Life Sciences

The Allen Institute for Brain Science and AWS have released Bloom, a single-cell multimodal model that encodes gene expression and chromatin accessibility into one shared cell representation while training on roughly 100-fold fewer cells than comparable pretrained models. On adult mouse whole-brain data, linear classifiers on Bloom embeddings reach 91% cross-validated adjusted balanced accuracy for 315 subclasses (multiome), 94% for 310 subclasses (scRNA-seq) and 94% for 297 subclasses (snRNA-seq), although the model saw only mouse multiome data.

The Allen Institute for Brain Science and AWS have released Bloom, a single-cell multimodal model that encodes gene expression and chromatin accessibility into one shared cell representation while training on roughly 100-fold fewer cells than comparable pretrained models. On adult mouse whole-brain data, linear classifiers on Bloom embeddings reach 91% cross-validated adjusted balanced accuracy for 315 subclasses (multiome), 94% for 310 subclasses (scRNA-seq) and 94% for 297 subclasses (snRNA-seq), although the model saw only mouse multiome data.

Neuralign places neural recordings from different sessions and animals, with different sampled neurons, into one tensor decomposition in which each component shares a single temporal curve while trial strength and neuron weights are estimated per session, so no matched neurons or matched trials are needed. On 93 brain-wide recordings from 19 mice, an unsupervised fit yielded a single component that separated the three anterior lateral motor cortex (ALM) optogenetic inhibition conditions, with balanced accuracy of 0.647 on held-out sessions against a chance level of 1/3. That component's change showed a modest association with the drop in behavioural accuracy (ρ = 0.266 over 180 session–inhibition observations).

Neuralign places neural recordings from different sessions and animals, with different sampled neurons, into one tensor decomposition in which each component shares a single temporal curve while trial strength and neuron weights are estimated per session, so no matched neurons or matched trials are needed. On 93 brain-wide recordings from 19 mice, an unsupervised fit yielded a single component that separated the three anterior lateral motor cortex (ALM) optogenetic inhibition conditions, with balanced accuracy of 0.647 on held-out sessions against a chance level of 1/3. That component's change showed a modest association with the drop in behavioural accuracy (ρ = 0.266 over 180 session–inhibition observations).

PDZ-binding motifs (PBMs) are short C-terminal sequences that determine which PDZ domains a protein binds. Designing PBMs by affinity has generally required a dedicated model per domain and made off-target control hard. This work encodes PDZ and PBM sequences into one 128-dimensional unit hypersphere, predicts pKd from their cosine similarity, and writes single-target, target-minus-off-target and dual-target-mean objectives as directions on that sphere; in four-position redesign for DLG4-PDZ3, 84 of the top 100 candidates fell in the top 1% of measured fitness in a published mutant library, versus 64 for the comparison method.

PDZ-binding motifs (PBMs) are short C-terminal sequences that determine which PDZ domains a protein binds. Designing PBMs by affinity has generally required a dedicated model per domain and made off-target control hard. This work encodes PDZ and PBM sequences into one 128-dimensional unit hypersphere, predicts pKd from their cosine similarity, and writes single-target, target-minus-off-target and dual-target-mean objectives as directions on that sphere; in four-position redesign for DLG4-PDZ3, 84 of the top 100 candidates fell in the top 1% of measured fitness in a published mutant library, versus 64 for the comparison method.

mRNA foundation models usually average token embeddings over the whole transcript before fitting a linear probe, so signals from short regulatory regions are diluted by longer ones. A parameter-free region-aware readout instead averages within the 5′ UTR, coding sequence and 3′ UTR and concatenates the three means; across 20 frozen models it raises Pearson correlation on translation and stability tasks by 11–25% on average, with no model significantly worse on any regression task. On classification tasks AUPRC changes range from −3% to +11%.

mRNA foundation models usually average token embeddings over the whole transcript before fitting a linear probe, so signals from short regulatory regions are diluted by longer ones. A parameter-free region-aware readout instead averages within the 5′ UTR, coding sequence and 3′ UTR and concatenates the three means; across 20 frozen models it raises Pearson correlation on translation and stability tasks by 11–25% on average, with no model significantly worse on any regression task. On classification tasks AUPRC changes range from −3% to +11%.

In ESM2 residue embeddings, polar/nonpolar binary patterns organize into a readable geometry. Generating 50 sequences for each of all 128 heptad binary patterns, 6400 in total, and running position-preserving PCA yields a jellyfish-shaped manifold: hydrophobic content spreads along PC1, while PC2 and PC3 encode heptad phase as near sine and cosine. A charge alphabet stays ordered, but scrambled amino-acid groupings or the non-protein model ByT5 lose the cyclic organization.

In ESM2 residue embeddings, polar/nonpolar binary patterns organize into a readable geometry. Generating 50 sequences for each of all 128 heptad binary patterns, 6400 in total, and running position-preserving PCA yields a jellyfish-shaped manifold: hydrophobic content spreads along PC1, while PC2 and PC3 encode heptad phase as near sine and cosine. A charge alphabet stays ordered, but scrambled amino-acid groupings or the non-protein model ByT5 lose the cyclic organization.

Across several large Perturb-seq datasets, a generative framework called Graph-guided Flow Matching (GFM) combines three biological knowledge graphs, covering Gene Ontology, protein interactions and perturbation similarity, with latent flow matching to predict single-cell transcriptomes after unseen perturbations. It outperforms a linear baseline and the deep-learning models CellFlow, GEARS and scGPT on unseen single-gene perturbations, trains up to 30 times faster, and keeps its advantage as training data shrink; on two-gene perturbations it does better when neither gene was seen in training.

Across several large Perturb-seq datasets, a generative framework called Graph-guided Flow Matching (GFM) combines three biological knowledge graphs, covering Gene Ontology, protein interactions and perturbation similarity, with latent flow matching to predict single-cell transcriptomes after unseen perturbations. It outperforms a linear baseline and the deep-learning models CellFlow, GEARS and scGPT on unseen single-gene perturbations, trains up to 30 times faster, and keeps its advantage as training data shrink; on two-gene perturbations it does better when neither gene was seen in training.

EEG foundation models transfer poorly as frozen feature extractors, so SeeEEG takes another route: it renders EEG as images using the montage, filtering and contrast settings of a clinical EEG viewer and trains only those display parameters, leaving the vision backbone frozen. With a frozen DINOv3 ViT-B as encoder, SeeEEG exceeds seven EEG foundation models in balanced accuracy on all eight clinical, sleep, affective and brain-computer interface datasets, reaching a mean rank of 1.3 by Cohen or AUROC against 2.9 for the strongest baseline, REVE.

EEG foundation models transfer poorly as frozen feature extractors, so SeeEEG takes another route: it renders EEG as images using the montage, filtering and contrast settings of a clinical EEG viewer and trains only those display parameters, leaving the vision backbone frozen. With a frozen DINOv3 ViT-B as encoder, SeeEEG exceeds seven EEG foundation models in balanced accuracy on all eight clinical, sleep, affective and brain-computer interface datasets, reaching a mean rank of 1.3 by Cohen or AUROC against 2.9 for the strongest baseline, REVE.

A global one-to-one renaming of the amino-acid alphabet leaves alignment-level statistics, column entropies and pairwise edit distances unchanged, yet contact maps from ESM-2, MSA Transformer and AMPLIFY collapse, while an inverse-covariance Potts model stays bit-identical. Across nine proteins and three seeds, ESM-2 650M keeps an overlap of only 0.015 with its own original contact set after random relabeling of all twenty residues, versus 0.655 when relabeling stays inside BLOSUM62 similarity groups. The contact readout therefore depends on canonical residue identity and cannot be cited as proof that these models have internalized coevolutionary statistics.

A global one-to-one renaming of the amino-acid alphabet leaves alignment-level statistics, column entropies and pairwise edit distances unchanged, yet contact maps from ESM-2, MSA Transformer and AMPLIFY collapse, while an inverse-covariance Potts model stays bit-identical. Across nine proteins and three seeds, ESM-2 650M keeps an overlap of only 0.015 with its own original contact set after random relabeling of all twenty residues, versus 0.655 when relabeling stays inside BLOSUM62 similarity groups. The contact readout therefore depends on canonical residue identity and cannot be cited as proof that these models have internalized coevolutionary statistics.

In cross-subject neural-image retrieval, comparing the mean of a few target observations with the global source mean conflates subject-specific representation shifts with differences in which stimuli were sampled. SA-LBC instead estimates a first-order shift by averaging residuals against same-stimulus source-population anchors, then shrinks the correction using source-derived uncertainty. On THINGS-EEG2 with 10 subjects under leave-one-subject-out evaluation, the Attention-CLIP ViT-B/32 configuration rises from 0.1867 MRR without calibration to 0.2013 and 0.2336, though retrieval gains remain representation-dependent.

In cross-subject neural-image retrieval, comparing the mean of a few target observations with the global source mean conflates subject-specific representation shifts with differences in which stimuli were sampled. SA-LBC instead estimates a first-order shift by averaging residuals against same-stimulus source-population anchors, then shrinks the correction using source-derived uncertainty. On THINGS-EEG2 with 10 subjects under leave-one-subject-out evaluation, the Attention-CLIP ViT-B/32 configuration rises from 0.1867 MRR without calibration to 0.2013 and 0.2336, though retrieval gains remain representation-dependent.

Finger-movement labels in non-human primates have long come from instrumented manipulanda fitted with bend sensors, which cap the dimensionality of measurable movement. Freebie uses several synchronized cameras and real-time pose estimation to output finger keypoints directly, reaching median end-to-end latencies of 15.8 ms at 30 FPS and 16.2 ms at 60 FPS, with a median absolute correlation of 0.96 against bend-sensor signals. EMG decoders trained on Freebie labels matched those trained on bend-sensor labels in closed-loop control, indicating that training a decoder need not require instrumenting the hand.

Finger-movement labels in non-human primates have long come from instrumented manipulanda fitted with bend sensors, which cap the dimensionality of measurable movement. Freebie uses several synchronized cameras and real-time pose estimation to output finger keypoints directly, reaching median end-to-end latencies of 15.8 ms at 30 FPS and 16.2 ms at 60 FPS, with a median absolute correlation of 0.96 against bend-sensor signals. EMG decoders trained on Freebie labels matched those trained on bend-sensor labels in closed-loop control, indicating that training a decoder need not require instrumenting the hand.