Repository Index

Code for running RFdiffusion

This package contains deep learning models and related scripts for RoseTTAFold

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

• deep-learning
• protein-sequences
• protein-structure
• semi-supervised-learning

Generation of protein sequences and evolutionary alignments via discrete diffusion models

• discrete-diffusion
• generative-model
• multiple-sequence-alignment
• protein-sequences

Generative biopolymer design with rich conditioning.

The repository host gives no description.

A Practical and Efficient NCBI Taxonomy Toolkit, also supports creating NCBI-style taxdump files for custom taxonomies like GTDB/ICTV

• bioinformatics
• taxonomy
• cross-platform
• lineage

A dependency-free cross-platform swiss army knife for PDB files.

• python
• protein
• pdb
• bioinformatics

Probabilistic cell segmentation for in situ spatial transcriptomics

Accurate and efficient spot detection for microscopy data

Python package for image-based profiling

• cytominer
• microscopy
• morphological-profiling
• python

NeuroAnatomy Toolbox: An R package for the (3D) visualisation and analysis of biological image data, especially tracings of single neurons.

• r
• neuroanatomy-toolbox
• cran
• neuron

The framework for quantification of neuronal anatomy

• neuroscience
• neuroanatomy-toolbox
• imagej
• imagej2

High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations

Methods for Image-Based Cell Profiling

• profiling
• microscopy
• r

A versatile single-cell cross-modality translation method via dual-aligned variational autoencoders

Single-cell fluorescence microscopy data analysis

• microscopy
• fluorescence-microscopy-imaging

DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.

Vendor-agnostic, FHIR-Genomics-aligned API contract for cross-vendor NGS interpretation. JSON Schema + TypeScript + Python pydantic.

Python library for statistical analysis of mRNA and protein distributions in fluorescent microscopy images