Active

Colorectal cancer (CRC) is a cancer of growing incidence that associates with a high mortality rate worldwide. There is a poor understanding of the heterogeneity of CRC with regard to causative genetic mutations and gene regulatory mechanisms. Previous studies have identified several susceptibility genes in small-scale experiments. However, the information has not been comprehensively and systematically compiled and interpreted. In this study, we constructed the gbCRC, the first literature-based gene resource for investigating CRC-related human genes.

Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text.

MicroRNAs carry out post-transcriptional gene regulation in animals by binding to the 3' untranslated regions of mRNAs, causing their degradation or translational repression. MicroRNAs influence many biological functions, and dysregulation can therefore disrupt development or even cause death. High-throughput sequencing and the mining of animal small RNA data has shown that microRNA genes can yield differentially expressed isoforms, known as isomiRs.

Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases.