Repository Index

Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications

• cell-annotation
• multiplexed-imaging
• mpif

C# math expression library with symbolic computation support

• csharp
• dotnet
• expression-parser
• math

Classification and Gradient-based Localization of Chest Radiographs using PyTorch.

• fine-tuning
• vgg16
• resnet18
• densenet121

Deep learning model for single-cell inference of multi-omic profiles from a single input modality.

• single-cell
• scrna-seq
• scatac-seq
• deep-neural-networks

MCP server for spatial transcriptomics analysis through natural language interfaces.

• bioinformatics
• cell-communication
• deconvolution
• machine-learning

Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.

• alphafold3
• docking
• drug-design
• drug-discovery

3D shape analysis using deep learning

• cancer-biology
• cell-biology
• deep-learning
• geometric-deep-learning

Java Scientific Computing Library - Signal Processing, FFT, Filters, PSD, DCT, SciPy-like APIs for JVM & Android used in Machine Learning and Data Science.

• android
• java
• python
• scipy

Accurate segmentation of bacterial microscope images using deep learning synthetically generated image data.

• microscopy
• image-processing
• segmentation
• biology

Workbench for practical machine learning in Ruby.

• machine-learning
• machine-learning-algorithms
• neural-network
• neural-networks

chemmodlab: A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models

Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).

• deep-reinforcement-learning
• generative-model
• vae
• molecule-generation

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.

Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning

A dependency-free Linear Algebra, Statistics, and Machine Learning library written from scratch in V

• ml
• statistics
• v

GNN-based gene-selection deep learning model for drug response prediction