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# GigaHeart ## A Cardiac-specific CT Foundation Model for Heart Transplantation

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RNAElectra is a nucleotide-resolution RNA language model trained using an ELECTRA-style objective for efficient and discriminative representation learning. The model produces contextualized embeddings for RNA sequences and is designed for downstream transcriptomic and regulatory modeling tasks.

## Overview This model, developed by the National Institute for Health and Care Research (NIHR), assigns HRCS Research Activity Codes to research awards using the award title and abstract (micro F1 = 0.60). When tags are aggregated to Research Activity Groups (RAGs), performance increases to a…

## Overview This model, developed by the National Institute for Health and Care Research (NIHR), assigns HRCS Health Categories (HCs) to research awards using the award title and abstract (micro F1 = 0.81). It is a multi-label transformer classifier built on BiomedBERT-large, domain-adapted (DAPT)…

Vortex Scientific is a from-scratch AI model family designed for deep scientific reasoning. Built from the ground up with a novel hybrid state-space + attention architecture, optimized for consumer laptop hardware (Apple Silicon MacBooks and Nvidia 4060 laptop GPUs).

Kyral's AI medical health assistant Medichat is an open-source, privacy-first AI health assistant that puts patients back in control of their health data. Medichat is powered by QWEN3-NEXT-80B, a custom-trained open-source model fine-tuned on the de-identified PMC Patient Summaries dataset — a…

Trama v1 ModelCard ---

In search enginers, rerankers are crucial for improving the accuracy of your retrieval system.

This repository contains the model presented in D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation.

For a convenient overview and download list, visit our model page for this model.

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Stereoscope is a variational inference model for single-cell RNA-seq data that can learn a cell-type specific rate of gene expression. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in Stereoscope.

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.

ScANVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. In addition, to scVI, ScANVI is a semi-supervised model that can leverage labeled data to learn a cell-type classifier in the latent space…

ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. The learned low-dimensional latent representation of the data can be used for visualization and clustering.

Stereoscope is a variational inference model for single-cell RNA-seq data that can learn a cell-type specific rate of gene expression. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in Stereoscope.

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.

ScANVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. In addition, to scVI, ScANVI is a semi-supervised model that can leverage labeled data to learn a cell-type classifier in the latent space…

ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. The learned low-dimensional latent representation of the data can be used for visualization and clustering.

Stereoscope is a variational inference model for single-cell RNA-seq data that can learn a cell-type specific rate of gene expression. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in Stereoscope.

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.

ScANVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. In addition, to scVI, ScANVI is a semi-supervised model that can leverage labeled data to learn a cell-type classifier in the latent space…

ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. The learned low-dimensional latent representation of the data can be used for visualization and clustering.

Stereoscope is a variational inference model for single-cell RNA-seq data that can learn a cell-type specific rate of gene expression. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in Stereoscope.

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.

ScANVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. In addition, to scVI, ScANVI is a semi-supervised model that can leverage labeled data to learn a cell-type classifier in the latent space…

ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. The learned low-dimensional latent representation of the data can be used for visualization and clustering.

Stereoscope is a variational inference model for single-cell RNA-seq data that can learn a cell-type specific rate of gene expression. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in Stereoscope.

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.

ScANVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. In addition, to scVI, ScANVI is a semi-supervised model that can leverage labeled data to learn a cell-type classifier in the latent space…

ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. The learned low-dimensional latent representation of the data can be used for visualization and clustering.

Stereoscope is a variational inference model for single-cell RNA-seq data that can learn a cell-type specific rate of gene expression. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in Stereoscope.

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.

ScANVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. In addition, to scVI, ScANVI is a semi-supervised model that can leverage labeled data to learn a cell-type classifier in the latent space…

ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. The learned low-dimensional latent representation of the data can be used for visualization and clustering.

Stereoscope is a variational inference model for single-cell RNA-seq data that can learn a cell-type specific rate of gene expression. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in Stereoscope.

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.

ScANVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. In addition, to scVI, ScANVI is a semi-supervised model that can leverage labeled data to learn a cell-type classifier in the latent space…

ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. The learned low-dimensional latent representation of the data can be used for visualization and clustering.

Evo 2 is a state-of-the-art DNA language model trained autoregressively on trillions of DNA tokens.

Sahal Shaji Mullappilly\, Mohammed Irfan K\, Omair Mohamed, Mohamed Zidan, Fahad Khan, Salman Khan, Rao Muhammad Anwer, and Hisham Cholakkal

BioCLIP is a foundation model for the tree of life, built using CLIP architecture as a vision model for general organismal biology. It is trained on TreeOfLife-10M, our specially-created dataset covering over 450K taxa--the most biologically diverse ML-ready dataset available to date.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 31% better perplexity than standard knowledge distillation at 3.8x compression.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 54% better perplexity than standard knowledge distillation at 9.4x compression.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 87% better perplexity than standard knowledge distillation at 20x compression.