Optimising chemical reactions using machine learning
- chemistry
- machine-learning
- optimization
- bayesian-optimization
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Optimising chemical reactions using machine learning
Interpretable bilinear attention network with domain adaptation improves drug-target prediction.
⚡ Open-source software for deep learning-based digital pathology
Easy link prediction tool
A tool for cell instance aware segmentation in densely packed 3D volumetric images
Axon/Myelin segmentation using Deep Learning
A strongly-typed genetic programming framework for Python
Graph and Network algorithms in Julia
Layout algorithms for graphs and trees in pure Julia.
Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux
A .NET library that provides fast, accurate and automatic differentiation (computes derivative / gradient) of mathematical functions.
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
🔟 Implementation of rational number arithmetic for .NET with arbitrary precision.
A platform independent tensor library with autograd for the JVM
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions
👩💻 Matrix and other math, accelerated by WebGPU and WASM
The framework for quantification of neuronal anatomy
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
C# math expression library with symbolic computation support
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
An extensible .NET genetic algorithm library for optimization and artificial intelligence - Evolutionary computation and complex problem solving made simple
The repository host gives no description.
Deep learning model for single-cell inference of multi-omic profiles from a single input modality.
Working with time-dependent networks in Julia