All Competitors

BERT-based predictor of DNA N6-methyladenine (6mA) modification sites, using word2vec encoding and cross-species transfer learning.

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

Protein language model that fuses Gene Ontology knowledge graphs with masked language modeling, improving protein function and interaction prediction.

Protein language model pretrained on UniRef90 with masked language modeling and Gene Ontology annotation prediction, at 16 million parameters.

Transformer that imputes missing CpG methylation states from sparse single-cell bisulfite sequencing, modeling genomic and cell-level structure.

Antibody-specific language model trained on the OAS database for restoring missing residues and generating high-quality sequence representations.

Medical-domain CLIP fine-tuned on radiology image-caption pairs from ROCO, serving as a drop-in visual encoder for medical visual question answering.

Unified platform implementing 155 biological language models for DNA, RNA, and protein sequence analysis at residue and sequence level.

Multi-modal self-supervised transformer for regulatory genomics, pre-trained on DNA sequence together with transcription factor binding matrices.

Protein complex structure prediction model extending AlphaFold 2 with paired MSA processing and ipTM scoring for multi-chain, multimeric assemblies.

Transformer that predicts gene expression and epigenomic signals from 200kb of DNA sequence, capturing distal enhancers up to 100kb from a promoter.

Suite of six protein language models, including ProtBERT and ProtT5, trained on up to 393 billion amino acids without multiple sequence alignments.

Protein structure prediction model that folds amino acid sequences into 3D structures with atomic accuracy, scoring a median GDT of 92.4 at CASP14.

Protein language model for zero-shot variant effect prediction, scoring mutations by log-odds from evolutionary sequences with no MSA or assay data.

Single-cell perturbation prediction model that forecasts transcriptional responses to drug combinations and doses never experimentally measured.

Transformer protein language model trained on 250 million protein sequences that learns structural and functional representations without supervision.

Bidirectional transformer for DNA using k-mer tokenization, fine-tunable for promoter, splice site, and transcription factor binding prediction.

Generalist deep learning algorithm for cell and nucleus instance segmentation using simulated diffusion flows, without per-dataset retraining.

Variational autoencoder for single-cell RNA-seq that models raw counts directly, learning latent cell representations without normalization.

Cross-species convolutional network trained jointly on human and mouse genomes to predict regulatory sequence activity and noncoding variant effects.

Sparse attention transformer that extends BERT to 8x longer sequences via random, local, and global attention, with genomic sequence applications.

Self-supervised 3D pretrained models for CT and MRI that learn anatomical representations from unlabeled volumes and transfer to segmentation tasks.

Variational autoencoder that predicts single-cell perturbation responses across cell types and species using latent space vector arithmetic.

Pretrained 3D-ResNet backbones for volumetric medical image analysis, co-trained across eight CT and MRI segmentation datasets for transfer learning.