bioRxiv

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Representation, Alignment, and Generation: A Comprehensive Survey of Foundation Models for Non-Invasive Brain Decoding

bioRxiv Jan 1, 2025
The ability to decode human thoughts, intentions, and perceptions directly from non-invasive brain recordings holds transformative potential for healthcare, communication, and human-computer interaction. However, translating the safety and scalabilit...
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Probabilistic Multi-site MR Image Harmonization via Feature Preserving Conditional Generative Adversarial Networks

bioRxiv Jan 1, 2025
Brain magnetic resonance imaging (MRI) is pivotal in diagnosing and monitoring neurological disorders. However, despite their extensive applications, MR images have certain shortcomings. In particular, factors other than the anatomy of brain tissues ...
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Machine learning-based development of Gadolinium binding peptides

bioRxiv Jan 1, 2025
Gadolinium-based contrast agents (GBCAs) are indispensable tools in magnetic resonance imaging (MRI), yet their clinical use is limited by non-specific tissue accumulation, low molecular specificity, and safety concerns. Protein and peptide scaffolds...
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Dopamine drives a positive reward bias on human reinforcement learning

bioRxiv Jan 1, 2025
Formal theories of reinforcement learning (RL) prescribe a clearly defined function for dopamine, namely modulating learning via reward prediction errors (RPEs). Yet, empirical evidence in humans remains scarce, and recent advances introducing noisy ...
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Challenging Backpropagation: Evidence for Target Learning in the Neocortex

bioRxiv Jan 1, 2025
Computational neuroscience currently debates two competing hypotheses to explain hierarchical learning in the neocortex: deep learning inspired approximations of the backpropagation algorithm, where neurons adjust synapses to minimize an error, and t...
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Limitations of the refolding pipeline for de novo protein design

bioRxiv Jan 1, 2025
With the emergence of powerful deep learning-based tools, computational protein design has become a widely accessible technique. Nowadays, it is possible to perform both sequence and structure design in a matter of minutes, making the technology attr...
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Assessing Knowledge Distillation of a Multi-Emitter Localizing Neural Network for Applications in Stochastic Optical Reconstruction Microscopy

bioRxiv Jan 1, 2025
Super Resolution Microscopy (SRM) is a powerful method in quantitative bioscience that allows interrogation of nanoscale details. These methods require extensive imaging times on the microscope resulting in data sets on the scale of gigabytes. In ord...
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MFAID-Net: A Multi-modal Feature Fusion Deep Learning Network for Robust Adaptive Introgression Detection Across Diverse Evolutionary Scenarios

bioRxiv Jan 1, 2025
Adaptive introgression (AI), the beneficial genetic transfer between species, is key to adaptation, yet its genomic identification is challenging. Existing methods are inconsistent and often require unavailable donor population data. We propose MFAID...
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scE2TM improves single-cell embedding interpretability and reveals cellular perturbation signatures

bioRxiv Jan 1, 2025
Single-cell RNA sequencing technologies have revolutionized our understanding of cellular heterogeneity, yet computational methods often struggle to balance performance with biological interpretability. Embedded topic models have been widely used for...
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Development of a universal imaging “phenome” using Shape, Appearance and Motion (SAM) features and the SAM Observation Tool (SPOT)

bioRxiv Jan 1, 2025
Cells are plastic, highly heterogeneous and change over time. High-content timelapse imaging promises to reveal dynamic cell behaviors, enabling more accurate identification of cell state and cell fate prediction for biological hypothesis generation ...
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