Zhou Hongyi, Edelman Brice, Skolnick Jeffrey
Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA, 30332, USA.
Sci Rep. 2025 Mar 20;15(1):9668. doi: 10.1038/s41598-025-93377-8.
Disease classification is important for understanding disease commonalities on both the phenotypical and molecular levels. Based on predicted disease mode of action (MOA) proteins, our algorithm PICMOA (Pan-disease Classification in Mode of Action Protein Space) classifies 3526 diseases across 20 clinically classified classifications (ICD10-CM major classifications). At the top level, all diseases can be classified into "infectious" and "non-infectious" diseases. Non-infectious diseases are classified into 9 classes. To demonstrate the validity of the classifications, for common pathways predicted based on MOA proteins, 77% of the top 10 most frequent pathways have literature evidence of association to their respective disease classes/subclasses. These results indicate that PICMOA will be useful for understanding common disease mechanisms and facilitating the development of drugs for a class of diseases, rather than a single disease. The MOA proteins, molecular functions, pathways for classes, and individual diseases are available at https://sites.gatech.edu/cssb/PICMOA/ .
疾病分类对于在表型和分子水平上理解疾病共性非常重要。基于预测的疾病作用模式（MOA）蛋白，我们的算法PICMOA（作用模式蛋白空间中的泛疾病分类）对20种临床分类（ICD10-CM主要分类）中的3526种疾病进行分类。在最高层次上，所有疾病可分为“传染性”和“非传染性”疾病。非传染性疾病分为9类。为了证明分类的有效性，对于基于MOA蛋白预测的常见途径，前10个最常见途径中有77%有文献证据表明与各自的疾病类别/亚类相关。这些结果表明，PICMOA将有助于理解常见疾病机制，并促进针对一类疾病而非单一疾病的药物开发。作用模式蛋白、分子功能、类别途径和个别疾病可在https://sites.gatech.edu/cssb/PICMOA/上获取。
