All Competitors

Fine-tuned Enformer derivative that predicts discrete, interpretable cis-regulatory element class annotations (enhancer, promoter, insulator) directly from DNA sequence across human cell types.

A VAE trained on scRNA-seq reference data and applied frozen at inference to impute unmeasured genes and denoise spatial transcriptomics profiles.

A graph foundation model for fMRI brain networks, pretrained across 27 datasets with graph and language prompts for zero/few-shot generalization to unseen disorders.

Protein pretraining framework that learns representations directly from cryo-EM density maps, transferring to flexibility, active-site, binding-affinity, and stability tasks.

A zebrafish DNA sequence-to-function model predicting cell-type-specific single-cell expression across 85 cell-type x developmental-timepoint combinations during embryogenesis.

A constrained deep flow-matching framework for distributional translation of omics signatures across biological domains, such as mouse-to-human transcriptomics, without paired samples.

A subspecialty lung-pathology foundation model, fine-tuned from Virchow2 and prospectively validated across 32 clinical tasks spanning the lung diagnostic workflow.

A cross-modal transfer-learning model that adapts the ESM-2 650M protein language model to mRNA analysis by swapping amino-acid tokens for codon tokens, applied to mRNA benchmarks without re-training.

Multimodal deep-learning framework that detects and localizes DNA lesions directly from native nanopore sequencing, built on the damage-aware LesionBERT foundation model.

A domain-specific foundation model for zero-shot plant root image segmentation, built on a MobileSAM backbone and trained across nine diverse root datasets.

A transferable spatial-transcriptomics deconvolution model whose rank-based spot encoding lets one pretrained model generalize across tissues, disease states, and platforms without retraining.

A virtual cell foundation model pretrained on 23M+ cells from 5,000 patient samples for drug target and biomarker discovery.

Three fixed ProtGPT2 fine-tunes specialized for metalloprotein generation, trained on ProteinMPNN-derived synthetic sequences.

A motif-aware graph diffusion transformer for controllable molecular generation that transfers to unseen properties by learning only lightweight task embeddings with the generator frozen.

Meta-learning framework that transfers perturbation responses across cell lines, donors, and drugs from a few seed perturbations, using one-eighth the parameters of typical single-cell foundation models.

A generative protein-protein docking model that adapts AlphaFold-Multimer via flow matching, replacing the template module with a docking module.

A supervised, chromatin-informed foundation model that predicts regulatory activity directly from plant genomic sequence in Arabidopsis and rice.

A portable transformer that classifies tumour types and learns representations from somatic variants, with auto-downloading WGS and WES checkpoints.

EEG foundation model pretrained by spectrogram reconstruction that improves online directional motor-imagery brain-computer interface control.

A transformer that predicts protein-RNA binding affinity from Boltz-2 pre-structural embeddings via cross-modal attention, without requiring predicted structures.

Dual-encoder contrastive model that embeds protein domains and peptides into a shared space to predict domain-peptide binding specificity at proteome scale.

Protein language model that predicts RNA-binding domains, global RNA-binding propensity, and mutation effects at single-residue resolution from sequence.

A sequence-based learning-to-rank variant effect predictor that aligns and aggregates ~1,100 overlapping deep mutational scanning assays into an assay-agnostic measure of mutational tolerance.

A family of genomic language models fine-tuned from a pretrained DNA language model to detect and classify antibiotic resistance genes beyond reference-database limits.