Kahn C E, Curie-Cohen M, Stone W H
Tissue Antigens. 1980 May;15(5):447-54. doi: 10.1111/j.1399-0039.1980.tb00207.x.
An algorithm is presented for clustering antisera by computer. It has two novel features: the leading serum to which all other sera in the cluster are compared is chosen as the most centrally located serum in the cluster; the similarity between two sera is defined from the 2 X 2 table of serum reactions as s = 2a/(2a + b + c). This similarity index is a better measure of the similarity between two sera than conventional measures of similarity such as the correlation coefficient. Finally, the identification of cluster and serum subsets provides a more complete analysis of cross-reactivity and multispecificity, and suggests which absorptions might yield monospecific typing sera. A computer program which performs this serum cluster analysis is available upon request.
本文提出了一种利用计算机对抗血清进行聚类的算法。它有两个新颖的特点:聚类中所有其他血清与之比较的主导血清被选为聚类中位置最中心的血清;两种血清之间的相似性由血清反应的2×2表格定义为s = 2a/(2a + b + c)。与传统的相似性度量(如相关系数)相比,这个相似性指数是两种血清之间相似性的更好度量。最后,聚类和血清子集的识别提供了对交叉反应性和多特异性更完整的分析,并表明哪些吸收可能产生单特异性分型血清。如需执行此血清聚类分析的计算机程序可随时索取。