benchmarking

Mapping long-distance jump dispersal risk by leveraging transport infrastructure data and sampling bias correction: a case study with the invasive spotted lanternfly (Lycorma delicatula)

The paper identifies high-risk areas for invasive species spread by analyzing transport infrastructure and correcting sampling bias using historical data on Lycorma delicatula.

Carrier-Limited Release Risk in Electrospun Nanofibers for Regenerative Endodontics: Constraint Discovery from 25 Formulations

The paper evaluates 25 electrospun PVP fiber formulations for regenerative endodontics, using machine learning to identify constraints and release risks, finding carrier-limited release and recommending further testing.

Validation of a high throughput fluorescent Capillary Electrophoresis Sodium Dodecyl Sulfate method for monoclonal antibody size heterogeneity assessment

This study validates a fluorescence-based CE-SDS method for assessing monoclonal antibody size heterogeneity, showing high specificity and precision compared to UV-based methods.

Negative sampling shapes benchmark discrimination and experimental retrieval in synthetic-lethality prediction

The paper evaluates how negative sampling affects synthetic-lethality prediction models, showing that benchmark performance does not reliably predict experimental retrieval success.

Kontext: Kernel-based method bridging spatial domain identification and cell-cell communication

Kontext identifies spatial domains and cell-cell communication by integrating ligand-receptor interactions, outperforming existing methods in spatial transcriptomics.

Transformer Language Models Reveal Distinct Patterns in Aphasia Subtypes and Recovery Trajectories

This paper uses GPT-2 to analyze aphasia subtypes and recovery by examining activation patterns in narrative speech over time, finding distinct differences across subtypes and changes during recovery.

Aneuploidy profiling among different types of brain metastases reveals a colon cancer-specific chromosomal signature

This study identifies a unique aneuploidy profile in colorectal cancer brain metastases compared to other cancer types and metastatic sites.

Spectroscopy-Based Machine Learning to Predict Stem-End Rot in Avocados with the Temporal Effects

This paper predicts stem-end rot in avocados using hyperspectral data and machine learning models, finding that physics-informed neural networks outperform traditional approaches by incorporating temporal dynamics.

mock-fastq-generator: A synthetic FASTQ generator

mock-fastq-generator creates synthetic FASTQ files for testing bioinformatics pipelines without using biological data.

A single-plane electrical impedance tomography framework for integrated liver and kidney assessment

This paper evaluates a single-plane electrical impedance tomography framework for assessing liver and kidney health, finding it feasible and effective for multi-organ evaluation.

Identification of environmental factors and growth stages in the prediction of fibre yield and fibre quality traits in rain-grown cotton

The paper identifies environmental factors and growth stages affecting fibre yield and quality in rain-grown cotton using statistical models and field data.

Machine Learning-Based Early Prediction of Neural Microelectrode Lifetime Under Accelerated Aging Conditions

This paper develops a machine learning model to predict neural microelectrode lifetimes from early-life data under accelerated aging conditions, achieving significant error reduction through feature engineering and calibration.

`Q-niche': topology-preserving niche hypervolumes built using a quantum mechanics modelling analogue.

Q-niche estimates ecological hypervolumes using a quantum mechanics-inspired algorithm, preserving topological features better than existing methods in synthetic and empirical data.

Assessing whole-cortex excitability from electromagnetic brain signals

This study evaluates ten excitability metrics from MEG data to show cortical excitability is multidimensional with distinct spatial and functional patterns.

Does Short Molecular Dynamics Rescoring Improve the Ranking of p38α Inhibitors over Docking? A Replicate Benchmark

This study compares docking and short MD rescoring for ranking p38α inhibitors and finds no significant improvement from MD rescoring over docking.

DeepPhage: leveraging the power of NGS phage display data, an in-depth analysis for the identification of Eu3+-binding peptides

DeepPhage identifies Eu3+-binding peptides from NGS phage display data by analyzing sequence biases and comparing multiple experimental conditions.

Harness engineering enables a multi-agent LLM system to generate cross-domain hypotheses beyond single-investigator reach

The paper demonstrates that a well-designed prompting system outperforms model upgrades in generating scientific hypotheses, introducing a framework to create falsifiable, cross-domain hypotheses.

Comparative analysis of protease-inhibitor gene family counts under different counting units and genome annotations

The paper compares protease-inhibitor gene family counts across species, showing how annotation methods and counting units affect results.

Comparative Evaluation of Commercial Gradient Diffusion and Disk Diffusion Tests for Aztreonam-Avibactam Susceptibility Testing

This study compares four commercial AZA susceptibility testing methods for carbapenem-resistant Enterobacterales, finding acceptable performance with minor errors mainly in breakpoint-adjacent isolates.

Advancing Spatial Pattern Integration in Species Distribution Modelling With Computer Vision: How Grain Size and Extent Concepts Shape Predictions of Emerging Aquatic Insects

This paper evaluates how grain size and window extent affect Deep SDMs for predicting aquatic insect distributions, finding significant performance variations with different resolutions and extents.

Comparing pest survey methods through detection probability: seasonal and abundance-dependent detection of spotted lanternfly (Lycorma delicatula, Hemiptera: Fulgoridae)

The paper evaluates three pest survey methods for spotted lanternflies, finding seasonal and abundance-dependent detection probabilities, with traps performing better on larger trees and preferred hostplants.

Assessing the Operational Feasibility of Evolutionary Therapy in Metastatic Non-Small Cell Lung Cancer

This study evaluates the clinical feasibility of evolutionary therapy for NSCLC by simulating its performance under real-world constraints like measurement error and appointment delays.

ARG-PASS v2.0 expands global resistome surveillance to reveal previously unrecognized antibiotic resistance genes reaching the human gut

ARG-PASS v2.0 identifies previously unrecognized antibiotic resistance genes in environmental samples and links them to human gut microbiomes through dietary exposure.

Degeneracy in neuronal bifurcation landscapes: equivalent bifurcation sequences across distinct parameter sets

The paper shows that different biophysical parameters can produce equivalent bifurcation sequences in neuronal models, revealing degeneracy in neuronal dynamics.

A human induced pluripotent stem cell-based high-throughput screening platform for teratogenicity hazard identification

Four-model hiPSC screen in 384-well imaging reached 83% accuracy (88% sensitivity, 75% specificity) on 130 scored compounds from a 146-compound reference set, scalable to hundreds of compounds monthly.

Multimodal Connectome Markers of Disruptive Behavior Problems in Youth: Distinct Patterns of Concurrent Severity and Prospective Symptom Change

The paper uses multimodal connectome data to identify patterns of functional and structural connectivity associated with disruptive behavior severity and change in youth.

Salience network expansion as a transdiagnostic neural marker for psychopathology risk in adolescence

The paper examines whether increased salience network surface area in adolescents is specific to depression or a general marker of psychopathology, finding stronger associations with overall psychopathology than depression alone.

When Does Active Learning Improve ADMET Prediction? A Multi-Endpoint Benchmark

This paper benchmarks active learning strategies for ADMET prediction, finding benefits vary by endpoint and strategy, with Deep Ensembles showing better predictive performance than MC Dropout.

GLESKit: an MCP-based system for gene-level biomedical investigation and evidence synthesis

GLESKit is a gene-centered system that synthesizes biomedical evidence by integrating multiple data types, achieving high report quality and performance in benchmark tasks.

Does a map-level insulation constraint improve sequence-based prediction of 3D genome folding? A Stage 1 Registered Report with a reverse-complement-equivariant state-space model (HiFold-Mamba)

The paper evaluates whether insulation-based losses improve sequence-based prediction of 3D genome folding using a reverse-complement-equivariant state-space model, finding improvements in boundary and loop prediction.