All Competitors

RNA backbone torsion and pseudo-torsion angle prediction from sequence alone, built by fine-tuning a DNABERT checkpoint on solved RNA structures.

Fragment-ion intensity prediction for cross-linked peptides, covering cleavable DSSO and DSBU chemistries alongside non-cleavable DSS and BS3.

Disease embeddings learned from human genetic evidence and phenotype ontologies, placing rare and common conditions in one mechanistic vector space.

Wearable accelerometry foundation model distilled from a PPG encoder, predicting cardiovascular and health biomarkers from motion signals alone.

Latent diffusion model generating Cell Painting images for a compound from its predicted bioactivity profile, reaching unfamiliar chemical matter.

Antibody CDR sequence and structure co-design from the whole antigen, using a relation-aware equivariant graph network with no specified epitope.

Self-supervised histopathology encoder that models tissue entities as a graph and pretrains by latent graph diffusion over masked subgraphs.

Multimodal foundation model for wearable physiological sensing across PPG, ECG, EEG, GSR, and IMU signals, using channel-aware attention.

Glucose forecasting from continuous glucose monitor streams over a two-hour horizon. Cuts one-hour rMSE 48.51% on OhioT1DM without training on it.

Pathology vision-language model for whole-slide diagnosis, adding lesion detection and segmentation to visual question answering on gigapixel images.

Cryo-EM heterogeneous reconstruction that models particles as one of K neural fields, resolving compositional and conformational states ab initio.

PROTAC degrader generation pipeline that screens target-binding fragments, then builds molecules under structure and physicochemical constraints.

Graph transformer over 3D protein structures predicting solvation free energy, hydrodynamic radius, diffusion constants, and molecular volume.

Biomedical multimodal LLM that answers questions about medical images and returns pixel-level segmentation masks, using a mixture-of-experts design.

DNA language model for unassembled metagenomic reads, pretrained to recover coding fraction and reading frame from 60-300 bp fragments.

Spatial gene expression prediction from H&E tumor histology, aligning a pathology foundation model with a single-cell RNA-seq foundation model.

Reference-free enzyme class annotation of single unassembled metagenomic reads, assigning top-level EC classes without assembly or homology search.

Multimodal foundation model integrating protein sequence, structure, and natural language to model and generate protein phenotypes across scales.

Text-guided medical image synthesis across OCT, fundus, X-ray, CT and MRI. Synthetic data lifts downstream clinical tasks by 12-17%.

Antibody language model trained on human clonal families, proposing mutations that mimic in vivo affinity maturation for binding and stability.

EEG foundation model for brain-computer interface decoding, factorizing self-attention into parallel spatial and temporal branches.

Bilingual Arabic-English medical multimodal model built on Llama 3.1 for radiology, CT, and histology image understanding and question answering.

Diffusion surrogate for DNA breathing simulations, generating biophysical features genome-wide to sharpen transcription factor binding prediction.