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The Gini methodology is a family of mathematical models that describe various relations in or between variables [1,2]. The basic concept of Gini methodology is the Gini coefficient (also known as Gini index, or Gini ratio), which measures the inequality of a distribution (e.g., income) with values ranged from 0 (complete equality) to 1 (complete inequality), has been popularly used in economics for quantifying the income inequality in a country [3,4]. Due to the superiority of analyzing data with normalized and non-normalized distribution [2], Gini coefficient and the derived statistical algorithms have been extended to apply in disciplines as diverse as social science, chemistry and engineering. Recently, the Gini methodology has also been introduced to biology for inferring transcription regulation relationships from gene expression data [5], and for exploring the symbiosis and pathogenesis of human immunodeficiency virus type 1 (HIV-1) infection [6].
Ma C, Huang SH, Zhou Y (2014) Measuring Inequalities in Gene Coexpression Networks of HIV-1 Infection Using the Lorenz Curve and Gini Coefficient. J Data Mining Genomics Proteomics 5:148.
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