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ProtFlow Implementation of the RiffDiff pipeline to design enzymes from theozymes.
[NeurIPS 2024] RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models
A curated, interactive directory of protein design research groups worldwide.
Rapid Generation of Rare Event Pathways Using Direction Guided Adaptive Sampling: From Ligand Unbinding to Protein (Un)Folding
PDB-CAT aims to automatically categorize the mmCIF PDB structures based on the type of interaction between atoms in the protein and the ligand, and checking for any mutations in the sequence.
vessel segmentation, artery and vein, optic disc, vascular feature analysis
PLACER is graph neural network for local prediction of protein-ligand conformational ensembles.
End-to-end RNA-seq pipeline for cancer mutation screening using ENCODE data, variant calling, and visualization
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Implementation of Fourier Neural Operator using PyTorch
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
[ NeurIPS 2024 ] Official Codebase for "Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning"
Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website …
The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
A generative AI extension for JupyterLab
Notebooks to follow along with "Deep Learning for Biology" Chapters 2 to 6.
Python implementation of the main algorithms of the Learning From Interpretation Transitions (LFIT) framework
Source code accompanying the 'Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting' paper
Source code accompanying the 'An end-to-end attention-based approach for learning on graphs' paper