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Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems due to large search spaces. This paper proposes a three-stage method for multi-domain dialogue policy learning-termed NDQN, and applies it to an information-seeking spoken dialogue system in the domains of restaurants and hotels. In this method, the first stage does multi-policy...
MicroRNAs (miRNAs) regulate the function of their target genes by down-regulating gene expression, participating in various biological processes. Since the discovery of the first miRNA, computational tools have been essential to predict targets of given miRNAs that can be biologically verified. The precise mechanism underlying miRNA–mRNA interaction has not yet been elucidated completely, and it is...
Understanding the regulation of gene expression is one of the major research problems in biology. An important part of this problem is to identify the binding sites in DNA for transcription factors. These binding sites are called motifs. The performance of de novo motif discovery algorithms is limited. To address the issue, ensemble methods have been proposed in order to enhance the prediction accuracy...
Different microRNA target prediction tools produce different results. Motivated by this fact, here we present an ensemble-learning approach that combines the outcomes from multiple tools to reduce prediction error. We test this approach with a dataset derived from a public database containing human microRNAs and microRNA-mRNA pairs. According to our experimental result, using the proposed method tends...
The knowledge of protein functions as well as structures is critical for drug discovery and development. The FEATURE system developed at Stanford is an effective tool for characterizing and classifying local environments in proteins. FEATURE utilizes vectors of a fixed dimension to represent the physicochemical properties around a residue. Functional sites and non-sites are identified by classifying...
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