AI for Science 论文解读合集（持续更新ing），论文/数据集/教程下载：hyper.ai

In-Silico Modeling of Metabolic Heterogeneity using Single-Cell Transcriptomes

Reconstruction algorithms for Python

Graph Neural Network Library for PyTorch

Call for participation in the impact of LLM for scientific discovery

Code and data for in silico strain design for enhanced production of +100 typical bio-products with yeast as a cell-factory. Predictions are based on the ecYeastGEM model avaiable at ecModels.

A constraint-based method for prediction of metabolic engineering targets using ecModels of metabolism

single cell Flux Estimation Analysis (scFEA) Try the below web server!

Enzyme-constrained genome-scale models in python

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

A universal metabolic model knowledge database (pan-GEM) for current existing yeast species with genome sequence, based on which we will build the GEMs of high quality for each yeast species.

StrainDesign is a python package for the computational design of metabolic networks and based on COBRApy

COSMIS is a framework for quantifying the mutational constraint on amino acid sites in 3D spatial neighborhoods. The framework currently maps the landscape of 3D mutational constraint on 6.1 amino …

PhyloFisher is a software package written in Python3 that can be used for the creation, analysis, and visualization of phylogenomic datasets that consist of protein sequences.

The consensus GEM for Saccharomyces cerevisiae

Files for the Genome Design/Genome Analytics Short-course organized by the SBRG

Making Protein folding accessible to all via Google Colab!

Platform for designing and evaluating Graph Neural Networks (GNN)

Geometric Vector Perceptrons --- a rotation-equivariant GNN for learning from biomolecular structure

STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2021)

Message Passing Neural Networks for Molecule Property Prediction