deep learning

Neanderthal introgressed ancestry reveals human genomic regions enriched with recessive deleterious mutations

The paper uses Neanderthal ancestry patterns to identify genomic regions enriched with recessive deleterious mutations using a machine learning classifier.

AI-driven discovery and engineering of human endogenous nanocage proteins for mRNA delivery

The paper develops DeepDelivery, an AI platform that identifies human proteins for mRNA delivery, validating 80% of top candidates for efficient, non-inflammatory delivery.

Extending digital biology: bacterial survival and morphological heterogeneity under antibiotic stress

This study develops a workflow to generate morphological signatures of bacterial progeny under antibiotic stress, revealing distinct morphologies linked to survival and antibiotic type.

COMBO-RATE: An experimentally validated bioinformatic tool to identify promiscuous HLA restrictions

COMBO-RATE identifies promiscuous HLA restrictions by analyzing immune response data, outperforming existing tools and revealing broader HLA allele coverage for T cell epitopes.

Spatial Single-Cell Mapping of Transcriptional Differences Across Genetic Backgrounds in Mouse Brains

The paper introduces ATLAS, a method to map transcriptional differences across genetic backgrounds in mouse brains, revealing significant cell type distribution changes between sexes and genetic models.

IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder

IDPForge generates all-atom ensembles of intrinsically disordered proteins and regions using a transformer diffusion model, showing good agreement with experimental data.

Deep Mutational Scanning of the GPCR Rhodopsin Reveals Pleiotropic Mutational Effects and Their Roles in Modulating Light Sensitivity

The paper uses deep mutational scanning to study pleiotropic effects in rhodopsin, revealing how mutations impact multiple molecular phenotypes and shape light sensitivity.

Aβ42 facilitates the activation of Discoidin Domain Receptor 2 and Its nuclear enrichment in Alzheimer's Disease Model

This study shows Aβ42 activates DDR2 in Alzheimer's disease models by non-canonically bypassing collagen ligands and promotes its nuclear enrichment, with structural insights into the mechanism and potential therapeutic inhibition.

General prediction of T cell receptor antigen specificity from sequence using AlphaFold 3

This paper uses AlphaFold 3 to predict T cell receptor antigen specificity from sequence, achieving high accuracy on unseen epitopes.

Integrative taxonomy using traits and genomic data for Species Delimitation with Deep learning

This paper introduces a deep learning method that integrates genomic and trait data to improve species delimitation in complex speciation scenarios.

ESMRank reveals a transferable axis of protein mutational constraint from overlapping variant effect assays

ESMRank is a sequence-based predictor that integrates protein language models and physicochemical features to rank mutational constraint across proteins, outperforming existing methods and capturing disease-relevant variants.

Data-driven gait cycle decomposition based on whole-body coordination dynamics

This study decomposes gait cycles using a dynamic system framework to identify coordination patterns in human locomotion.

Preserving Information and Integrity in Cryo-EM: A Fourier-Space Deposition Approach

The paper identifies limitations in current cryo-EM data deposition practices and proposes an alternative approach to better preserve information and support validation.

XChrom: a cross-cell chromatin accessibility prediction model integrating genomic sequence and cellular context

XChrom predicts chromatin accessibility across cells and genomic regions using genomic sequence and cell identity, outperforming existing methods and enabling cross-species analysis.

Impact of Rare and Common Genetic Variation on Cell Type-Specific Gene Expression

Single-cell eQTL mapping in 5.4 million PBMCs from 1,925 individuals with matched WGS finds common-variant eQTLs for 83% and rare-variant signals for 47% of 21,404 genes.

DINOSim: Zero-Shot Object Detection and Semantic Segmentation on Microscopy Images

DINOSim detects and segments objects in microscopy images without annotated data, achieving performance comparable to supervised methods using a pretrained DINOv2 encoder and k-nearest neighbors refinement.

Antimicrobial peptide databases and prediction tools: Toward a standard evaluation framework

The paper evaluates antimicrobial peptide prediction tools and proposes a standardized evaluation framework to improve comparability and reproducibility.

Benchmarking and Experimental Validation of Machine Learning Strategies for Enzyme Engineering

This study benchmarks machine learning models for enzyme engineering using a curated dataset, finding zero-shot predictors are most generalizable but still modestly correlated with activity, with consensus models improving precision.

GlioVision: A Multi-Modal MRI Framework for Non-Invasive Glioma Molecular Biomarkers Prediction

GlioVision predicts four glioma molecular biomarkers from MRI data using a multi-modal framework, achieving high AUC scores across multiple cohorts.

Programmable acoustic single cell manipulation with model-free machine learning

This paper introduces VeLO, a model-free machine learning algorithm for programmable two-dimensional single-cell manipulation using acoustic standing waves, achieving robust control across multiple cell types.

Extracting massive ecological data on state and interactions of species using large language models

This study uses large language models to extract ecological interactions from 83,910 scientific papers, creating a network of 144,402 interactions between 36,471 taxa with high precision and sensitivity.

In silico prediction of metabolic trait robustness in microbial cells

This paper introduces TRAEV, a computational framework to predict the evolutionary robustness of metabolic traits in microbial cells, validated using yeast strains in controlled environments.

Using SpliceAI to triage splice-altering variants in 7,220 individuals with rare conditions highlights limitations of the precomputed scores

The paper evaluates precomputed SpliceAI scores for splice-altering variant prioritization in NDD cohorts, finding inaccuracies and providing a tool to improve variant triage.

Recovery of a minor cryo-EM particle population reveals conformational equilibria linking cofactor loading, turnover, and reactivation in methionine synthase

The paper reanalyzes a cryo-EM dataset using advanced methods to uncover a minor reactivation-state structure of methionine synthase, revealing conformational changes during cofactor turnover.

golgi: an open-source graphical platform for image-to-recruitment modeling of peripheral nerve stimulation

golgi provides an open-source platform for modeling peripheral nerve stimulation, enabling image-to-recruitment analysis with a graphical interface and reproducible results.

Increasing Cancer Stemness Drives Prostate Cancer Progression, Plasticity, Therapy Resistance and Poor Patient Survival

This study quantifies cancer stemness in prostate cancer across progression stages, showing its association with therapy resistance, poor survival, and lineage plasticity.

NK Cells Engineered with a Chimeric Antigen Receptor Delay HIV Rebound and Reshape HIV Reservoir Composition

This study shows that engineered NK cells with a CD4-based CAR delay HIV rebound and alter the reservoir's composition in humanized mice.

Accurate prediction of protein stability changes from single mutations using self-distillation and antisymmetric constraint strategies

DPStab predicts protein stability changes from single mutations using self-distillation and antisymmetric constraints, achieving state-of-the-art performance in ΔΔG and ΔTm prediction.

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.

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.