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wfondrie
Fast and flexible semi-supervised learning for peptide detection in Python
@wfondrie Discussed here --> https://github.com/wfondrie/mokapot/issues/74
Hello there! I was wondering if you had any interest in adding a plugin architecture to the package? By this a mean allowing someone (me) to implement packages that can...
Added supervised step in `model.py` in `fit()` method before direction is chosen and made corresponding init changes to `Model` and `PercolatorModel` class and `brew()` method in `brew.py`. Also made necessary...
Currently mokapot uses the old `numpy.random` functions to control random number generation. We should upgrade to using the new `numpy.random.Generator` interface. Alongside this change, we should add a `random_state` or...
To have truly valid precursor- and peptide-level FDR estimates for data independent acquisition (DIA) results, we need to add support for the peptide competition step described in section 2.4 of...
@jspaezp suggested switching over to [pytest-datadir](https://github.com/gabrielcnr/pytest-datadir) for managing our test data. This would have the benefit of simplifying our test fixtures, making it easier for folks to contribute. The user...
In crosslinking mass spectrometry experiments, we try to detect two peptides that are covalently linked from a single MS2 spectrum. Because the pairs can take various forms (target-target, target-decoy, decoy-decoy),...
Improve speed and limit memory consumption - stream input files for inference - add feature: skip deduplication - add feature: ensemble model - add feature: rescale input before inference with...
Hi Will, Thanks for your great tool! I was trying to use mokapot in combination with sage outputs to look at enzyme free peptidomics samples. Unfortunately, when I run this...
Polars seems like a promising replacement for Pandas in mokapot that should make things run even faster. https://www.pola.rs/