contrastive learning

UniMedSeg: Unified Multi-Modal Medical Image Segmentation via Uncertainty Guided Encoding and Frequency-Spatial Dual-Prompt Decoding

UniMedSeg improves multi-modal medical image segmentation by integrating uncertainty-guided encoding and dual-prompt decoding for better domain generalization and spatial risk estimation.

scProfiterole: Clustering of Single-Cell Proteomic DataUsing Graph Contrastive Learning via Spectral Filters

The paper introduces scProfiterole, a framework for clustering single-cell proteomic data using graph contrastive learning with spectral filters, showing improved cell type identification.

Frustration Quenching and Network Topology of the Energy Landscape as Primary Determinants of Protein-Ligand Binding Pose Prediction by Deep Learning Models

This paper shows that deep learning models' performance in predicting protein-ligand binding poses depends on the energy landscape's frustration quenching and network topology, not algorithmic flaws.

MINT: Predicting Enzyme-Cofactor Specificity by Multimodal Knowledge Transfer from Protein-Ligand Interactions

MINT predicts enzyme-cofactor specificity using a multimodal framework with transfer learning, achieving high accuracy in cofactor binding prediction.

Cross-cohort Mamography]{Representation-dependent domain adaptation for cross-cohort generalization of mammography diagnostic models

This study evaluates cross-cohort generalization of mammography diagnostic models using different image representations and domain-adversarial learning, finding that MedSigLIP embeddings provide the best transferability.

Discovering Latent Scientific Concepts through Discrete Representation Learning

This paper introduces Kodiak, a discrete representation learning method for scientific imaging that improves representation quality and enables interpretable, reusable concepts across domains.

trFlow: ultrafast all-atom protein conformational ensemble generation via conditional flow matching

trFlow generates all-atom protein conformations faster and more accurately than existing methods by using flow matching with three key innovations.

Deep learning of fossil pollen morphology reveals 25,000 years of ecological change in East African grasslands

This paper uses deep learning to analyze fossil grass pollen morphology and reconstruct 25,000 years of ecological change in East African grasslands, showing reduced diversity and shifts in C3:C4 ratios during the last ice age.

Auditing Protein-Protein Interaction Signals with Sparse Autoencoder Fingerprints

AuditPPI evaluates PPI predictors by transforming language model features into auditable fingerprints, revealing that predictive success does not necessarily reflect biological signal capture.

Branch-Wise Regularization for Heterogeneous GNN-Based MALDI-TOF AMR Prediction with Biomarker-Consensus Edges

This paper evaluates a heterogeneous GNN for AMR prediction from MALDI-TOF spectra, showing it performs better than MLP only at high spectral similarity.

FAMUS: A Few-Shot Learning Framework for Large-Scale Protein Annotation

FAMUS improves protein annotation by using contrastive learning to compare query sequences against multiple profiles, outperforming existing tools in KEGG and PANTHER annotations.

Foveation as self-supervision signal: Blur masks beat blank masks for learning robust visual representations

This paper introduces blur and retinal masks as self-supervision signals for vision transformers, showing improved robustness and generalization in visual representation learning.

Multi-Model Machine Learning Consensus Identifies a Dual-Interferon Gene Signature in Ischemic Stroke: An Integrative Single-Cell Transcriptomic Analysis

This paper uses a multi-model consensus framework to identify a dual-interferon gene signature in ischemic stroke using single-cell RNA sequencing data from a mouse model.

RNA-guided contrastive learning enhances patient-level prediction from histology

This paper improves histology-based molecular receptor prediction by integrating RNA-seq data during training, showing that gene selection strategy is key to model performance.

CycloCross: Adversarial single-cell RNA-seq data translation across species

Translating cells between mouse, macaque and human scRNA-seq with a CycleGAN, without ortholog mapping, preserved developmental time structure, imputed missing states and produced more realistic synthetic cells than scGAN.

Beyond Marginal Coverage: Class-Conditional Conformal Prediction in Multi-Class Psychiatric Neuroimaging

This paper introduces class-conditional conformal prediction to address uneven coverage in multi-class psychiatric neuroimaging, showing improved coverage consistency across diagnostic classes.

AC-Diff: Anatomy-Contrast Disentangled Diffusion for Multi-Vendor Liver MRI Harmonization

AC-Diff improves liver MRI harmonization by separating anatomy from contrast, achieving better segmentation results.

RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design

RNASeek is a generative foundation model for RNA sequence design and functional prediction, using reinforcement learning to optimize RNA properties like stability and cleavage activity.

Tri-Modality Representation Learning for Molecular Property Prediction

This paper introduces TMCA, a multimodal framework that integrates SMILES, molecular graphs, and 3D conformations for molecular property prediction, achieving the best performance on benchmark datasets.

A Taxonomy-Informed Sparse DNA Foundation Model for Microbial Genomics

A sparse mixture-of-experts DNA model conditioned on hyperbolic taxonomy embeddings learns better microbial representations than its taxonomy-free twin and comes close to Evo2-7B in probing and fine-tuning at lower compute.

CSGDA: A Cell State-Guided Graph Domain Adaptation Network for Single-Cell Drug Response Prediction

CSGDA predicts drug responses across biological heterogeneities by addressing distribution shifts using a cell state-guided graph domain adaptation framework.

Advancing Wildlife Image Analysis: A Graph Attention Contrastive Learning Approach for Region-Specific Mammal Classification

This paper develops a region-specific AI model for mammal classification using graph attention contrastive learning, achieving high accuracy in recognizing local species and improving data cleaning efficiency.

Spatially resolved contrastive analysis of perturbation-driven microenvironmental heterogeneity across tissues and conditions

Haruka identifies condition-specific and shared spatial domains across tissues and conditions using contrastive learning, demonstrating improved detection of spatially heterogeneous perturbation responses.

Self-Supervised Discovery of Discrete Local States in Noisy Image Sequences

This paper introduces a self-supervised framework to discover discrete local states in noisy image sequences without clean targets or labels, achieving high precision in particle tracking and separating biological structures from artifacts.

AMADEUS: Annotation-free multi-animal direction estimation using self-supervised learning

AMADEUS tracks multiple animals without annotations or markings, achieving better performance in identity tracking and keypoint localization across various species.

SHOT-CCR: Biologically guided adversarial training for test-time adaptation in cellular morphology

SHOT-CCR improves classification of genetic perturbations in Cell Painting datasets by reducing batch effects through biologically guided adversarial training.

Scalable Computational Phenomics of Nuclear Morphology

NucXplore converts H&E-stained nuclei into 129 explicit features, outperforming classical and deep learning methods in histopathology cohorts and revealing aging and exposure-related cellular changes.

Counterfactual Explanations for Graph Neural Networks in Patient Outcome Prediction

This paper introduces a counterfactual explanation algorithm for graph neural networks to explain patient outcome predictions in patient similarity networks.

OpenCRS: an open-source regulated human cardiorespiratory model with large-scale calibration and global sensitivity analysis at rest and during exercise

OpenCRS is a cardiorespiratory model calibrated with global sensitivity analysis to identify influential parameters and mechanisms during rest and exercise.

Exploring the potential role of the TETRATRICOPEPTIDE THIOREDOXIN-LIKE gene family in nitrogen-fixing and water-restricted soybean plants

This study characterizes the soybean TTL gene family, identifies co-expression modules related to abiotic stress, and links TTL proteins to nitrate reductase interactions in nitrogen-fixing plants under water deficit.