Research On Clinic-Genomic Relation Mining And Knowledge Base Establishment For Colorectal Cancer

With the development of medical informatics and molecular biology, vast amounts of biomedical data have been accumulated. These data covered multiple levels, including both clinical data in macro aspect and genomic data in microcosmic aspect. However, due to the lack of effective linkages among data in different levels, the fruits of basic research have not been translated into clinical practice completely, and problems arising in clinical practice also have not made big difference to the basic research directions. Exploited value of available biomedical data is far less than the intrinsic value of these data. How to bridge clinical data with omics data, mining the potential knowledge of available data, and thus promoting the bidirectional translation between clinical research and basic research, are new scientific issues faced by biomedical informatics.In recent years, a growing number of researchers began to focus on how to establish relation between clinical data and omics data. But up to now, there is no research mining clinic-genomic relations by comprehensively analyzing available gene expression data for a single disease. Therefore, around colorectal cancer and aiming at facilitating the diagnosis and treatment of colorectal cancer, this paper research on the method of mining clinic-genomic relations and establishing corresponding knowledge base for colorectal cancer.This paper proposed a clinic-genomic relation mining method for colorectal cancer, which consists of three parts:UMLS-based clinical concept extraction method, Statistical-analysis-based clinic-genomic relation mining method and Literature-mining-based gene extraction method, of which the Statistical-analysis-based clinic-genomic relation mining method is the key part of this paper. After acquired the clinic-genomic knowledge of colorectal cancer, this paper proposed analyzing clinical knowledge using UMLS semantic type, analyzing genomic knowledge using KEGG pathway and analyzing clinic-genomic relations using Gephi for evaluation and interpretation of knowledge acquisition results. In total, this paper gained665colorectal cancer related clinical concepts,8393colorectal cancer related genes and23517clinic-genomic relations. During the process of knowledge analysis, several new discoveries were found, such as colorectal cancer related disease (osteoporosis), related symptoms (angina pectoris) and so on. To effectively organize these various results, this paper established a flexible and scalable knowledge base to store them. Finally, a clinic-genomic knowledge sharing platform was developed based on the clinic-genomic knowledge base. In addition to be used for knowledge query and classification-based knowledge browsing, this platform can also be used for clinic-genomic relation re-analysis in an interactive way. Upon the current status of biomedical informatics study, this paper proposed a clinic-genomic relation mining method and further built a knowledge base for colorectal cancer, a typical cancer, and also provided a representative way for promoting clinical research and basic research simultaneously.