benchmarking

Evaluation design shapes estimates of cross-donor generalization in single-cell classification of cell-type

This paper evaluates how different evaluation designs affect cross-donor generalization in single-cell classification, finding that random cell-level splitting overestimates performance compared to donor-held-out evaluation.

Asking the Data: Conversational Interrogation of LC-MS/MS Proteomics Data Sets with an LLM Agent

The paper introduces a conversational framework using an LLM agent to interrogate proteomics data sets, identifying issues in four LC-MS/MS datasets and demonstrating its feasibility in reanalyzing data.

How well does electrical source imaging reconstruct neural time series? Evidence from simultaneous scalp and intracranial EEG recordings

This study evaluates how well electrical source imaging reconstructs neural time series using simultaneous scalp and intracranial EEG recordings, finding limited temporal reconstruction and spatial leakage.

Best Practices for Pre-Processing Large-Scale ATAC-Seq Data: Why Epidemiological Studies Demand Greater Stringency

The paper introduces PIPPA, a pipeline for processing large-scale ATAC-seq data, showing that peak calling performance varies by dataset and recommending parameters for high-confidence chromatin accessibility profiles.

Extending Musculoskeletal Modelling Validation Beyond Gait: Knee Loading and Kinematics During Daily Activities

This study validates musculoskeletal models across daily activities, finding good agreement in knee forces but discrepancies in kinematics and EMG during stair descent.

Scalable hardware and software for high-density, multi-regional electrophysiology in non-human primates

The paper introduces MIND and UnitAssay for scalable, high-density, multi-regional electrophysiology in non-human primates, enabling stable recordings and reliable single-unit identification.

Variant effect predictors are shaping the ClinVar classifications used to train and evaluate them

The paper shows that variant effect predictors influence ClinVar classifications, creating circularity in variant classification and prediction.

BaSiCPy: Scalable and Robust Shading Correction for Optical Microscopy Images

BaSiCPy improves illumination correction in microscopy images by addressing foreground correlations and enabling automated processing.

Mimic: A Unified Neural Network Architecture for Modelling Genomic Sequences and Module-level Comparison

This paper introduces Mimic, a unified neural network architecture for genomic sequence modeling, showing it outperforms existing models in protein-DNA interactions and enhancer activity.

Computational Design and Evaluation of De Novo Protein Minibinders Targeting Specific Sites of STAT3

The paper designs and evaluates de novo protein minibinders targeting specific STAT3 sites using AI and AlphaFold3, identifying high-confidence binders with novel sequences.

Robustness of the inference of the distribution of fitness effects to selective sweeps

This study evaluates how selective sweeps affect DFE inference using fitdadi, finding that performance declines at low recombination rates and with unfolded SFS, but remains largely unbiased with a flexible demographic model.

Scaling a context-aware genomic model for condition-specific edit design in plants

CROP-1 predicts gene activity based on DNA sequence and regulatory state, enabling context-specific edit design in plants with higher accuracy than sequence-only models.

Characterization of heterogeneous bioinformatics training needs in the AI era

The paper introduces a network-based clustering method to identify distinct learner profiles from training-needs surveys, revealing heterogeneous bioinformatics training needs in the AI era.

Cornealia: automated collective order analysis reveals density-dependent reorganization of donor corneal endothelium

Cornealia analyzes collective order in donor corneal endothelium, revealing density-dependent reorganization and mechanical responses not captured by traditional metrics.

Open-Source, High-Speed and High-Resolution Data Acquisition Platform for Biopotential Recordings and Neural EIT applications

This paper develops an open-source biopotential recording system with performance comparable to high-end devices, validated through in-vivo and benchtop experiments.

Whole-brain normative modeling across multiple functional connectivity metrics in MEG

This paper introduces M/EEG-FUNCOIN, a normative modeling approach for whole-brain functional connectivity in MEG data, showing improved characterization of age and sex-related patterns and detection of connectivity deviations linked to cognitive ability.

Historical and Contemporary Management of Genetic Diversity in French Local Horse Breeds: Insights from Genomic Data

This study uses genomic data to analyze the genetic diversity and management history of French local horse breeds, showing distinct patterns of kinship and inbreeding linked to different management practices.

Quantification method affects replicability of eQTL analysis, colocalization, and TWAS

The paper shows that RNA-seq quantification methods and reference annotations significantly impact eQTL analysis, colocalization, and TWAS results across multiple tissues and phenotypes.

An AI-based and coding-free integration for forest Leaf Area Index calculation

This paper introduces a coding-free AI method for improving leaf area index (LAI) calculation from hemispherical images, showing higher consistency and reliability compared to existing tools.

Joint reconstruction of multi-contrast relaxation-diffusion imaging

This paper introduces DIFFUSE, a framework for joint k-q-TE image reconstruction in diffusion MRI, showing improved error reduction and multi-contrast reconstruction.

Sequence design for three-dimensional genome folding using Akita Semifreddo

This paper introduces Akita Semifreddo, a framework for designing DNA sequences that produce specific 3D genome folding patterns, demonstrating the ability to create boundaries, dots, and flames with controlled strengths.

Mathematical AI Predicts 40% and 57% Influenza Vaccine Effectiveness Against H1N1 and H3N2, Respectively, for the 2026--2027 U.S. Season

The paper uses mathematical distances from HA sequences to forecast influenza vaccine effectiveness for the 2026--2027 season, predicting 40% and 57% effectiveness against H1N1 and H3N2, respectively.

A life-history axis of resource acquisition in bacteria: depolymerisation versus uptake

The paper develops a genomic ratio to quantify resource-acquisition strategies in bacteria, finding uptake-optimised bacteria dominate early decomposition and depolymerisation-optimised bacteria dominate later stages.

Auto-AVS: A Novel Framework for Automated Autophagic Vacuole Simulation

Auto-AVS simulates autophagic vacuoles in yeast to test stereological assumptions, finding no effect of body deformation on section statistics but discrepancies in body-number estimates.

Spatiotemporal proteomic profiling of genome editing-associated microenvironment

This study develops a Cas9-TurboID platform to map dynamic protein assemblies around genome editing sites and identifies factors that enhance editing efficiency in heterochromatic regions.

Resource-Rational Encoding of Reward Information in Working Memory During Planning

This study shows humans allocate working memory during planning based on decision relevance, not salience, using computational models and behavioral experiments.

Inferring fluctuating interaction probabilities in ecological networks across environmental change

This paper develops a method to infer species interactions and environmental changes in ecological networks using mechanistic models and statistical inference, validated on synthetic and empirical data.

Ecological and metabolic signatures of biofilm viromes across a national river surveillance network

This study characterizes river biofilm viromes across a national network, revealing seasonal structure, host associations, and metabolic functions linked to environmental variables.

Inferring brain structural complexity from dynamical measures

This study evaluates how neural complexity metrics relate to network architecture by simulating neural dynamics across diverse network structures and analyzing both observational and perturbational measures.

Protein Aggregation Capture for Top-down Proteomics

This paper introduces a magnetic bead-based sample preparation method for top-down proteomics to improve sensitivity and reduce sample loss during protein aggregation capture.