All Competitors

Bulk tumor transcriptome model ensembling hundreds of variational autoencoders into interpretable cancer-specific latent spaces for 18 cancers.

Inverse RNA folding from contact maps: an axial-attention transformer designing sequences for pseudoknots, non-canonical pairs and multiplets.

RNA language model with 650M parameters pretrained on 36 million non-coding RNA sequences, generalizing structure prediction to unseen RNA families.

RNA foundation model trained on chemical-mapping data from millions of sequences, predicting reactivity, secondary structure, and degradation.

RNA secondary structure prediction from a single sequence, without MSAs. Axial-attention transformer reaching F1 0.764 on the TS-PDB benchmark.

RNA-binding protein affinity prediction at single-base resolution from sequence alone. One model spans 155 RBP targets across three cell lines.

RNA language model trained on multiple sequence alignments of Rfam families, predicting secondary structure and solvent accessibility from homology.

Generative RNA design model that samples family sequences from a VAE latent space constrained by Rfam covariance models and consensus structure.

RNA foundation model trained on chemical mapping data, with embeddings adapted to predict RNA secondary and tertiary structure and mRNA stability.

Asymmetric encoder-decoder transformer for single-cell RNA-seq that encodes only non-zero genes, cutting FLOPs 10-100x versus standard transformers.

Structure-based RNA virtual screening that scores small molecules against a binding site's base-pairing graph, around 10,000x faster than docking.

RNA 3D structure prediction pipeline pairing a transformer (RNAformer) that predicts inter-nucleotide geometries with Rosetta energy minimization.

RNA foundation model trained on 1 billion sequences, with a 400M-parameter variant for secondary and tertiary structure and functional annotation.

Multimodal aging clock predicting chronological age from DNA methylation or bulk RNA-seq, with a frozen backbone reused for disease target discovery.

Predicts 12 types of RNA modification sites from sequence, fine-tuning DNABERT representations fused with CNN-encoded sequence features.

Structure prediction for protein, RNA, and protein-RNA complexes in one AlphaFold2-derived framework that accepts MSA or language model encoders.

RNA language model pre-trained on 2M+ pre-mRNA sequences from 72 vertebrate species for splice-site prediction and variant effect analysis.

Predicts CLIP-seq crosslink counts along an RNA sequence base by base, separating protein-specific signal from experimental background.

Protein-nucleic acid complex structure prediction from sequence, folding protein, DNA and RNA chains in one network with confidence estimates.

RNA foundation model pretrained on 23.7 million non-coding RNA sequences, producing embeddings for structure prediction, annotation, and RNA design.

RNA methylation site predictor combining multiple sequence encodings with dilated convolution and BiLSTM layers to identify m6A and m1A sites.

RNA language model that learns base-level embeddings capturing sequence context and secondary structure, enabling fast structural alignment.

RNA virtual screening that reads a binding site's base-pairing graph and predicts the chemical fingerprint of its ligand to rank compound libraries.