All Competitors

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

Multimodal Q-former that fuses DNA sequence, gene context, protein function, and text for zero-shot variant interpretation with a frozen LLM.

Unified bio-language Mixture-of-Experts model spanning DNA, protein sequence and structure, and biological text across eight task families.

Microbiome world model that treats a community as a set of taxa, scoring how well each member fits and predicting community dynamics zero-shot.

DNA language model combining Mamba state-space layers, gated dilated convolutions, and Fourier attention to capture multi-scale regulatory patterns.

Microbiome foundation models that treat microbial community composition as a language, enabling zero- and few-shot transfer across prediction tasks.

Autoregressive DNA foundation model for variant effect prediction, using 6-mer tokenization to match Evo2-7B win rates at far higher throughput.

Generative multimodal foundation model spanning DNA, RNA, and protein, with any-to-any inference across genome, transcriptome, and proteome.

Autoregressive nucleotide-and-text foundation model generating DNA and RNA sequences from natural-language prompts that name species and function.

Genomic foundation model for rice, pretrained on 422 Oryza genomes with a 1 Mbp context window and a 1.25B-parameter mixture-of-experts transformer.

Generative DNA foundation model trained on 91.7M nucleotide sequences and annotations for species classification and mutation effect prediction.

Chromatin-informed foundation model predicting regulatory activity and chromatin state directly from plant genomic sequence in Arabidopsis and rice.

Genomic foundation model that learns DNA representations by predicting masked regions in latent space rather than reconstructing raw nucleotides.

Long-context plant DNA language model, 676M parameters on a Mamba2 backbone, pretrained on 65 angiosperm genomes for cross-species variant annotation.

Transformer that classifies tumour types and subtypes from somatic variants in whole-genome and whole-exome data, with auto-downloading checkpoints.

Discrete diffusion model that designs regulatory DNA with tunable cell-type-specific activity and learns activity-predictive representations.

DNA language model that replaces fixed tokenization with conservation-guided patching, letting models up to 10x smaller match top genomic benchmarks.

Genomic foundation model for Cypriniformes fish, built on a Mamba-2 state space model with a 32 kb context window for long-range genome modeling.

Self-supervised transformer for population genetics, pretrained on 1000 Genomes data, that detects positive selection via haplotype-wise attention.

DNA foundation model using masked discrete diffusion to unify bidirectional sequence understanding and de novo generation in one architecture.

Large language model trained on functional genomics data to prioritize novel therapeutic targets from genome-wide CRISPR knockout screens.

Foundation model for 3D genome architecture, using masked locus modeling over genome-wide contact profiles to capture chromosome-scale organization.

Multimodal deep learning model that predicts protein-mediated chromatin contact maps and loops de novo from protein-binding profiles and sequence.

Plant genomic foundation models from 0.1B to 1B parameters, pretrained on 43 phylogenetically diverse plant genomes for variant effect prediction.