Human

Alzheimer's disease (AD) is an incurable neurodegenerative disorder. Much effort has been devoted to developing effective therapeutic agents. Recently, targeting microRNAs (miRNAs) with small molecules has become a novel therapy for human diseases. In this study, we present a systematic computational approach to construct a bioactive Small molecule and miRNA association Network in AD (SmiRN-AD), which is based on the gene expression signatures of bioactive small molecule perturbation and AD-related miRNA regulation.

Microarray technology is a widely used approach to gene expression analysis. Many tools for microarray management and data analysis have been developed, and recently new methods have been proposed for deciphering biological pathways by integrating microarray data with other data sources. However, to improve microarray analysis and provide meaningful gene interaction networks, integrated software solutions are still needed. Therefore, we developed M@IA, an environment for DNA microarray data analysis allowing gene network reconstruction.

Small non-coding RNAs play a key role in many physiological and pathological processes. Since 2004, miRNA sequences have been catalogued in miRBase, which is currently in its 21st version. We investigated sequence and structural features of miRNAs annotated in the miRBase and compared them between different versions of this reference database.

Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists.