bioRxiv

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PRSformer: Disease Prediction from Million-Scale Individual Genotypes

bioRxiv Jan 1, 2025
Predicting disease risk from DNA presents an unprecedented emerging challenge as biobanks approach population scale sizes (N > 106 individuals) with ultra-high-dimensional features (L > 105 genotypes). Current methods, often linear and reliant on sum...
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Improved interpretability in LFADS models using a learned, context-dependent per-trial bias

bioRxiv Jan 1, 2025
The computation-through-dynamics perspective argues that biological neural circuits process information via the continuous evolution of their internal states. Inspired by this perspective, Latent Factor Activity using Dynamical systems (LFADS, [1]) i...
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Automating RNA-Ligand Interaction Modeling via a Self-Improving LLM Agent

bioRxiv Jan 1, 2025
Precise modeling of RNA-ligand interactions is essential for understanding RNA functionality and designing RNA-targeted therapeutics. Current computational approaches largely focus on predicting discrete binding sites, limiting their applicability to...
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Leveraging AI for facioscapulohumeral muscular dystrophy prediction and omics biomarker identification

bioRxiv Jan 1, 2025
Facioscapulohumeral muscular dystrophy (FSHD) is an autosomal dominant muscle disorder characterized by a complex genetic etiology, variable prognosis, and a lack of effective therapies. Previous studies have identified candidate protein and miRNA bi...
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scGPD: single-cell informed gene panel design for targeted spatial transcriptomics

bioRxiv Jan 1, 2025
In targeted spatial transcriptomics technologies, a key challenge is to select an informative gene panel that captures the complexity of cellular and spatial heterogeneity within tissues. Many existing methods use prior knowledge or heuristic selecti...
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Decoding emergent properties of microbial community functions through sub-community observations and interpretable machine learning

bioRxiv Jan 1, 2025
The functions of microbial communities, including substrate conversion and pathogen suppression, arise not as a simple sum of individual species’ capabilities but through complex interspecies interactions. Understanding how such functions arise from ...
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Protein-protein interaction priors shape biologically coherent latent spaces for causally concordant cross-omic translation

bioRxiv Jan 1, 2025
Deep learning models routinely compress omics into low-dimensional codes, yet many equally accurate embeddings fail to reflect how cells are wired, which limits explanation and causal reasoning. We present a simple, architecture-agnostic approach to ...
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Neural dynamics reveal foraging-like computations in the frontal cortex

bioRxiv Jan 1, 2025
The prefrontal cortex has a central role in flexible behavior. Computations for weighing alternatives, committing to an option, integrating evidence, and abandoning an option are dominantly formalized in reinforcement learning (RL). Yet, ecological c...
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Explicit memory representations in decisions from experience

bioRxiv Jan 1, 2025
Reinforcement learning (RL) models explain how people adapt behavior through incremental value updates but assume that individual experiences are not explicitly stored or retrieved. Across two experiments (N = 282 and N = 1,818), we tested whether pe...
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Unsupervised learning of multi-omics data enables disease risk prediction in the UK Biobank

bioRxiv Jan 1, 2025
The size and complexity of biomedical datasets continue to grow, driving the development of methods that reduce dimensionality while preserving biological signals. Yet, when deep learning is applied to such data, the impact of preprocessing choices a...
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