All Competitors

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

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

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.

Transformer that predicts protein-protein interactions at residue resolution, spanning mutations, PTMs, peptide-MHC binding, and disease variants.

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.

Protein language model trained on roughly 2.8 billion sequences, forming the representation core of Biohub's world model of protein biology.

Variant effect predictor pairing a protein language model with family-specific evolutionary constraints to score stability, binding, and epistasis.

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

Codon-level mRNA language model adapted from ESM-2 650M by swapping amino-acid tokens for codon tokens, transferring protein knowledge to mRNA tasks.

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.

Sparse autoencoders trained on protein language model embeddings to expose interpretable features and drive zero-shot variant effect prediction.

Protein conformational ensemble tokenizer that learns a discrete alphabet of states from molecular dynamics, reusable as a frozen feature layer.

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

Protein sequence embedding model, contrastively fine-tuned from ESM-2, that places functionally and structurally related proteins close together.

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

Protein language model aligning ESM sequence embeddings with molecular dynamics trajectories for zero-shot mutation effect and stability prediction.

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.

Direction-aware foundation model trained on bulk RNA-seq differential-expression profiles to simulate coordinated gene dynamics in viral infection.

Long-context RNA foundation model that predicts splicing, isoform abundance, and variant effects from 64 kb of unspliced pre-mRNA sequence.