Tree-based Data Mining Research Articles

COPS: Detecting Co-Occurrence and Spatial Arrangement of Transcription Factor Binding Motifs in Genome-Wide Datasets

In multi-cellular organisms, spatiotemporal activity of cis-regulatory DNA elements depends on their occupancy by different transcription factors (TFs). In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with cis-regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficient for dissecting complex regulatory networks. Thus, potent computational tools detecting statistically significant and biologically relevant TF-motif co-occurrences in genome-wide datasets are essential for analyzing context-dependent transcriptional regulation. We have developed COPS (Co-Occurrence Pattern Search), a new bioinformatics tool based on a combination of association rules and Markov chain models, which detects co-occurring TF binding sites (BSs) on genomic regions of interest. COPS scans DNA sequences for frequent motif patterns using a Frequent-Pattern tree based data mining approach, which allows efficient performance of the software with respect to both data structure and implementation speed, in particular when mining large datasets. Since transcriptional gene regulation very often relies on the formation of regulatory protein complexes mediated by closely adjoining TF binding sites on CRMs, COPS additionally detects preferred short distance between co-occurring TF motifs. The performance of our software with respect to biological significance was evaluated using three published datasets containing genomic regions that are independently bound by several TFs involved in a defined biological process. In sum, COPS is a fast, efficient and user-friendly tool mining statistically and biologically significant TFBS co-occurrences and therefore allows the identification of TFs that combinatorially regulate gene expression.

Using innovative technology to establish sizing systems

Among textile manufacturing industries, garment manufacturing produces products with the highest added value. Because certain standards and specifications must be followed in large-scale production, each country must have its own standard-sizing systems for manufacturers to follow. Data mining is used in many fields but there is a lack of research on sizing systems in garment manufacturing. This study aims to establish such sizing systems using a decision tree-based data mining approach. When sizing systems are established using the innovative technology, three advantages can be seen: resulting in fewer size groups with high coverage; generating regular patterns and rules; providing manufacturers with reference points to facilitate manufacturing. As a result, production planning can be made more realistic and inventory costs due to mismatches can be minimised. The innovative technology is found to be effective in processing classification problems for promoting production planning and management.

Using decision tree-based data mining to establish a sizing system for the manufacture of garments

While data mining has been widely used in many fields, little research has been done on its applications to sizing systems for the manufacturing of garments. The goal of this study was to establish systems for using a decision tree technique to determine the pants sizes of army soldiers. We first defined the subject and constructed a large anthropometric database. Second, we prepared and analyzed data, performed factor analyses, and then extracted important sizing variables. Third, we used the decision tree technique to mine data in an effort to identify and classify significant patterns in the body shapes of soldiers. The use of decision tree-based data mining to establish sizing systems is advantageous because it can (1) allow for a wider coverage of body shapes with a fewer number of sizes, (2) generate regular sizing patterns and rules, and (3) provide manufacturers with reference points to facilitate production. The newly developed sizing system can provide garment manufacturers with size specifications, design development, pattern grading, and market analysis. Moreover, when production plans can be made more realistic, inventory costs due to mismatches can be minimized.