Score Variant

Rank Score Normalization

The rank score is MAXMIN normalized into range (0, 1) according to the following formula:

RankScoreNormalized = (RankScore - CategorySumMin) / (CategorySumMax - CategorySumMin)

where RankScore is the sum of rank score across categories (including rules such as min, max, sum etc) RankScore = SUM(Score_category_n) for 0...n categories and CategorySumMin is the sum of minimal score values for all categories, i. e CategorySumMin = SUM(CategoryMin_n) for 0...n categories. The same applies to CategorySumMax = SUM(CategoryMax_n) for 0...n categories.

Refer to score_variants.py::score() method for implementation details.

Additionally, also read in the score-compounds.md on compound scoring step that affects final rank score values.