Biophysics & Systems Biology · Life Sciences
Quantitative Methods
The newest papers in Quantitative Methods that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
140 papers, from 4 September 2026 to 5 November 2026: 140 arXiv preprints.
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Newest papers, page 4
Structure Before Intelligence: Gating What Agentic Scientific Workflows Release
An agentic scientific workflow can run to completion and still release wrong results: a silent numerical error, a stale input, or a defect in a shared upstream step can contaminate several outputs…ProtModernBERT: Compute-Efficient Protein Language Modeling
Large protein language models provide a powerful framework for learning sequence-function relationships and guiding experimental design.Maximum likelihood, neural networks and posterior mean for inferring fractional Brownian motion parameters
Fractional Brownian motion (fBM) is a model of anomalous diffusion parameterised by a Hurst exponent $H \in (0,1)$ and a generalised diffusion coefficient $D_H > 0$.Feasibility of a Patient-Specific Digital Twin for Ventriculoperitoneal Shunt Trajectory Planning
Catheter obstruction is the commonest cause of shunt failure; freehand insertion at Keen's point leaves the tip free in cerebrospinal fluid in only one case in ten.Predicting Unseen Scientific Interactions: Conditional Flow Models Hallucinate Non-Additive Effects
Conditional generative models are increasingly used for scientific prediction beyond the direct training support.MedEvo: Medical Reasoning Evolution through Knowledge Navigation
Medical multimodal reasoning models are typically trained on teacher-generated rationales, discarding the valid reasoning in students' own failed trajectories, while general-domain correction…ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution
Electrocardiogram (ECG) models need supervision that targets evolving weaknesses and remains faithful to waveform evidence.STAMP as a topology-aware molecular language for fragment-level learning and editing
Molecular fragments provide chemically meaningful units, but existing molecular languages rarely make their locations and connections explicit for editing.MFG: Multi-turn Feedback Guided Molecular Optimization
Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while maintaining a similar overall…PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization
The Practical Molecular Optimization (PMO) benchmark standardized evaluation in molecular optimization, but it is built on simple property-based oracles that do not capture structure-based drug…AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms
In-silico optimization of nucleic acids relies on predictive oracles that imperfectly capture biological function, so sequences optimized against one model may exploit model-specific artifacts and…From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos
Morphogenesis is driven by the interplay of genetic regulation and tissue mechanics.Double-Take: Improving Capacity, Binding, and Selectivity In Long-Context Within Bidirectional Memory
Processing a long sequence does not guarantee that distant information remains usable.Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels
Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets.Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification
Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics--image fusion models for renal cell carcinoma (RCC) are commonly evaluated…Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy
Preclinical models poorly predict human drug efficacy, particularly in neurological disorders.Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model
Existing reaction models primarily learn molecular transformations, whereas enzy- matic reactions depend jointly on molecular structure and catalytic function.FaceKit: a Toolkit for Interpretable Facial Phenotyping, Synthetic Image Generation and Privacy Analysis in Rare Diseases
Many rare genetic diseases are associated with recognizable craniofacial features.Many Brains, One Geometry: A Shared Visual-Semantic Space for Cross-Dataset fMRI Decoding
Visual decoding from fMRI is typically siloed by participant and experiment, obscuring whether heterogeneous neural measurements can be organized within a common computational geometry.CircuitATLAS: Agentic reasoning over a systems neuroscience knowledge graph for target discovery in circuitopathies
Drug discovery for neurological disease has traditionally centered on the molecules altered by disease.