# Supervised Contrastive Learning

RawMod, released by a University of Maryland-led team, stacks the raw nanopore signal of many reads at each site and compares it base by base with the expected signal of the reference, so a single 0.23M-parameter model can say whether a site carries a modification without naming its type. Across 15 datasets spanning 5mC, 5hmC, 4mC, 6mA and 5hmU in bacterial, phage, plant, mouse, human and synthetic DNA, it reaches a mean AUROC of 0.964, against 0.799 for the best released model, Dorado 4mC+5mC. It needs no unmodified control from the same sample, but a modification type absent from training transfers zero-shot only when another modification of the same base was seen.

RawMod, released by a University of Maryland-led team, stacks the raw nanopore signal of many reads at each site and compares it base by base with the expected signal of the reference, so a single 0.23M-parameter model can say whether a site carries a modification without naming its type. Across 15 datasets spanning 5mC, 5hmC, 4mC, 6mA and 5hmU in bacterial, phage, plant, mouse, human and synthetic DNA, it reaches a mean AUROC of 0.964, against 0.799 for the best released model, Dorado 4mC+5mC. It needs no unmodified control from the same sample, but a modification type absent from training transfers zero-shot only when another modification of the same base was seen.

CloneTrast uses clonal identities from paired single-cell RNA and TCR sequencing as training labels, so the model learns T-cell clonal-functional states from gene expression alone and needs no TCR data at inference. Training used 26 datasets across 14 cancer types with more than 1.5 million T cells, and external testing used 11 datasets with more than 1 million T cells, including two cancer types unseen in training. On both training and test sets, patient clonal purity improved significantly over a conventional expression embedding, and one NK-like niche enriched for tumor-blood shared TCRs was associated with poor immunotherapy response across studies.

CloneTrast uses clonal identities from paired single-cell RNA and TCR sequencing as training labels, so the model learns T-cell clonal-functional states from gene expression alone and needs no TCR data at inference. Training used 26 datasets across 14 cancer types with more than 1.5 million T cells, and external testing used 11 datasets with more than 1 million T cells, including two cancer types unseen in training. On both training and test sets, patient clonal purity improved significantly over a conventional expression embedding, and one NK-like niche enriched for tumor-blood shared TCRs was associated with poor immunotherapy response across studies.

On more than 1.1 million annotated rat EEG/EMG segments, a robustness-oriented deep learning pipeline reaches 94.3% F1 for four-class sleep staging, 7.3 and 6.4 points above SlumberNet's 87.0% and random forest's 87.9%. Most automated stagers were evaluated on random splits that preserve the training distribution, and degrade when subjects or drug conditions shift. The pipeline combines a global context vector, supervised contrastive learning and a dual-branch classifier to learn representations insensitive to subject and treatment; trained on saline recordings only and tested on unseen animals, every metric drops by less than 1%.

On more than 1.1 million annotated rat EEG/EMG segments, a robustness-oriented deep learning pipeline reaches 94.3% F1 for four-class sleep staging, 7.3 and 6.4 points above SlumberNet's 87.0% and random forest's 87.9%. Most automated stagers were evaluated on random splits that preserve the training distribution, and degrade when subjects or drug conditions shift. The pipeline combines a global context vector, supervised contrastive learning and a dual-branch classifier to learn representations insensitive to subject and treatment; trained on saline recordings only and tested on unseen animals, every metric drops by less than 1%.

Single-cell reference mapping no longer lets class representatives drift during training. SCLSC built each type representative from a mean expression profile, so its position moved as optimization proceeded; SOLAR assigns every reference label a fixed orthogonal direction before training and lets the encoder learn only where cells land in that space. Across nine held-out batches from human immune, lung and pancreas datasets at 30 dimensions, mean query macro-F1 was 0.811, above 0.774 for matched PCA and below 0.838 for scANVI.

Single-cell reference mapping no longer lets class representatives drift during training. SCLSC built each type representative from a mean expression profile, so its position moved as optimization proceeded; SOLAR assigns every reference label a fixed orthogonal direction before training and lets the encoder learn only where cells land in that space. Across nine held-out batches from human immune, lung and pancreas datasets at 30 dimensions, mean query macro-F1 was 0.811, above 0.774 for matched PCA and below 0.838 for scANVI.

The work proposes BrainSTR, a spatio-temporal contrastive learning framework that learns state-consistent phase boundaries via a data-driven Adaptive Phase Partition module, identifies diagnostically critical phases with attention, and extracts disease-related connectivity within each phase using an Incremental Graph Structure Generator regularized by binarization, temporal smoothness, and sparsity; a spatio-temporal supervised contrastive learning approach then leverages diagnosis-relevant spatio-temporal patterns to refine the similarity metric between samples and build a well-structured semantic space. Experiments on ASD, BD, and MDD validate its effectiveness, and the discovered critical phases and subnetworks provide interpretable evidence consistent with prior neuroimaging findings.

The work proposes BrainSTR, a spatio-temporal contrastive learning framework that learns state-consistent phase boundaries via a data-driven Adaptive Phase Partition module, identifies diagnostically critical phases with attention, and extracts disease-related connectivity within each phase using an Incremental Graph Structure Generator regularized by binarization, temporal smoothness, and sparsity; a spatio-temporal supervised contrastive learning approach then leverages diagnosis-relevant spatio-temporal patterns to refine the similarity metric between samples and build a well-structured semantic space. Experiments on ASD, BD, and MDD validate its effectiveness, and the discovered critical phases and subnetworks provide interpretable evidence consistent with prior neuroimaging findings.