bioRxiv

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Integration of Bioinformatics and Machine Learning to characterize Fusobacterium nucleatum’s pathogenicity

bioRxiv Jan 1, 2025
Fusobacterium nucleatum has been found to be associated with cancer lesions in both oral and colon cancers. Although important studies have dissected the clinical aspects of its remarkable pathogenicity, there is a lack of molecular studies. Our comp...
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Integrating computational protein structure predictions and genetic dependencies yields an atlas of human multi-protein complexes (AHMPC)

bioRxiv Jan 1, 2025
Knowledge of which proteins interact to form functional complexes in cells is essential for understanding molecular mechanisms in biology. Structure prediction methods recently allowed to compute the Human Interactome of likely binary protein interac...
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OmniShim - a vendor-independent B0 Shimming software toolbox

bioRxiv Jan 1, 2025
To introduce the OmniShim Toolbox, a software package designed to calibrate and perform B0 shimming in user-defined regions of interest across systems from different vendors, supporting various shim orders. In this study, we systematically compared t...
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Exploiting correlations across trials and behavioral sessions to improve neural decoding

bioRxiv Jan 1, 2025
Traditional neural decoders model the relationship between neural activity and behavior within individual trials of a single experimental session, neglecting correlations across trials and sessions. However, animals exhibit similar neural activities ...
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Interpretable Deep Learning Reveals Biologically Relevant Spatial Gene Expression Patterns in Lung Tumors and their Microenvironment

bioRxiv Jan 1, 2025
Lung adenocarcinoma (LUAD), the most common subtype of non–small cell lung cancer (NSCLC) exhibits profound histological and molecular heterogeneity, hindering accurate prognosis and effective treatment. Current approaches to assess this heterogeneit...
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Physics-Informed Neural Networks for Real-Time Deformation-Aware AR Surgical Tracking

bioRxiv Jan 1, 2025
Soft tissue deformation severely degrades registration accuracy in AR-assisted surgery. We propose a physics-informed neural network (PINN) that integrates biomechanical priors into real-time depth-based registration. The model embeds finite element ...
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Translating clinical gene sequencing into a foundational representation of tumor subtype

bioRxiv Jan 1, 2025
While gene sequencing is routine in cancer care, translating sequences into treatment decisions remains a challenge. Here we introduce MutationProjector, an AI foundation model that transforms tumor mutation profiles into a compact representation of ...
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DoRIAT: A Bayesian framework for interpreting and annotating docking runs

bioRxiv Jan 1, 2025
The advent of sequence-to-structure deeplearning models have transformed protein engineering landscape by providing an accurate and cost effective way to determine crystal structures. Despite their accuracy, deep-learning predictions tend to give lim...
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In Silico Design of APOE ɛ4 Interaction Inhibitor Peptides for Alzheimer’s Disease

bioRxiv Jan 1, 2025
Protein-protein interactions (PPIs) are essential for cellular functions, and their aberrant formation contributes to neurodegenerative diseases. Alzheimer’s disease (AD), characterized by its complex pathogenesis, poses an increasing societal burden...
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Knowledge-Driven Interpretable Neural Networks for Mechanistic Insight

bioRxiv Jan 1, 2025
Analyzing omics data based on pathway knowledge is critical for understanding the molecular mechanism behind pathological changes, but current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting ...
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