contrastive learning

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.

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.

Grounding olfactory perception in language: Benchmarks and models for generating natural language odor descriptions

ODIEU pairs human odor texts for 10,124 molecules with olfaction-tuned Sentence-BERT evaluators; CIRANO, a frozen GPT-2 with a molecular prefix, generates descriptions approaching human agreement.

Deep contrastive learning enables genome-wide virtual screening

DrugCLIP, a contrastive dense-retrieval model for pockets and molecules, beats docking on DUD-E and LIT-PCBA and screened about 10,000 human AlphaFold2 proteins in 24 hours.

Computational design of conformation-biasing mutations to alter protein functions

Contrastive scoring of protein variants across alternative backbone structures with inverse folding models (Conformational Biasing) predicts mutations that shift conformational equilibria and tune effector binding, catalysis, and labeling specificity.

Peptide Design through Binding Interface Mimicry

PepMimic, an all-atom latent diffusion model guided by a contrastive interface encoder, designs peptides mimicking known binders; 8% of tested peptides across five targets reached 10^-8M KD.

A contextualised protein language model reveals the functional syntax of bacterial evolution

Trained on over 1.3 million bacterial genomes represented as ordered protein sequences, the transformer foundation model Bacformer predicts protein interactions, operons, essentiality, and phenotypes while generating trait-conditioned synthetic proteomes.

conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics

conST learns embeddings from gene expression, spatial neighbors, and histology via three-level contrastive learning, reaching adjusted Rand index 0.65 on spatialLIBD slice 151673, with GNNExplainer-based explanations.