Finding Possible Promoter Binding Sites in DNA Sequences by Sequential Patterns Mining With Specific Numbers of Gaps.

Identifying motifs in promoter regions is crucial to our understanding of transcription regulation. Researchers commonly use known promoter features in a variety of species to predict promoter motifs. However the results are not particularly useful. Different species rarely have similar features in promoter binding sites. In this study, we adopt sequence analysis techniques to find the possible promoter binding sites among different species. We sought to improve the existing algorithm to suit the task of mining sequential patterns with specific number of gaps. Moreover, we discuss the implementation of proposed method in a distributed environment. The proposed method finds the transcription start sites (TSS) and extracts possible promoter regions from DNA sequences according to TSS. We derived the motifs in the possible promoter regions, while taking into account the number of gaps in the motifs to deal with unimportant nucleotides. The motifs generated from promoter regions using the proposed methodology were shown to tolerate unimportant nucleotides. A comparison with known promoter motifs verified the efficacy of the proposed method.

TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams.

Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system.