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Releases: GenoML/genoml2

v1.5.4 - New pip version

17 Jun 23:45

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Several key updates, including:

  1. Addition of preliminary multiclass prediction capabilities (which is still under active development)
  2. Refinement of continuous module
  3. Bug fixes, both big and small
  4. Changes to munging and harmonization workflows to allow train and test data to be munged together upfront and ensure they are preprocessed under the same conditions
  5. File restructuring for increased modularity
  6. Addition of log file in outputs for reproducibility

thank you @spencermg for implementing these improvements and @nvk23 for feedback!

v1.0.1 - Updates

07 Nov 15:10
a6001c7

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Updates to packages, README, and minor bug fixes

v1.0.0-beta.11

02 Mar 21:30

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This release includes

  • Fix to issue #25
  • Other minor bug fixes
  • Updates to the requirements file

v1.0.0-beta.10

26 Jan 22:28

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This release includes the "training wheels" to prevent nominating overfitting and also includes new probability plots across all supervised ML scripts

v1.0.0-beta.9

16 Dec 19:15
4ec078a

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Changes in discrete training to nominating the best algorithm and the probability plots

v1.0.0-beta.8

18 Nov 22:26

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Minor, but necessary, changes

v1.0.0-beta.7

17 Nov 16:40

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This beta release addresses the following issues:

  • Issue #18 : PLINK pre-maturely killed when certain flags are chosen
  • Issue #19 : --adjust_data crashing if features are removed at earlier munging stages

v1.0.0-beta.6

16 Nov 16:41

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This release has addressed a few issues:

  • Issue #12: [Experimental] fix to using tuned models to test unseen, harmonized data
  • Issue #16: Users now have more control to adjusting without reducing if desired, and choosing to normalize after adjusting if desired
  • Issue #17: Users can now control stringency of r^2 value when pruning with PLINK using the --r2_cutoff

The README has been updated to reflect these changes better, please report any issues to the GitHub issues page!

v1.0.0-beta.5

27 Oct 18:54
86047a1

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Implementing a "global adjuster" - flags in the munging process to account for additional covariates and confounders you would like to adjust for. This comes from feature request/issue #11 - Adjust genotypes for tSNE/UMAP/PC loadings.

v1.0.0-beta.4

09 Sep 19:08

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Addressing issue #13 - next release might not be for a while!