Autoimmune Disease Classification Based on PubMed Text Mining

Abstract

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.

A better understanding of the role of the CTLA–CD80/86 axis in the treatment of autoimmune diseases

“…Autoimmune diseases can range from mild to life‐threatening, depending on the organ involved 1 . Various factors play a pivotal role in the occurrence of these diseases, such as genetic background, age, gender, and even environmental factors 2–4 . When the body encounters pathogens, the first cells of immune system that deal with them are antigen‐presenting cells (APCs), such as dendritic cells (DCs) 5 .…”

“…9 Autoimmune diseases not only have a negative impacts on the patient's quality of life, but also increase the chance of developing another autoimmune disease in 25% of patients, suggestive of existing similar molecular mechanisms in these diseases. 10 Therefore, an in-depth understanding of these molecular mechanisms may help to identify an appropriate treatment strategy and prevent the co-occurrence of other autoimmune diseases. Among the immunomodulation mechanisms, it can refer to the presence of Treg cells (FOXP3 + /CD4 + ) in terminating the immune responses and shortening the reaction time between APC and Teff.…”

Spectrum of Neurological Disorders Related to Autoimmune Diseases in Brazzaville, Congo

“…Autoimmune diseases are pathologies that result from a dysfunction of the immune system, which is caused by genetic, hormonal and environmental factors [1] [2] [3] [4] [5].…”

“…However, they mainly affect young and adult populations [7] [8] [9] [10]. Worldwide, they are among the leading causes of morbidity and mortality [1] [11] [12] and their prevalence varies between 4% and 9% [3] [5].…”

“…Autoimmune diseases include single organ-specific autoimmune diseases and systemic autoimmune diseases [2] [5]. However, combinations of single organ or systemic autoimmune diseases may be encountered in patients; these are multiple autoimmune syndromes [5] [13] [14] [15].…”