bioRxiv

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Predicting functional constraints across evolutionary timescales with phylogeny-informed genomic language models

bioRxiv Jan 1, 2025
Genomic language models (gLMs) have emerged as a powerful approach for learning genome-wide functional constraints directly from DNA sequences. However, standard gLMs adapted from natural language processing often require extremely large model sizes ...
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Canonical cortical circuits: A unified sampling machine for static and dynamic inference

bioRxiv Jan 1, 2025
The brain lives in an ever-changing world and needs to infer the dynamic evolution of latent states from noisy sensory inputs. Exploring how canonical recurrent neural circuits in the brain realize dynamic inference is a fundamental question in neuro...
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PhyloAug: An Evolutionary and Structure-Aware Data Augmentation Tool *

bioRxiv Jan 1, 2025
Genomic Language Models (GLMs) suffer from the inherent problem of data scarcity, due to the cost, time and complexity of wet-lab experiments. Data augmentation offers a solution; however traditional methods may unintentionally affect the underlying ...
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Lysosomal multi-omics reveals altered sphingolipid catabolism as driver of lysosomal dysfunction in the aging brain

bioRxiv Jan 1, 2025
Recent data indicate that lipid composition has profound influence on the brain function and that changes in lipid homeostasis affect brain aging and predisposition to neurodegenerative diseases. Lipids dynamically reside in multiple intracellular lo...
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Neural network-based encoding in free-viewing fMRI with gaze-aware models

bioRxiv Jan 1, 2025
Representations learned by convolutional neural networks (CNNs) exhibit a remarkable resemblance to information processing patterns observed in the primate visual system on large neuroimaging datasets collected under diverse, naturalistic visual stim...
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Toward Unified Biomarkers for Focal Epilepsy

bioRxiv Jan 1, 2025
Accurately localizing the epileptogenic network (EpiNet) remains a major barrier to effective epilepsy treatment, largely due to limited mechanistic understanding. The EpiNet is a patient-specific brain network shaped by complex, overlapping patholog...
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CenSegNet: a generalist high-throughput deep learning framework for centrosome phenotyping at spatial and single-cell resolution in heterogeneous tissues

bioRxiv Jan 1, 2025
Centrosome amplification (CA) is a hallmark of epithelial cancers, yet its spatial complexity and phenotypic heterogeneity remain poorly resolved due to limitations in conventional image analysis. We present CenSegNet (Centrosome Segmentation Network...
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Molecular dynamics simulation of the truncated NMDA receptor in the open state

bioRxiv Jan 1, 2025
The GluN1/GluN2A N-methyl-D-aspartate receptor (NMDAR) is a critical ligand-gated ion channel in the central nervous system, playing essential roles in synaptic plasticity, learning, and memory. Understanding its dynamics in the open/active state is ...
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dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data

bioRxiv Jan 1, 2025
Multi-parameter cytometry technologies enable high-dimensional analysis of immune cell populations at single-cell resolution. Deep learning has emerged as a transformative tool for analyzing these datasets, but existing methods often struggle with tr...
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Identifying Optimal Machine Learning Approaches for Human Gut Microbiome (Shotgun Metagenomics) and Metabolomics Integration with Stable Feature Selection

bioRxiv Jan 1, 2025
Microbiome research has been limited by methodological inconsistencies. Taxonomy-based profiling presents challenges such as data sparsity, variable taxonomic resolution, and the reliance on DNA-based profiling, which provides limited functional insi...
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