Samuels Hadas, Malov Malki, Saha Detroja Trishna, Ben Zaken Karin, Bloch Naamah, Gal-Tanamy Meital, Avni Orly, Polis Baruh, Samson Abraham O
Azrieli Faculty of Medicine, Bar Ilan University, Safed 1311502, Israel.
School of Medicine, Yale University, New Haven, CT 06520, USA.
J Clin Med. 2022 Jul 26;11(15):4345. doi: 10.3390/jcm11154345.
Autoimmune diseases (AIDs) are often co-associated, and about 25% of patients with one AID tend to develop other comorbid AIDs. Here, we employ the power of datamining to predict the comorbidity of AIDs based on their normalized co-citation in PubMed. First, we validate our technique in a test dataset using earlier-reported comorbidities of seven knowns AIDs. Notably, the prediction correlates well with comorbidity (R = 0.91) and validates our methodology. Then, we predict the association of 100 AIDs and classify them using principal component analysis. Our results are helpful in classifying AIDs into one of the following systems: (1) gastrointestinal, (2) neuronal, (3) eye, (4) cutaneous, (5) musculoskeletal, (6) kidneys and lungs, (7) cardiovascular, (8) hematopoietic, (9) endocrine, and (10) multiple. Our classification agrees with experimentally based taxonomy and ranks AID according to affected systems and gender. Some AIDs are unclassified and do not associate well with other AIDs. Interestingly, Alzheimer's disease correlates well with other AIDs such as multiple sclerosis. Finally, our results generate a network classification of autoimmune diseases based on PubMed text mining and help map this medical universe. Our results are expected to assist healthcare workers in diagnosing comorbidity in patients with an autoimmune disease, and to help researchers in identifying common genetic, environmental, and autoimmune mechanisms.
自身免疫性疾病（AIDs）常常相互关联，约25%患有一种自身免疫性疾病的患者往往会患上其他合并的自身免疫性疾病。在此，我们利用数据挖掘的力量，基于PubMed中自身免疫性疾病的标准化共被引情况来预测其合并症。首先，我们在一个测试数据集中，使用先前报道的七种已知自身免疫性疾病的合并症来验证我们的技术。值得注意的是，预测结果与合并症相关性良好（R = 0.91），验证了我们的方法。然后，我们预测100种自身免疫性疾病的关联，并使用主成分分析对它们进行分类。我们的结果有助于将自身免疫性疾病分类到以下系统之一：（1）胃肠道，（2）神经，（3）眼睛，（4）皮肤，（5）肌肉骨骼，（6）肾脏和肺部，（7）心血管，（8）造血，（9）内分泌，以及（10）多种。我们的分类与基于实验的分类法一致，并根据受影响的系统和性别对自身免疫性疾病进行排名。一些自身免疫性疾病未被分类，且与其他自身免疫性疾病的关联性不佳。有趣的是，阿尔茨海默病与其他自身免疫性疾病如多发性硬化症相关性良好。最后，我们的结果基于PubMed文本挖掘生成了自身免疫性疾病的网络分类，并有助于绘制这个医学领域。我们的结果有望帮助医护人员诊断自身免疫性疾病患者的合并症，并帮助研究人员识别常见的遗传、环境和自身免疫机制。
