evaluation of Open-Weight Large Language Models for Screening RNA-seq Metadata in Public Databases

bioRxiv May 5, 2026
Although the Gene Expression Omnibus and other public repositories are expanding rapidly, curation across these databases has not kept pace. Data reuse is often hindered by unstandardized metadata comprising unstructured text. To address this, we dev...
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Autonomous error detection is enabled by conflict-dependent forward models in human medial frontal cortex

bioRxiv May 4, 2026
Learning from mistakes is fundamental for survival. Humans can monitor their behavior to detect action errors without external feedback, utilizing error information for adaptation and learning. How such errors are detected remains unknown. We investi...
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AI-guided discovery of atypical protein assemblies

bioRxiv May 4, 2026
Artificial intelligence (AI) systems such as AlphaFold have transformed structural biology by enabling accurate prediction of protein structures. However, their capacity to uncover new classes of macromolecular assemblies remains largely untapped. We...
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An interpretable deep learning framework for classifying neuronal morphologies using topology and graph neural networks

bioRxiv May 4, 2026
Neuronal shape determines how neurons process and integrate information, yet a consistent and objective classi cation of neuronal morphologies remains elusive. Current approaches rely heavily on subjective expert views or on prede ned features, limit...
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Automatic Bevacizumab Response Prediction in Ovarian Cancer from Digital Pathology Images via Novel AI-based Computational Pipeline

bioRxiv May 4, 2026
Ovarian cancer is one of the gynecological cancer types, which, if metastasized and not detected early, can cause deaths among women. Therefore, there is a need to accurately predict drug responses to ovarian cancer. A gynecological pathologist inspe...
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A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response Predictions

bioRxiv May 4, 2026
Mechanistically predicting the consequences of drug action requires distinguishing whether molecular interactions are activating or inhibitory, yet most biomedical knowledge graphs and graph neural networks represent biology as unsigned associations....
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LNGCN: A Distance-Aware Dynamics Network for Protein-Protein Interaction Prediction

bioRxiv May 4, 2026
High-throughput accurate protein-protein interaction (PPI) prediction is foundational to systems-level biological understanding, disease mechanism dissection, and structure-based drug discovery. Traditional graph convolutional networks (GCNs) are lim...
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Dual GLP-1/FGF21 agonism suppresses voluntary alcohol consumption, alcohol choice, and nucleus accumbens dopamine modulation

bioRxiv May 4, 2026
Excessive alcohol consumption remains a major public health challenge with limited therapeutic options. Both glucagon-like peptide-1 (GLP-1) and fibroblast growth factor-21 (FGF21) independently regulate alcohol intake through complementary metabolic...
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Joint Variable Selection for Omic Biomarkers in Time-to-Event Data

bioRxiv May 4, 2026
The incidence of the vast majority of neurodegenerative, cancer, and metabolic diseases generally increases exponentially with age. In large-scale biobanks, linking time-to-diagnosis information in electronic health records to multiple genomic (``mul...
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