Data visualisation web components for the life sciences.
- biohackeu21
- bioinformatics
- visualization
- webcomponents
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Data visualisation web components for the life sciences.
Python package for image-based profiling
A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
The repository host gives no description.
The repository host gives no description.
A tool for cell instance aware segmentation in densely packed 3D volumetric images
Learning cell communication from spatial graphs of cells
CASSIA: A Multi-Agent LLM-Based Single-Cell Cell Type Annotation Framework
Integrated pipeline for multiplexed image analysis
Official repo for CellPLM: Pre-training of Cell Language Model Beyond Single Cells.
Structural variant detection and association testing
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
BulkFormer: A large-scale foundation model for human bulk transcriptomes
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions
NeuroAnatomy Toolbox: An R package for the (3D) visualisation and analysis of biological image data, especially tracings of single neurons.
PyTorch implementation of the MIDAS algorithm for single-cell multimodal data integration (Nature Biotechnology 2024).
Python package for analysis of multiomic single cell RNA-seq and ATAC-seq.
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
Repository for paper scMulan: a multitask generative pre-trained language model for single-cell analysis.
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High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics
Methods for Image-Based Cell Profiling