af2complex
deepvariant
af2complex | deepvariant | |
---|---|---|
1 | 5 | |
125 | 3,080 | |
- | 1.0% | |
1.8 | 9.1 | |
about 1 year ago | about 2 months ago | |
Python | Python | |
- | BSD 3-clause "New" or "Revised" License |
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af2complex
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Is there any software that can predict if two amino acid sequences would interact?
Also, I saw a "AF2Complex" add-on to AlphaFold 2 which seems to do exactly what I'm looking for, but I don't think that it's available on ColabFold yet and I don't have TB storage available. I'm not expecting you to know this, but is there any way to run an AF2 add-on on Colab?
deepvariant
- Look over my purchase, is there anything I should return?
- Welche Berufe haben ein riesiges Angebot an Arbeitskräften (zb. Marketing) und welche Berufe haben wirklich echten Fachkräftemangel und brauchen dringend mehr öffentliche Plattform?
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Give me your suggestions for papers with a Convolutional Neural Network in Bioinformatics
See https://www.nature.com/articles/nbt.4235 for the paper and https://github.com/google/deepvariant for the code.
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[D] Is deep learning having an impact in life sciences yet?
Deepvariant is a another example.
What are some alternatives?
ColabFold - Making Protein folding accessible to all!
galaxy - Data intensive science for everyone.
openfold - Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
masurca
Invariant-Attention - An implementation of Invariant Point Attention from Alphafold 2
scanpy - Single-cell analysis in Python. Scales to >1M cells.
awesome-protein-design - A curated list of awesome protein design research, software and resources.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Biopython - Official git repository for Biopython (originally converted from CVS)
slivar - genetic variant expressions, annotation, and filtering for great good.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.