A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms

Abstract

BackgroundUnderstanding the molecular mechanisms involved in disease is critical for the development of more effective and individualized strategies for prevention and treatment. The amount of disease-related literature, including new genetic information on the molecular mechanisms of disease, is rapidly increasing. Extracting beneficial information from literature can be facilitated by computational methods such as the knowledge-discovery approach. Several methods for mining gene-disease relationships using computational methods have been developed, however, there has been a lack of research evaluating specific disease candidate genes.ResultsWe present a novel method for gathering and prioritizing specific disease candidate genes. Our approach involved the construction of a set of Medical Subject Headings (MeSH) terms for the effective retrieval of publications related to a disease candidate gene. Information regarding the relationships between genes and publications was obtained from the gene2pubmed database. The set of genes was prioritized using a “weighted literature score” based on the number of publications and weighted by the number of genes occurring in a publication. Using our method for the disease states of pain and Alzheimer’s disease, a total of 1101 pain candidate genes and 2810 Alzheimer’s disease candidate genes were gathered and prioritized. The precision was 0.30 and the recall was 0.89 in the case study of pain. The precision was 0.04 and the recall was 0.6 in the case study of Alzheimer’s disease. The precision-recall curve indicated that the performance of our method was superior to that of other publicly available tools.ConclusionsOur method, which involved the use of a set of MeSH terms related to disease candidate genes and a novel weighted literature score, improved the accuracy of gathering and prioritizing candidate genes by focusing on a specific disease.

Automated Recommendation of Research Keywords from PubMed That Suggest the Molecular Mechanism Associated with Biomarker Metabolites

“…MeSH terms have been used to develop informatic methods (i.e., semantic similarity, MeSH-Gram [8,9]), to visualize research trends (i.e., hierarchical structure, MeSH Sim [10]), and to estimate relationships among terms (i.e., establishment of disease-related MeSH terms [11]). Moreover, methods have been reported for suggesting keyword-based topics for unseen biomedical research articles from PubMed [12] and for annotation of scientific data with keywords from a controlled vocabulary [13].…”

Discovery of Gene-disease Associations from Biomedical Texts

“…Some researchers compute the similarity values between genes and diseases based on Gene Ontology (GO) [5] or Disease Ontology (DO) [6] terms [7,8,9]. Other controlled vocabularies such as MeSH [10] have already been utilized for linking proteins to disease terminologies [11,12,13]. Some other information like gene expressions [14,15,16], protein/genome sequences [17, 18.…”