BackgroundMicroRNAs (miRNAs) are small non-coding RNA molecules that are ~22-nt-long sequences capable of suppressing protein synthesis. Previous research has suggested that miRNAs regulate 30% or more of the human protein-coding genes. The aim of this work is to consider various analyzing scenarios in the identification of miRNA-target interactions, as well as to provide an integrated system that will aid in facilitating investigation on the influence of miRNA targets by alternative splicing and the biological function of miRNAs in biological pathways.ResultsThis work presents an integrated system, miRTar, which adopts various analyzing scenarios to identify putative miRNA target sites of the gene transcripts and elucidates the biological functions of miRNAs toward their targets in biological pathways. The system has three major features. First, the prediction system is able to consider various analyzing scenarios (1 miRNA:1 gene, 1:N, N:1, N:M, all miRNAs:N genes, and N miRNAs: genes involved in a pathway) to easily identify the regulatory relationships between interesting miRNAs and their targets, in 3'UTR, 5'UTR and coding regions. Second, miRTar can analyze and highlight a group of miRNA-regulated genes that participate in particular KEGG pathways to elucidate the biological roles of miRNAs in biological pathways. Third, miRTar can provide further information for elucidating the miRNA regulation, i.e., miRNA-target interactions, affected by alternative splicing.ConclusionsIn this work, we developed an integrated resource, miRTar, to enable biologists to easily identify the biological functions and regulatory relationships between a group of known/putative miRNAs and protein coding genes. miRTar is now available at http://miRTar.mbc.nctu.edu.tw/.

Ao meu orientador Prof. Dr. Luiz Antonio Gioielli, pela confiança, dedicação, amizade e principalmente por ter me ensinado o que é pesquisa e sua importância.

Millions of people now participate in on line games, placing tremendous and often unpredictable maintenance burdens on their operators. Thus, understanding the dynamic behaviors of a player is critical for the systems, network, and designers. To the best of our knowledge, little work builds character interaction model based on the data stream mining. This work improves our understanding the behaviors of avatar/player in a on line game by collecting the behavior data, extracting frequent behavior patterns, learning the hidden hints and making good prediction on responses to the unexpected impacts. Besides, we develop two efficient approaches for mining the behavior data to find the interesting behavior pattern for future prediction on responses of opponents. Our novel findings include the following: One, due to the constraints of limited resources of time, memory, and sample size, MSS-MB was proposed to meet these conditions; the other, due to the constraints of real-time and on-line, there may have some errors occurred in the processing period, MSS-BE was proposed to control the errors as needed. Finally, based on the experimental results, we can predict the responses of opponents efficiently in the on line game.

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