All Competitors

RNA-RNA interaction prediction framework that scores pairing between long transcripts directly from sequence using Nucleotide Transformer embeddings.

Multi-omics foundation model that folds DNA, RNA, and protein into one codon-level nucleotide representation following the central dogma.

Protein-conditioned RNA sequence and structure co-design, refining flow-matched backbones against Lennard-Jones and folding free-energy terms.

Tertiary structure-based RNA design model that fuses RNA backbone geometry with protein language model features of the bound partner protein.

RNA backbone conformer assignment from low-resolution maps, using Bayesian posteriors over a learned library of 3D suite shape clusters.

Crossmodal diffusion model synthesizing bulk tumor gene expression from H&E whole-slide images, so grading and survival prediction need no RNA assay.

Slide-level pathology foundation model that encodes a whole-slide image of any size into one embedding, supervised by paired sequencing data.

RNA 3D structure generation from sequence and base-pair maps using SE(3) flow matching, with no MSAs or structural templates.

Multi-transformer model that predicts tissue-specific alternative splicing outcomes and generalizes zero-shot to unseen cellular conditions.

RNA language model classifying transcripts as coding or long non-coding, using convolutional sequence encoding to fit whole transcripts in context.

Direct RNA nanopore basecaller that translates raw ionic-current signal into nucleotide sequence using a hybrid Mamba-Transformer backbone.

RNA modification classification from nanopore direct-RNA current, resolving m6A, inosine, pseudouridine, Gm, and m1A at single-base resolution.

Metagenomic foundation model pretrained on 1.5 trillion base pairs of wastewater DNA and RNA for pathogen detection and biosurveillance.

RNA foundation model unifying sequence representation, 3D structure prediction, and de novo design. Ranks first on 11 of 13 BEACON tasks.

Bulk RNA-seq foundation model that learns patient-level embeddings from binned gene expression for pan-cancer classification and survival prediction.

Graph neural network that predicts magnesium ion binding sites on RNA structures, powering SAXS-based validation of RNA solution conformations.

Protein and RNA sequence annotation that also names the residues driving each label, learned from sequence-level supervision alone.

RNA secondary structure and 3D motif prediction from alignments, using a probabilistic grammar that places over 50 known motifs by covariation.

RNA backbone torsion and pseudo-torsion angle prediction from sequence alone, built by fine-tuning a DNABERT checkpoint on solved RNA structures.

Codon optimization framework pairing a frozen ProtBert encoder with a masked codon head, so every designed coding sequence translates back exactly.

RNA foundation model with 1.6 billion parameters, pretrained on 42 million non-coding RNA sequences for structure prediction and RNA sequence design.

RNA-binding protein binding profiles predicted base by base across 800 bp windows, with an m6A signal channel that exposes methylation-RBP crosstalk.

Histopathology model predicting extrachromosomal DNA status from routine H&E slides by first inferring the tumor transcriptome from tile features.