All Competitors

Genomics foundation model that represents individual DNA fragments in a learned semantic space for cell-free DNA cancer detection and cell typing.

Physics-informed graph neural network predicting metabolite concentrations from gene expression, generalizing zero-shot to unseen metabolites.

Heterograph foundation model for metabolic engineering, fusing protein, chemistry, and text embeddings over a 6.9M-node biological knowledge graph.

Enhancer RNA mapping model that locates eRNA loci genome-wide from DNA sequence and aggregated RNA-seq signal using a CNN-transformer architecture.

Plant genome foundation model for ab initio gene structure annotation, predicting genes, coding sequences, and exons at single-nucleotide resolution.

Ab initio gene annotation model that predicts gene boundaries and exon-intron structure from raw DNA, generalizing zero-shot to unseen species.

Multimodal foundation model for precision neurology that reconstructs a patient's molecular brain state from blood to predict disease progression.

Gene representation framework fusing DNA, transcript, protein, text, and single-cell embeddings into one latent space that survives missing views.

Genomic language model from Radical Numerics with a 2 Mbp context window, built for zero-shot variant effect prediction and sequence design.

Generative transformer for phylogenetic inference that transduces sets of unaligned molecular sequences directly into Newick-format trees.

Family of ten compact GPT-2 decoder-only DNA language models spanning BPE vocabularies from 16 to 8192 tokens, built for lossless genome compression.

860M-parameter generative single-cell foundation model that jointly represents and generates epigenomic, transcriptomic, and proteomic modalities.

Reasoning LLM that predicts antimicrobial susceptibility of clinical bacterial isolates and supplies mechanistic explanations for each prediction.

Fine-tuned Enformer derivative that annotates cis-regulatory elements from DNA sequence, emitting enhancer, promoter, and insulator class labels.

Gene regulation model that conditions a pretrained DNA sequence embedding on CpG methylation to capture cell-type and allele-specific regulation.

Transcription factor-DNA binding specificity prediction from sequence, with a physics-derived dual-encoder trained by symmetric contrastive learning.

CRISPR off-target prediction model that scores gRNA-DNA specificity from sequence, framing guide-target recognition as cross-modal retrieval.

Genomic foundation model with 120M parameters that learns adaptive DNA token boundaries by dynamic chunking, not fixed k-mer or byte-pair tokens.

Self-supervised foundation model that embeds cancer genomes from somatic SNVs and copy-number alterations across 33 tumor types for tumor subtyping.

Zebrafish sequence-to-function model predicting cell-type-specific gene expression from DNA sequence across embryonic development.

700M-parameter DNA language model pretrained on the rice pangenome, serving as a reusable base model for crop genomics and molecular breeding.

Contrastive promoter-protein pretraining that aligns bacterial promoters with their encoded proteins to learn regulatory genomics representations.

Mixture-of-Experts genomic foundation model for the human microbiome, with 4.7B parameters pretrained on bacterial, archaeal, and phage genomes.

Promptable DNA language model that generates multi-kilobase plasmid sequences from plain-language component specs, refined with verifiable rewards.