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Life Sciences
Life scientists post their preprints on bioRxiv, and quantitative biology appears on arXiv too. This page shows the newest papers ReadSearch has read from both, from cell biology and genetics to neuroscience, ecology and immunology.
709 papers, from 31 August 2026 to 6 November 2026: 426 arXiv preprints, 228 bioRxiv preprints and 55 OpenAlex records.
Newest papers, page 29
GenoTrace: Inheritable Watermarks for Genome Foundation Model Distillation
Can a genome model retain a detectable record of the synthetic sequences used to train it?GenomeOcean Anywhere: Private WebGPU Inference for Genome MoEs
Genome foundation models are most useful where sequences are generated, yet the largest models need datacenter accelerators and a place to send private DNA.CipherGenome: Homomorphic Inference for Genomic Mixture-of-Experts
Genome foundation models are growing into sparse mixture-of-experts (MoE) networks whose expert weights no longer fit on the machines that hold the sequences, yet sending a private genome to rented…Generalizable single-cell perturbation response prediction using energy-guided flow matching
Predicting phenotypic and transcriptional responses to perturbations at single-cell resolution provides a powerful tool for probing biological systems.Recovery-Directed Symbolic Distillation of Neural Likelihoods
Amortized neural likelihoods enable computationally expensive inference for models with analytically intractable or unspecified likelihoods, but their black-box nature limits interpretability.AmbiModBench: Benchmarking Gene Perturbation Prediction Beyond Shared Responses
Predicting cellular responses to genetic perturbations helps prioritize experiments in single-cell genomics, where exhaustive measurement is infeasible.Structured Population Models for Follicular Development
Ovarian follicle development has been modeled either by compartmental models, where developmental stages are represented by discrete compartments, or by physiologically structured population models…Beyond Gaussian Assumptions: Distribution-Aware Channel Capacity for Effective Connectivity
Effective-connectivity estimation from brain signals often relies on Gaussian residual modeling, which enables tractable estimation but can discard informative distributional structure and distort…BarcodeMAE+: Rethinking Masked Pretraining and Global Representations for DNA Barcode Foundation Models
Many DNA foundation models are pretrained by masking parts of a sequence and asking the model to reconstruct them.SAGE: A sampling-aware global evaluation benchmark for species distribution modeling
Knowing where species occur is fundamental for biodiversity research and conservation.FlatClip: A Geometry-Aware Surface-Level Baseline for fMRI Representation Learning
Recent fMRI foundation models differ substantially in the spatial scale at which they represent brain activity.MAESTRO: a Multimodal Auditory-attention Egocentric Speech-TRacking Open corpus
Humans rely on gaze, head movements, and visual cues to attend to speakers in noisy environments, yet auditory attention decoding (AAD) has been studied primarily using electroencephalography (EEG).Correcting the Dropout-LayerNorm Expectation Gap Improves Protein Structure Models
Although $\mathbb{E}[\mathrm{dropout}(x)] = x$, here we show that $\mathbb{E}[\mathrm{LayerNorm}(\mathrm{dropout}(x))]$ is not equal to $\mathrm{LayerNorm}(x)$.Reachability Does Not Imply Searchability in Expanding Networks
Routing and search respond in opposite ways to rapid network expansion.PFArena: Benchmarking Language Models for Protein Modification
Protein modification requires navigating an immense sequence space, yet wet-lab validation remains low-throughput and costly.A Spiking Neural Network Model of Elementary Self-Consciousness via Endogenous Default Mode Network Dynamics
Understanding the neurobiological mechanisms underlying self-referential cognition and baseline self-consciousness remains a fundamental challenge in computational neuroscience.Improving Molecular-Morphology Contrastive Pretraining using Deep-Learning-based Morphology Profiles
Recent advancements in image-based profiling techniques have enabled the collection of high-volume cell morphology data, allowing new molecular embedding models to learn from the experimental…Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics
Spatial transcriptomics (ST) measures gene expression in tissue context, but dense capture grids can be costly and may repeatedly sample morphologically similar regions.Physiologically Informed Digital Auscultation for Pneumonia Detection in Long-term Care Residents
Pneumonia is difficult to diagnose in older long-term care residents; multimorbidity and atypical presentations obscure signs, motivating operationally efficient objective testing.Discover, Falsify, Revise: Auditing Input-Use Claims from Source Code to Predictive Contribution in Agent-Discovered Cell Models
AI virtual cells aim to predict cellular responses to specified interventions, yet held-out predictive performance alone does not establish use of the supplied perturbation information.
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