contrastive learning

Instance-Wise Contrastive Graph Neural Network Enables the Discovery of Novel Aedes aegypti Larvicidal Compounds

This paper introduces a contrastive graph neural network to discover novel larvicidal compounds against Aedes aegypti, identifying LC-79 as a potent and selective candidate.

Improved cryo-EM reconstruction of sub-50 kDa complexes using 2D template matching

This paper improves cryo-EM reconstruction of small complexes using 2D template matching and high-resolution structures as priors, enhancing density of ligand-binding sites in a 43 kDa protein kinase.

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.

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.

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.

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.

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.

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.

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.

Mechanistic Interpretability of Fine-Tuned Protein Language Models for Nanobody Thermostability Prediction

This paper fine-tunes a protein language model on nanobody thermostability data and uses sparse autoencoders to uncover biophysical rules governing stability, identifying both known and novel stabilizing features.

Overcoming artificial structures in resolution-enhanced Hi-C data by signal decomposition and multi-scale attention

This paper introduces SHARP, a method to enhance Hi-C data resolution while avoiding artificial structures by decomposing signals and using multi-scale attention.

CORSICA: Reproducible Suppression of Cochlear Implant Artifacts in EEG evoked by Continuous Speech

CORSICA suppresses cochlear implant artifacts in speech-evoked EEG data using cross-correlation with speech stimuli, outperforming alternative methods with minimal manual intervention.

Decoupling Detection and Classification to Improve Morphological Phenotype Analysis of Sickle Red Blood Cells in Full-Scope Microscopy

This paper improves sickle cell disease morphology analysis by combining a YOLO detector with a DenseNet classifier for accurate detection and classification of red blood cell morphologies in full-scope microscopy images.

Quartet-based species tree methods enable fast and consistent tree of blobs reconstruction under network multispecies coalescent

The paper introduces TOB-QMC, a method for scalable and accurate tree of blobs reconstruction under network multispecies coalescent, improving upon TINNiK in both speed and accuracy.

DeepPathway: Predicting Pathway Expression from Histopathology Images

DeepPathway predicts pathway expression from H&E images using contrastive learning, validated on prostate cancer and brain tumor datasets.

MAE-UNETR++: Masked Autoencoder Pretraining for 3-D Lung Nodule Segmentation

This paper proposes MAE-UNETR++ for 3-D lung nodule segmentation, showing that MAE pretraining outperforms random initialization and Decathlon transfer learning in low-data scenarios.

A Generalized Supervised Contrastive Learning Framework for Integrative Multi-omics Prediction Models

This paper introduces a supervised contrastive learning framework for multi-omics prediction models, showing improved accuracy over single-omics approaches in type 2 diabetes and high-fat diet studies.

Time-frequency embedding with contrastive pre-training allows sub-second seizure detection

This paper introduces a 3D CNN with a trainable CWT layer and BiCL pre-training for sub-second seizure detection, achieving >95% accuracy on EEG data.

ContrastQA: A Label-guided Graph Contrastive Learning-based approach for protein complex structure quality assessment

ContrastQA improves protein complex structure quality assessment using label-guided graph contrastive learning, achieving lower ranking losses than existing methods on CASP16 datasets.

Pro4S: prediction of protein solubility by fusing sequence, structure, and surface

Pro4S predicts protein solubility by integrating sequence, structure, and surface data, improving accuracy and reducing non-expressed proteins in de novo design.

Unlocking biological insight from single-cell data with an interpretable dual-stream foundation model

This paper introduces scDMC, a dual-stream foundation model for single-cell data that improves representation learning and achieves state-of-the-art results in cell annotation, clustering, and integration.

Contrastive learning of adverse events to provide effective and interpretable vector representations for machine-assisted pharmacovigilance

This paper develops interpretable adverse event vector representations using contrastive learning for pharmacovigilance, outperforming existing methods in drug-event causality prediction.

Contrastive learning unites sequence and structure in a global representation of protein space

This paper introduces CLSS, a contrastive learning model that co-embeds protein sequences and structures, revealing evolutionary relationships and structural transitions.

When Multimodal Fusion Fails: Contrastive Alignment as a Necessary Stabilizer for TCR--Peptide Binding Prediction

The paper shows that naive fusion of sequence and graph modalities in TCR-peptide binding prediction can degrade performance and introduces TRACE, a contrastive alignment method that stabilizes multimodal learning.

Long-Horizon associative learning as a unifying framework for statistical learning across scales

This paper introduces a unifying framework for statistical learning across temporal scales, showing a single mechanism can capture both local and higher-order patterns in data.

Discovery of a phenazine thiol conjugase from sparse data using genome-informed machine learning

The paper uses genome-informed machine learning to discover a new enzyme, PTC, that catalyzes phenazine thioconjugation from sparse data.

HiC2Self: self-supervised denoising for bulk and single-cell Hi-C contact maps

HiC2Self denoises Hi-C contact maps using self-supervised learning, reconstructing 3D chromatin structures from low-coverage data across bulk and single-cell samples.

Global StationaryOT: Trajectory inference for aging time courses of single-cell snapshots

This paper introduces a method to infer cell trajectories from aging time courses, improving accuracy in noisy data by leveraging temporal information.

Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics

This paper introduces st-Xprop, a graph learning method for spatial domain detection in multi-modal spatial transcriptomics, improving clustering accuracy and biological relevance.