All Competitors

Protein language model over byte-pair-encoded amino acid tokens, fine-tuned for protein family classification and binary interaction prediction.

Glomerulus and nuclei detection in whole-slide pathology images, predicting a bounding circle rather than a box for rotation-consistent localization.

Peptide retention time prediction for LC-MS/MS proteomics, from a genetic-algorithm search over convolutional and bidirectional GRU architectures.

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

Pfam-pretrained protein sequence models — transformer, LSTM and ResNet — released with five tasks benchmarking transfer from frozen embeddings.

RNA-binding protein target site prediction from 1,000 bp of sequence, scoring how a noncoding variant disrupts binding across 88 RBPs.

Peptide tandem mass spectrum prediction from sequence, giving fragment-ion intensities and indexed retention time for any organism or protease.

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

Protein language model using a multiplicative LSTM over 24 million UniRef50 sequences to produce fixed-length embeddings for protein engineering.

Molecular descriptor model that embeds compounds into a continuous 512-dimensional space by translating between equivalent chemical line notations.

3D convolutional network that predicts subcellular fluorescence labels from transmitted-light microscopy, enabling label-free imaging of living cells.

Antibody paratope prediction model that identifies antigen-contacting residues from heavy and light CDR sequences alone, using CNN and RNN layers.

Chromatin feature prediction from 2 kb of DNA, scoring 2,002 transcription factor, DNase and histone profiles to rank noncoding variant effects.

Tissue-specific gene expression prediction from DNA sequence, scoring a noncoding variant as the log fold change it causes in each of 218 tissues.

Nucleus instance segmentation for fluorescence microscopy and H&E histology, predicting a star-convex polygon per pixel to separate crowded nuclei.

Dilated convolutional network that predicts cell-type-specific epigenetic and transcriptional profiles from DNA sequence across mammalian genomes.

Visible neural network simulating eukaryotic cell growth by embedding the Gene Ontology into its architecture for interpretable phenotype prediction.

Attention-based model predicting gene expression from histone modification signals across 56 cell types, with interpretable attention scores.

Convolutional neural network that predicts DNA accessibility from sequence across 164 DNase-seq cell types, enabling variant effect prediction.

Noncoding variant effect prediction from DNA sequence, scoring how an allele shifts 919 chromatin features across ENCODE and Roadmap cell types.