Baw-Jhiune Liu

A Robot as a Teaching Assistant in an English Class

Advancement in robotic research enables robot can assist human in many way. However, few researches have been done on applying on education. This paper reports field trials of using robot in an English learning classroom. In our experiment, the robot plays as a partner of a teacher. Five models of collaboration between teachers and robot are proposed. Teachers design the course flow in advance based on the learning content and these five models. Formative evaluations of the experiments are presented to show the impact of adopting robot in the classroom.

Materialized view selection using genetic algorithms in a data warehouse system

A data warehouse stores lots of materialized views to provide efficient decision-support or OLAP queries. The view-selection problem addresses the selection of a fittest set of materialized views under the limitation of storage space forms a challenge in data warehouse research. In this paper, we present genetic algorithms to choose materialized views. We also use experiments to demonstrate the power of our approach.

Identification of hot regions in protein-protein interactions by sequential pattern mining

BackgroundIdentification of protein interacting sites is an important task in computational molecular biology. As more and more protein sequences are deposited without available structural information, it is strongly desirable to predict protein binding regions by their sequences alone. This paper presents a pattern mining approach to tackle this problem. It is observed that a functional region of protein structures usually consists of several peptide segments linked with large wildcard regions. Thus, the proposed mining technology considers large irregular gaps when growing patterns, in order to find the residues that are simultaneously conserved but largely separated on the sequences. A derived pattern is called a cluster-like pattern since the discovered conserved residues are always grouped into several blocks, which each corresponds to a local conserved region on the protein sequence.ResultsThe experiments conducted in this work demonstrate that the derived long patterns automatically discover the important residues that form one or several hot regions of protein-protein interactions. The methodology is evaluated by conducting experiments on the web server MAGIIC-PRO based on a well known benchmark containing 220 protein chains from 72 distinct complexes. Among the tested 218 proteins, there are 900 sequential blocks discovered, 4.25 blocks per protein chain on average. About 92% of the derived blocks are observed to be clustered in space with at least one of the other blocks, and about 66% of the blocks are found to be near the interface of protein-protein interactions. It is summarized that for about 83% of the tested proteins, at least two interacting blocks can be discovered by this approach.ConclusionThis work aims to demonstrate that the important residues associated with the interface of protein-protein interactions may be automatically discovered by sequential pattern mining. The detected regions possess high conservation and thus are considered as the computational hot regions. This information would be useful to characterizing protein sequences, predicting protein function, finding potential partners, and facilitating protein docking for drug discovery.

ProSplicer: a database of putative alternative splicing information derived from protein, mRNA and expressed sequence tag sequence data

ProSplicer is a database of putative alternative splicing information derived from the alignment of proteins, mRNA sequences and expressed sequence tags (ESTs) against human genomic DNA sequences. Proteins, mRNA and ESTs provide valuable evidence that can reveal splice variants of genes. The alternative splicing information in the database can help users investigate the alternative splicing and tissue-specific expression of genes.

Applying evolutionary algorithms to materialized view selection in a data warehouse

AbstractEffective analysis of genome sequences and associated functional data requires access to many different kinds of biological information. A data warehouse [14,16] plays an important role for storage and analysis for genome sequence and functional data. A data warehouse stores lots of materialized views to provide an efficient decision-support or OLAP queries. The view-selection problem addresses to select a fittest set of materialized views from a variety of MVPPs 0 forms a challenge in data warehouse research. In this paper, we present genetic algorithm to choose materialized views. We also use experiments to demonstrate the power of our approach.

MAGIIC-PRO: detecting functional signatures by efficient discovery of long patterns in protein sequences

This paper presents a web service named MAGIIC-PRO, which aims to discover functional signatures of a query protein by sequential pattern mining. Automatic discovery of patterns from unaligned biological sequences is an important problem in molecular biology. MAGIIC-PRO is different from several previously established methods performing similar tasks in two major ways. The first remarkable feature of MAGIIC-PRO is its efficiency in delivering long patterns. With incorporating a new type of gap constraints and some of the state-of-the-art data mining techniques, MAGIIC-PRO usually identifies satisfied patterns within an acceptable response time. The efficiency of MAGIIC-PRO enables the users to quickly discover functional signatures of which the residues are not from only one region of the protein sequences or are only conserved in few members of a protein family. The second remarkable feature of MAGIIC-PRO is its effort in refining the mining results. Considering large flexible gaps improves the completeness of the derived functional signatures. The users can be directly guided to the patterns with as many blocks as that are conserved simultaneously. In this paper, we show by experiments that MAGIIC-PRO is efficient and effective in identifying ligand-binding sites and hot regions in protein–protein interactions directly from sequences. The web service is available at and a mirror site at .

Improving Mathematics Teaching and Learning Experiences for Hard of Hearing Students With Wireless Technology-Enhanced Classrooms

Hard of hearing students usually face more difficulties at school than other students. A classroom environment with wireless technology was implemented to explore whether wireless technology could enhance mathematics learning and teaching activities for a hearing teacher and her 7 hard of hearing students in a Taiwan junior high school. Experiments showed that the highly interactive communication through the wireless network increased student participation in learning activities. Students demonstrated more responses to the teacher and fewer distraction behaviors. Fewer mistakes were made in in-class course work because Tablet PCs provided students scaffolds. Students stated that the environment with wireless technology was desirable and said that they hoped to continue using the environment to learn mathematics.

Database of repetitive elements in complete genomes and data mining using transcription factor binding sites

Approximately 43% of the human genome is occupied by repetitive elements. Even more, around 51% of the rice genome is occupied by repetitive elements. The analysis presented here indicates that repetitive elements in complete genomes may have been very important in the evolutionary genomics. In this study, a database, called the Repeat Sequence Database, is first designed and implemented to store complete and comprehensive repetitive sequences. See http://rsdb.csie.ncu.edu.tw for more information. The database contains direct, inverted and palindromic repetitive sequences, and each repetitive sequence has a variable length ranging from seven to many hundred nucleotides. The repetitive sequences in the database are explored using a mathematical algorithm to mine rules on how combinations of individual binding sites are distributed among repetitive sequences in the database. Combinations of transcription factor binding sites in the repetitive sequences are obtained and then data mining techniques are applied to mine association rules from these combinations. The discovered associations are further pruned to remove insignificant associations and obtain a set of associations. The mined association rules facilitate efforts to identify gene classes regulated by similar mechanisms and accurately predict regulatory elements. Experiments are performed on several genomes including C. elegans, human chromosome 22, and yeast.

Efficient discovery of structural motifs from protein sequences with combination of flexible intra- and inter-block gap constraints

Discovering protein structural signatures directly from their primary information is a challenging task, because the residues associated with a functional motif are not necessarily clustered in one region of the sequence. This work proposes an algorithm that aims to discover conserved sequential blocks interleaved by large irregular gaps from a set of unaligned biological sequences. Different from the previous works that employ only one type of constraint on gap flexibility, we propose using combination of intra- and inter-block gap constraints to discover longer patterns with larger irregular gaps. The smaller flexible intra-block gap constraint is used to relax the restriction in local motif blocks but still keep them compact, and the larger flexible inter-block gap constraint is proposed to allow longer irregular gaps between compact motif blocks. Using two types of gap constraints for different purposes improves the efficiency of mining process while keeping high accuracy of mining results. The efficiency of the algorithm also helps to identify functional motifs that are conserved in only a small subset of the input sequences.

WildSpan: mining structured motifs from protein sequences.

BackgroundAutomatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards) are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost.ResultsWildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode of WildSpan is developed for discovering functional regions of a single protein by referring to a set of related sequences (e.g. its homologues). The discovered W-patterns are used to characterize the protein sequence and the results are compared with the conserved positions identified by multiple sequence alignment (MSA). The family-based mining mode of WildSpan is developed for extracting sequence signatures for a group of related proteins (e.g. a protein family) for protein function classification. In this situation, the discovered W-patterns are compared with PROSITE patterns as well as the patterns generated by three existing methods performing the similar task. Finally, analysis on execution time of running WildSpan reveals that the proposed pruning strategy is effective in improving the scalability of the proposed algorithm.ConclusionsThe mining results conducted in this study reveal that WildSpan is efficient and effective in discovering functional signatures of proteins directly from sequences. The proposed pruning strategy is effective in improving the scalability of WildSpan. It is demonstrated in this study that the W-patterns discovered by WildSpan provides useful information in characterizing protein sequences. The WildSpan executable and open source codes are available on the web (http://biominer.csie.cyu.edu.tw/wildspan).