contrastive learning

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

The paper evaluates contrastive learning on VOC-OR binding datasets, showing improved generalization and reduced performance inflation from identity leakage.

Language-guided domain adaptation improves protein sequence rep-resentations for antimicrobial resistance analysis

BacLLM improves protein sequence representations for antimicrobial resistance analysis by integrating functional knowledge and enhancing resistance gene identification and mechanism prediction.

Reference-Guided De Novo Retrieval of 3D Chromatin Organizational Features with Representation Learning

The paper introduces C-Fetch, a framework that retrieves 3D chromatin features using reference examples, showing improved enrichment for biologically relevant contacts compared to existing methods.

Error-Controlled Discovery of Protein Interactions via Conformal Calibration

PPI-Caliper controls false discovery rate in protein interaction prediction using conformal calibration without retraining models.

Perturbation-induced gene programs map the hidden regulatory architecture of complex diseases

This paper introduces Perturbscape, a framework that uses perturbation-induced gene programs to identify disease-relevant genes and pathways, showing higher heritability informativeness than steady-state approaches.

RELAY: Relational In-Context Learning for Cellular Perturbation Prediction

RELAY predicts cellular responses to perturbations by learning relational patterns from measured data, outperforming existing methods on unseen variable values and combinations.

FedEpitope: Parameter-Efficient Federated Protein Language Models for Linear B-Cell Epitope Prediction

FedEpitope uses federated learning with LoRA to predict linear B-cell epitopes without centralizing data, achieving good performance across multiple clients.

MaxCAT-Net: Hybrid Transformer-Based Skin Lesion Classification and Lightweight Segmentation with Calibration and Cross-Dataset Validation

MaxCAT-Net improves skin lesion classification and segmentation with hybrid architecture and calibration, showing performance on benchmark datasets but with preliminary results.

A single-cell multimodal foundation model enables cross modality analysis between transcriptome and chromatin

MUTUAL enables cross-modality analysis between transcriptome and chromatin by learning a shared RNA-ATAC space from multiome data without refitting.