Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning

The increasing amount of scientific literature in biological and biomedical science research has created a challenge in the continuous and reliable curation of the latest knowledge discovered, and automatic biomedical text-mining has been one of the answers to this chal-lenge. In this paper, we aim to further improve the reliability of biomedical text-mining by training the system to directly simulate the human behaviors such as querying the PubMed, selecting articles from queried results, and reading selected articles for knowledge. We take advantage of the efficiency of biomedical text-mining, the flexibility of deep reinforcement learning, and the massive amount of knowledge collected in UMLS into an integrative arti-ficial intelligent reader that can automatically identify the authentic articles and effectively acquire the knowledge conveyed in the articles. We construct a system, whose current pri-mary task is to build the genetic association database between genes and complex traits of the human. Our contributions in this paper are three-fold: 1) We propose to improve the reliability of text-mining by building a system that can directly simulate the behavior of a researcher, and we develop corresponding methods, such as Bi-directional LSTM for text mining and Deep Q-Network for organizing behaviors. 2) We demonstrate the effec-tiveness of our system with an example in constructing a genetic association database. 3) We release our implementation as a generic framework for researchers in the community to conveniently construct other databases.

Clinic-Genomic Association Mining for Colorectal Cancer Using Publicly Available Datasets

In recent years, a growing number of researchers began to focus on how to establish associations between clinical and genomic data. However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease. Colorectal cancer is one of the malignant tumours. A number of genetic syndromes have been proven to be associated with colorectal cancer. This paper presents our research on mining clinic-genomic associations for colorectal cancer under biomedical big data environment. The proposed method is engineered with multiple technologies, including extracting clinical concepts using the unified medical language system (UMLS), extracting genes through the literature mining, and mining clinic-genomic associations through statistical analysis. We applied this method to datasets extracted from both gene expression omnibus (GEO) and genetic association database (GAD). A total of 23517 clinic-genomic associations between 139 clinical concepts and 7914 genes were obtained, of which 3474 associations between 31 clinical concepts and 1689 genes were identified as highly reliable ones. Evaluation and interpretation were performed using UMLS, KEGG, and Gephi, and potential new discoveries were explored. The proposed method is effective in mining valuable knowledge from available biomedical big data and achieves a good performance in bridging clinical data with genomic data for colorectal cancer.

Genetic associations with asthma and virus-induced wheezing: a systematic review

Various wheezing phenotypes can be identified based on differences in natural histories, risk factors and responses to treatment. In epidemiologic studies, atopic asthma or virus-induced wheezing can be discriminated by the presence or the absence of sensitization to allergens. Children with asthma have been shown to present lower levels of lung function. Patients with viral respiratory infections evolve from normal lung function to enhanced airway reactivity. The objective of this study was to identify genes and polymorphisms associated with different wheezing phenotypes. Using data obtained from the Genetic Association Database, we systematically reviewed studies on genes and polymorphisms that have been associated with virus-induced wheezing or atopic asthma. The research was carried out in February of 2009. Genes associated with the studied outcomes in more than three studies were included in the analysis. We found that different genes and loci have been associated with virus-induced wheezing or atopic asthma. Virus-induced wheezing has frequently been associated with IL-8 polymorphisms, whereas atopic asthma and atopy have frequently been associated with Th2 cytokine gene (CD14 and IL-13) polymorphisms on chromosome 5. This review provides evidence that different wheezing disorders in childhood can be differently affected by genetic variations, considering their role on airway inflammation and atopy. Future studies of genetic associations should consider the different wheezing phenotypes in infancy. In addition, stratified analyses for atopy can be useful for elucidating the mechanisms of the disease.