transformer / foundation model

The paper evaluates text-guided segmentation using SAM3 for weakly supervised leaf symptom segmentation, finding text guidance matches or outperforms conventional methods but is prompt-sensitive, with hybrid approaches improving robustness.

The paper screens variants of a Form II Rubisco to improve oxygen tolerance, identifies key mutations, and uses a transformer model to guide engineering.

This paper develops a machine-vision workflow to convert video footage of salmonids into individual encounter histories for capture-recapture analysis, achieving high accuracy in fish detection and re-identification.

CatESO optimizes enzyme sequences to improve catalytic turnover by integrating kcat prediction with differentiable design, achieving higher predicted activity and structural confidence than existing methods.

The paper identifies a novel phage resistance mechanism in Pseudomonas putida through experimental evolution, revealing a secreted protein Psh that confers broad-spectrum phage resistance without fitness costs.

A set-aware latent transport model predicts treated cell populations from unpaired controls, keeps perturbation-specific signal in CRISPR, drug and cytokine data, and guided a cytokine choice tested in three donors.

scMaize provides a maize-specific foundation model and integrated single-cell atlas, showing improved classification and cross-species generalizability with functional priors.

Across 13 single-cell foundation models and 94 datasets, PCA and established methods usually match or beat them; pretrained embeddings help mainly with scarce labels, rare cells and open-set detection.

This paper introduces a learned single-cell spatiotemporal manifold to analyze tissue morphology and dynamics over time.

SNAIL is a named entity recognition framework that identifies bioinformatics software and databases in scientific texts, outperforming existing methods and enabling meta-analysis of tool usage.

This study identifies KDM5D on the Y chromosome as a suppressor of CD8+ T cell antitumor immunity in males, linking it to TCR and cholesterol-exhaustion programs.

The paper structures and engineers deep-sea halophilic PET hydrolases to improve their salt tolerance and catalytic efficiency for industrial applications.

The paper develops DNABERT-Enhancer, a genomic language model for enhancer prediction, achieving high accuracy and identifying allele-specific activity and candidate SNPs.

Bromo, a pairwise transformer, ranks a protein's peptide precursors by expected mass spectrometry response, beating XGBoost, PREGO and PeptideRanger on held-out yeast data, and can be fine-tuned per instrument.

State, a set-based transformer trained on over 100 million perturbed cells, predicts perturbation responses in new cellular contexts better than mean, linear and deep learning baselines.

MetAgeFormer is a transformer-based model pre-trained on metabolomic data to estimate biological age and identify metabolic subtypes associated with disease risk.

Web resource offering predicted monomeric and oligomeric structures for 1,603 M. leprae proteins, with pockets, epitope scores, essentiality and a target priority ranking, plus a nanopore resistance-calling module.

Hi-Cformer models multi-scale chromatin contact maps from single-cell Hi-C data, improving cell type separation and imputation of 3D genome features.

scProtoTransformer provides a scalable reference mapping framework across molecular, cellular, and donor levels using a prototype-based Transformer model with improved interpretability and performance.

OmicsFM is a proteomics foundation model that matches transcriptomic models in performance, revealing complementary biology between proteomics and transcriptomics.

InsilicoCell predicts cellular functional profiles and enables multi-objective virtual drug screening by integrating diverse molecular data and tasks.

WaterFlow predicts ordered water molecule positions in protein structures with sub-angstrom accuracy, outperforming existing methods and improving drug design and crystallographic refinement.

TaHL-PTM improves PTM prediction by addressing tokenization issues in decoder-only PLMs, achieving better performance on residue-level tasks.

AntiSite predicts antibody and nanobody paratopes using shared models with or without structural data, achieving high performance across benchmarks and generalizing to nanobodies.

This paper introduces CVIT-ESP and ESPFormer, pre-trained Vision Transformer models adapted for EEG-based seizure prediction, achieving higher AUC-ROC than existing models.

CodonMamba is a foundation model for mRNA coding sequence design that achieves state-of-the-art performance and enables programmable codon optimization through inference-time steering.

This paper introduces DualMyo, a Transformer-based model for EMG-to-digit classification, achieving 91% accuracy with lightweight fine-tuning for cross-session adaptation.

The paper introduces a multi-modal framework for biomarker discovery in TNBC using public data, achieving high predictive performance and generating testable hypotheses.

The paper predicts plant morphological complexity using transcription factor data with TabPFN, finding TF family expansion drives complexity evolution.

The paper introduces GB.GeneUnet, a transformer-based model pretrained on multi-species genomes to predict gene expression at megabase-scale contexts, achieving state-of-the-art performance.