Computational Biology
Showing 32 of 68 projects
A BERT-based foundation model pretrained on large-scale scRNA-seq data for automated cell type annotation in single-cell analysis.
An integrative hetnet (heterogeneous network) encoding biomedical knowledge for drug repurposing and discovery.
A curated collection of research papers tracking the frontier of AI-based protein design methods and applications.
GraphDTA predicts drug-target binding affinity using graph neural networks for drug discovery.
Deep learning model using convolutional neural networks to predict drug-target binding affinity from protein sequences and compound SMILES.
A zero-shot foundation model for generating universal embeddings from single-cell gene expression data.
Ankh is a state-of-the-art protein language model for general-purpose protein modeling and engineering tasks.
A deep convolutional neural network that predicts RNA-seq coverage at 32bp resolution from DNA sequence.
A transformer-based model for predicting drug-target interactions using substructural pattern mining and augmented transformer encoders.
A computational pipeline that predicts drug-target interactions by learning low-dimensional vector representations from heterogeneous biological networks.
A curated list of software, tools, pipelines, and plugins for image analysis in biological research.
A Julia package providing core utilities for manipulating, comparing, and analyzing phylogenetic trees and networks.
A probabilistic cell segmentation method for spatial transcriptomics data from platforms like Xenium, CosMx, MERSCOPE, and Visium HD.
A deep learning model using transformer architecture to predict compound-protein interactions from molecular and protein sequences.
Automated 3D brain image registration tool for aligning sample data with anatomical atlases across multiple species.
A large transformer foundation model for single-cell RNA sequencing data analysis, including gene network inference, denoising, and cell annotation.
A deep bilinear attention network framework with adversarial domain adaptation for interpretable drug-target interaction prediction.
A curated collection of databases, software, and papers for computational biology research.
A knowledge-informed cross-species foundation model pre-trained on over 120 million human and mouse single-cell transcriptomes to decipher universal gene regulatory mechanisms.
A deep learning framework for predicting chromatin profiles and sequence regulatory activities from DNA sequences and variants.
A suite of tools (wham and whamg) for sensitive and accurate structural variant detection and association testing from genomic sequencing data.
A performant JAX reimplementation of the UniRep model for generating protein sequence representations.
A curated list of resources for molecular docking, protein-protein docking, and related computational biology tasks.
A Python toolbox for analyzing multiplexed imaging data, featuring segmentation, pixel/cell clustering, and spatial analysis.
A pre-trained language model for single-cell RNA sequencing data that encodes cell-cell relations and accelerates inference for downstream tasks.
A curated collection of readings and resources for relational deep learning models applied to drug pair scoring tasks.
A curated collection of resources and survey paper on relational deep learning methods for drug pair scoring tasks.
A curated list of software, datasets, and publications for image-based profiling of biological phenotypes in drug discovery and cell biology.
A neural network model that integrates neighbor information from heterogeneous networks to discover new drug-target interactions.
A curated database of mechanistic paths from drugs to diseases via biological entities, using the Biolink Model for semantic interoperability.
A PyTorch deep generative model for integrating and imputing single-cell multimodal data with missing modalities.
A multitask generative pre-trained language model for zero-shot cell type annotation, batch integration, and conditional cell generation in single-cell transcriptomics.
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