A Knowledge-Driven Approach to Extract Disease-Related Biomarkers from the Literature

Table 6

Comparison of disease-biomarkers pairs identified by the text mining (TM) approach with disease-biomarkers annotations in DisGeNET, based on MeSH disease classification [31].


MeSH disease class	MeSH disease class name	Number of disease-biomarker associations	The number validated with DisGeNET (%)

C01	Bacterial infections and mycoses	1,529	164 (10.73)
C02	Virus diseases	3,297	302 (9.16)
C03	Parasitic diseases	590	82 (13.90)
C04	Neoplasms	31,627	5,264 (16.64)
C05	Musculoskeletal diseases	5,771	388 (6.72)
C06	Digestive system diseases	8,154	1,156 (14.18)
C07	Stomatognathic diseases	2,531	195 (7.70)
C08	Respiratory tract diseases	5,460	735 (13.46)
C09	Otorhinolaryngologic diseases	770	40 (5.19)
C10	Nervous system diseases	10,819	1,132 (10.46)
C11	Eye diseases	2,513	226 (8.99)
C12	Male urogenital diseases	5,110	666 (13.03)
C13	Female urogenital diseases and pregnancy complications	6,432	863 (13.42)
C14	Cardiovascular diseases	9,310	1,393 (14.96)
C15	Hemic and lymphatic diseases	7,689	948 (12.33)
C16	Congenital, hereditary, and neonatal diseases and abnormalities	10,382	397 (3.82)
C17	Skin and connective tissue diseases	6,724	851 (12.66)
C18	Nutritional and metabolic diseases	6,314	711 (11.26)
C19	Endocrine system diseases	5,253	681 (12.96)
C20	Immune system diseases	10,210	1,393 (13.64)
C21	Disorders of environmental origin	2	0 (0.00)
C23	Pathological conditions, signs, and symptoms	8,212	606 (7.38)
C24	Occupational diseases	72	11 (15.28)
F01	Behavior and behavior mechanisms	594	24 (4.04)
F03	Mental disorders	2,810	613 (21.89)

BioMed Research International

A Knowledge-Driven Approach to Extract Disease-Related Biomarkers from the Literature

Table 6