A control-validated pan-proteome deep-learning pipeline nominates GPR35 as a candidate target of the orphan bacterial metabolite ligiamycin A [new]

Guiding generative models to uncover diverse and novel crystals via reinforcement learning [new] ...to find thermodynamically stable, diverse crystalline materials with desired properties.

How to design a cancer vaccine (and vastly improve them): Alex Rubinsteyn & Ben Vincent [blog] by precisely selecting tumor-spec. neoantigens & CTAs via adv. comp. & immunopeptidomics for effective, personalized immunotherapy.

Benchmarking large language models for ACMG/AMP variant interpretation and variant calling [new] using a novel framework assessing safety, prov., & reproducibility, attributing failures to spec. arch. layers w/ temp. blinded data.

Tissue tearing degrades optimal-transport and diffeomorphic registration of spatial transcriptomics beyond displacement magnitude: a multi-seed deformation benchmark and a supervised graph cross-atten... [new]

Real-time mass defect-driven prediction of glycopeptide precursors enables enrichment-free serum glycoproteomics [new] ... by using machine learning for direct N-glycopeptide classification in DDA from serum.

Deciphering the Tumor Microenvironment: An Integrated Single-Cell RNA-Seq and AI Framework for Novel Biomarker and Therapeutic Target Discovery in Melanoma [new]

ADMET Property Prediction with Quantum-Inspired Preprocessing [new] Encodes molecular descriptor dependencies into a many-body H, using simulated time evolution to generate compact, interpretable features for ADMET property modeling.

Task-adapted biological foundation models uncover perturbation-centric representations [new] Supervised perturbation transforms bio lat. space, aligning same-pert. responses, capturing links.

Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning [new] by strategically balancing compound novelty and predicted bioactivity for OOD extrap., efficiently discovering hits in whole-cell screens.

TRIOPS: A deep learning framework for prediction of T cell receptor-MHC binding specificity [new] Predicts TCR-MHC restrict. from seq, extends to unseen alleles via cross-react. sampling, identifies allele-spec. HLA loss in tumors.

PRISM : Peptide-specificity annotation of T-cell receptors with uncertainty quantification [new] maps TCRs to a peptide-organized latent space via metric-learning, augmenting training with synthetic data to enable OOD abstention.

VirProtRAG: Literature-grounded viral protein function annotation with retrieval-augmented generation [new] Uses hybrid retrieval & evidence reranking for verifiable annotations, expanding and refining viral protein characterization.

Accurate, comprehensive gene annotation and ortholog identification across thousands of vertebrate genomes with TOGA2 [new] Uses exon orthology, DL, & gene tree recon creates comprehensive comparative G res. for thousands of genomes.

A High-Confidence Atlas of Protein Methylation Enables AI-Driven Detection of Methylated Peptides [new] ...by creating a stringent human methylation atlas from reanalyzed public data to train a DL model for improved peptide detection

Unbalanced Perturbation Dynamics For Cell Fate Design [new] models transcriptomic and cell-number dynamics from single-cell snapshots for predicting responses and designing interventions to achieve desired cell fates.

Generative cell phenotyping with structured latent populations [new] ...is achieved via a semi-supervised VAE with a Gaussian mixture prior, unifying cell classification, discovery, density estimation, and anomaly detection.

Context-dependent calibration of Evo2 likelihood with bacterial fitness: a quantitative characterization across five E. coli datasets [new]

Strict OOD Antigen-to-Antibody Retrieval with CDR-Aware Slot Late Interaction [new] ...focuses on robustly retrieving antibodies for unseen antigens by constraining antibody latent slots to CDRs and scoring local slot compatibility.

SAPPTree: Identification of an S-Acylation Motif Drives a Novel S-Acylation Prediction Program [new] Uses novel S-acylation motif (cys cluster, hydro stretch, polybasic helix) & struct. features in a RF model to predict S-acylation.

MycoCirc: A Pan-Fungal Multi-Modal Pretrained Model for Fungal circRNA Prediction from Genome Sequence [new] integrates 3 modalities via cross-attn to predict fungal circRNAs from genomes, offers a comprehensive prediction atlas.

Folding scFv--Antigen Complexes at Scale [new] by generating large cofolded structure ensembles to characterize model reliability and the impact of diverse inference-time settings.

Pathogen context reshapes antimicrobial peptide generation [new] by using genome features and strain descriptions to condition peptide generators for target-specific libraries.

CodonBERT and ESM-2 Embedding Spaces Share an Evolutionarily Conserved Paired Geometry Encoding Synonymous Codon Information [new] ...arises from indep trained nuc/prot models converging on shared, lin retrievable & evol constr. reps

Distillation enables scalable high-fidelity virtual screening across ultra-large chemical libraries [new] Transferring structure-based model's predictive power to efficient seq-based surrogate, trained on small frac. of large lib.

Benchmarking the translational potential of AI-based drug-resistance prediction from Mycobacterium tuberculosis whole-genome sequencing data [new]

Structural Organization of the Nvj3-Mdm1 Complex Reveals a Conserved Lipid-Compatible Contact Site Module [new] Module's heterodimer forms lipid-comp. conduit at the interface, suggests direct interorganelle lipid xfer at the NVJ.

Unveiling Cerebrospinal Fluid Protein Biomarkers in Pediatric Acute Lymphoblastic Leukemia Using Proximity Extension Assay [new] reveals distinct CSF protein sigs for ALL disease effect, CNS inv, and monitoring treatment response.

A Comprehensive Evaluation of Protein Structure Prediction Models for Short Peptides [new] Studies DL models' handling of short peptides, observing variation by length & secondary structure. Consensus helps define struct. hypotheses.

GenPerturb: sequence-grounded interpretation of perturbation transcriptomes using pretrained genomic models [new] Links Perturb-seq gene responses to cis-reg elements & TF motifs by sequence analysis, without matched chromatin data.

Raw-count embeddings improve single-cell foundation models [new] by finding simple log-transformed raw counts suffice, rendering complex normalisation, gene ordering, and positional encoding unnecessary.

A generalizable interface-seeded framework for de novo design of functional oligomers [new] ... designs modular responsive homo-oligomers for conditional/reversible assembly, building ligand-dependent biosensors & gene switches.

Using spIsoNet to address the preferred-orientation problem in cryoEM reconstructions [new] ...through deep learning for anisotropic map correction and enhanced particle pose estimation in cryoEM data.

AART enables fast and accurate cross-platform proteomic translation [new] ...via integrated regression and residual learning, addressing technical differences to enable reproducible and integrated proteomic research across platforms.

Evo 2's Perception of Single Nucleotide Substitutions in the Genes of Two Plant Model Organisms [new] ...perceives simulated nucleotide substitutions less likely than observed ones in plant genes, reflecting evolutionary constraints.

Inferring viral proteins that act as public goods during coinfection [new] ...quantitative framework analyzes MS2 evolution data under varying MOI to infer protein-specific public goods; lysis protein is the strongest candidate.

Principled approaches for extending neural architectures to function spaces for operator learning [new] Adapting NNs into discretization-agnostic NOs to learn from cont. sci data, enabling robust sci simulations across resolutions.

Multimodal computational framework identifies B cell convergence in autoimmunity and ageing [new] integrating receptor seqs with transcriptomics to precisely map pathogenic B cell origins & paths in autoimmunity (SLE) & aging.

Multi-modality Graph Representation Learning for Malignant Cell Identification from scRNA-seq using DeepMalignant [new] ...unsupervised integrates gene exp and CNA w/ graph attention for accurate malignant cell ID and TME analysis.

Programmable acoustic single cell manipulation with model-free machine learning [new] ... utilizes an RL-inspired algorithm, adapting to system dyn. online for precise 2D manipulation of individual cells without prior modeling.

HiFi-ST: High-Fidelity Reconstruction of Continuous Spatial Transcriptomic Expression Fields via Conditional Neural Fields [new] Reconstr. cont. spat. gene exp. fields model. spots as reg. obs, via MC sampling & multiscale tis. feat.

WattmaMod enables high-resolution and extensible RNA modification profiling for nanopore direct RNA sequencing [new] ...via DL framework w/ self-sup, adaptive training & multi-scale sig enc. for multi-mod char., extensible new types.

Zero-Shot Metabolite Prediction from Gene Expression via Physics-Informed Graph Neural Networks [new] integrates enzyme expr, EC embeds, and metabolite descr in a unified metabolic graph for zero-shot pred without specific params.

StructureSAFE: A structure-aware chemical language model for unified hit identification and lead optimization [new] ...integrates prot struct evol. data via pretraining, resolves design trade-offs, unifies hit ID & lead opt.

Performance Attribution in pLM-Based Biological Relation Prediction [new] ...uses controls to dissect information sources supporting pLM performance, showing specific controls are vital for accurate attribution.

Models trained with noisy genomes extend bacterial phenotype prediction into deep time [new] ...by applying ML to noisy reconstructed ancestral gene content, allowing inference of ancient bacterial phenotypes, even back to the LBCA.

Noninvasive H3 K27M screening in pediatric diffuse midline glioma using radiomics on heterogeneous T2-weighted MRI [new] ... leverages features from routine T2-weighted MRI as a moderate screening/triage aid for this mutation, not replacing tissue biopsy.

Resolving superposition in AI for interpretability and cross-modal alignment in patient-neuronal images [new] ... by geometrically purifying latent representations to align image features with scRNA-seq data, reconstructing neuronal pathology pathways.

A Novel Machine Learning Approach for Central Nervous System Tumor Classification from DNA Methylation [new] This method rigorously classifies CNS tumors from DNA methylation by integrating dimensionality reduction and multinomial logistic regression.

Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases [new] Automates NNP training for enzyme QM cls., inc. electrostatics, charge xfer & reactive data for insights.