ReMM score changelog
- 0.4:
  - Features:
    - For missing values using genome mean of feature for sequence and conservaton features. 1 for p-value. All other features have zero as missing value
    - Updating DGVCount to 02/25/2020 on hg19/hg38
    - Update dbVARCount to 10/20/2021 on hg19/hg38
    - Update ISCApath to 11/03/2021 on hg19/hg38
    - Replace tfbsConsSites with UCSC table encRegTfbsClustered on hg19/hg38
  - Software:
    - Using parSMURF for training
  - Complete retraining of hg19 and hg38 builds (hg19: AUROC=0.993; AUPRC=0.394; hg38: AUROC=0.996; AUPRC=0.610)

- 0.3.1.post1:
  - New hg38 release. Completely retrained on the new genome build.
    - Training data:
      - Liftover positives. No change in size.
      - Negatives used from CADD v1.4 GRCh38 (human derived), filtered as described in the original paper (https://doi.org/10.1016/j.ajhg.2016.07.005). Size slightly different (hg38: 13,902,234; hg19: 14,755,199) .
    - Features:
      - Same size as in hg19: 26 features.
      - We tried to use the same features as in hg19. Sometimes new versions of data have to be used (e.g. DGV, ISCA, dbVAR).
    - Training was done with the parSMURF implementation of hyperSMURF.
      - Same hg19 parameters are used.
    - Metrics via 10-fold cytoband cross-validation (same cytoband to fold map):
      - Area under the ROC curve: 0.996 (hg19: 0.989, see https://doi.org/10.1016/j.ajhg.2016.07.005)
      - Area under the precision recall curve: 0.548 (hg19: 0.441, see https://doi.org/10.1016/j.ajhg.2016.07.005)
  - Scores for hg19 in this release are the same as version 0.3.1. Only the files have been renamed.


- 0.3.1:
  - Bugfix of region chr17:79759050-81195210. Region is missing in older versions.

- 0.3:
  - First official public version.
  - Values for positions in training data are computed by cytoband-aware 10 fold cross-validation.
  - Other position scores are compted by a generalized model of all training data.
  - This version was used in the Genomiser publication (Smeley et.al. A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease. AHJG. 2016)