protein_bert
By nadavbra
asap
Amino-Acid Sequence Annotation Predictor (ASAP) (by ddofer)
| protein_bert | asap | |
|---|---|---|
| 2 | 1 | |
| 456 | 25 | |
| - | - | |
| 6.0 | 0.0 | |
| 5 months ago | about 4 years ago | |
| Jupyter Notebook | Python | |
| - | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
protein_bert
Posts with mentions or reviews of protein_bert.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-30.
asap
Posts with mentions or reviews of asap.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-30.
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ProteinBERT: A universal deep-learning model of protein sequence and function
That should be trivial for it, attention models are good for "feature X exists somewhere in the text"/ That said, if your feature is just the presence of some short motif, why not just use n-gram/k-mer features? Those are invariant to location, and super fast/simple. I did some packages in the past for that, specially for proteins (PROFET, ASAP(for residue level)).
What are some alternatives?
When comparing protein_bert and asap you can also consider the following projects:
tape - Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.