All Competitors

Mamba-based mature RNA foundation model, contrastively trained on splice isoforms and 400+ mammalian species orthologs for mRNA property prediction.

RNA language model that predicts G-quadruplex formation and subtype from transcript sequence and scores how single-nucleotide variants alter folding.

RNA language model pretrained by masked language modeling over the whole human transcriptome, giving a fine-tunable encoder for mRNA sequence tasks.

Transformer-based generative language model for de novo RNA design, pretrained on 16 million non-coding RNA sequences from RNAcentral.

Generative bit-diffusion model that designs ADAR guide RNAs for any target adenosine, conditioned on desired editing efficiency and specificity.

Constrained RNA sequence design that turns an encoder-only RNA language model into a generator by greedy gradient search over nucleotides.

Latent diffusion model for RNA sequence generation that steers non-coding RNA and 5' UTR design with reward-model gradients at sampling time.

RNA language model adapted from ESM-2 by cross-modality transfer learning, matching RNA-native baselines with 1/8 the trainable parameters.

Codon optimization model designing host-specific coding DNA for a target protein, conditioned on any of 164 organisms across all domains of life.

Cas13d guide RNA efficiency predictor that pairs RNA-FM sequence embeddings with predicted secondary structure to score sgRNA on-target activity.

mRNA language model embedding the joint 5' UTR and coding sequence to predict translation efficiency, expression level, and transcript stability.

Conditional generator of bulk transcriptome and DNA methylation profiles, sampling tissue-, age- and species-matched synthetic omics samples.

Histopathology image translation with diffusion, moving H&E tiles between stains, tumor types, and organ sites and editing them from omics profiles.

Cell-free RNA language model for multi-cancer detection, classifying plasma samples straight from raw sequencing reads without gene annotation.

RNA language model that switches between nucleotide and byte-pair tokenization by input length, so one 117M encoder handles sequences of any length.

Generative biological foundation model placing DNA, RNA, and protein in one shared vocabulary, spanning genomic, proteomic, and cross-molecule tasks.

RNA tertiary structure prediction from sequence alone with a variational autoencoder over distance matrices, reaching 4.5 A RMSE on short RNAs.

Multimodal model fusing pre-trained DNA, RNA, and protein encoders through cross-attention to predict tissue-specific transcript isoform expression.

Discrete diffusion model that generates RNA secondary structure contact maps as pixel-wise segmentation, conditioned on RNA-FM and UFold features.

RNA language model pretrained on 23 million non-coding sequences with motif-level masking, then steered by predicted RNA type at fine-tuning.

Unified DNA, RNA, and protein foundation model with 1.8B parameters, pretrained across 169,861 species to learn the central dogma from sequence.

RNA language model pretrained on 231 bacterial and archaeal RNA families that generates full-length rRNA and scores mutations for thermostability.

Transformer language model for 5' UTR sequences that predicts mRNA translation efficiency, ribosome loading, and protein expression levels.