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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 17
What to randomize matters: a controlled study of volume, axis and representation in low-data molecular generation
Chemical language models can struggle to generate new molecules when trained on small datasets, where a model may reproduce training examples rather than learn a useful generative distribution.Automatic Construction of Strong Simple Baselines
To rigorously establish the value of a novel deep learning method, it must be compared against appropriate baselines.Stationary Action Principles for Neuronal Growth
Neuronal growth is guided by biochemical, mechanical, and geometrical cues while being influenced by stochastic fluctuations in cytoskeletal dynamics, adhesion, and growth cone sensing.CogArena: Benchmarking Multimodal Agents on Interactive Behavioral Experiments
Claims about the cognitive capabilities of large language models are typically tested by translating behavioral paradigms into text prompts, bypassing the visual interfaces and time pressure through…ACOR: incremental evidence accumulation towards real-time DNA storage reconstruction
DNA offers high-density, durable storage by encoding digital information into synthetic strands and retrieving it through sequencing.Neuronal Identity as an Organizational Basis for Analyzing Neural Population Dynamics
Individual neurons exhibit diverse molecular, morphological, and electrophysiological properties that shape their distinct roles in neural circuits.Accurate structural modelling of protein-glycan recognition with GlycoWeave
Protein-glycan recognition underlies diverse biological processes, yet accurate prediction of protein-glycan complex structures remains challenging owing to the chemical diversity, conformational…HGPRGDel: GPR-Aware Learning and Ranking for Growth-Coupled Gene Deletion Design
Designing microbial strains for growth-coupled production, in which cellular growth is linked to the synthesis of a target compound through genetic interventions, is a fundamental goal in metabolic…Every route counts, but some count more: a max-plus view of the C. elegans brain
Brains do not simply add up every signal they receive: they select which routes dominate.A unified coarse-grained neural network potential for the dynamics of biomolecular complexes
The behavior of biomolecular complexes is governed by chemically specific interactions and often involves rare events that atomistic molecular dynamics struggle to capture within accessible…Evaluating and improving AI agents for sequencing protocol understanding
Existing scientific-agent benchmarks rarely evaluate whether agents can reconstruct the exact molecular structures and generation workflows specified by sequencing protocols, a capability critical…Solitary movement style predicts collective foraging while whom to follow emerges only in company
How far can behaviour inside a group, and the group's performance, be predicted from members measured while alone?A Differentiable, GPU-Accelerated Finite-Element Formulation in JAX for Large-Deformation Shells with Contact: Application to Patient-Specific Aortic Valve Simulation
Patient-specific aortic-valve models can help assess how tissue properties and geometry influence leaflet deformation and coaptation, with potential to inform intervention planning.Regional prediction of hematoma expansion from non-contrast computed tomography in spontaneous intracerebral hemorrhage patients
Background The combination of hematoma location, expansion and final volume influence neurological outcome.mdRNAcat: A Molecular Dynamics RNA Catalog of REST2 Conformational Ensembles
Learning molecular models of RNA requires simulation data that capture conformational variability and provide consistent physical training signals.A theory of noisy linear recurrent neural network learning dynamics
Biological neural networks are noisy: their dynamics involve difficult-to-predict fluctuations which are likely due not just to chaos, but also to a variety of low-level biophysical processes.On the stability of learning
Learning algorithms are usually analyzed under the assumption that informative gradients are continuously available.Structure, Not Sequence: Tracking and Validating Folding Mechanisms in ESMFold's Trunk with Sparse Autoencoders
Structure predictors like ESMFold fold proteins using neural network computation and have transformed structural biology.Enhancing Clinical Classifier Robustness with Deep Representation Learning Based OOD Detection on DNA Methylation Data
DNA methylation (DNAM) array-based tumor classification is a powerful clinical diagnostic tool, but models trained on fixed reference cohorts can produce faulty predictions for tumor types not…HELICS: Biobank-scale Conditional Synthetic Genome Generation via Latent Flow Matching
Individual-level genotype data underpin genetic discovery, disease-risk modeling, and therapeutic development, yet many critical settings remain data-limited, including underrepresented ancestries…
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