Jorng-Tzong Horng

KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites

KinasePhos is a novel web server for computationally identifying catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the profile hidden Markov model, computational models are learned from the kinase-specific groups of the phosphorylation sites. After evaluating the learned models, the model with highest accuracy was selected from each kinase-specific group, for use in a web-based prediction tool for identifying protein phosphorylation sites. Therefore, this work developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. The prediction tool is freely available at .

Applying genetic algorithms to query optimization in document retrieval

Abstract This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. One of the contributions of the paper is to combine the Bigram (Chen, A., He, J., Xu, L., Gey, F. C., & Meggs, J. 1997. Chinese text retrieval without using a dictionary , ACM SIGIR’97, Philadelphia, PA, USA, pp. 42–49; Yang, Y.-Y., Chang, J.-S., & Chen, K.-J. 1993), Document automatic classification and ranking , Master thesis, Department of Computer Science, National Tsing Hua University) model and PAT-tree structure (Chien, L.-F., Huang, T.-I., & Chien, M.-C. 1997 Pat-tree-based keyword extraction for Chinese information retrieval , ACM SIGIR’97, Philadelphia, PA, US, pp. 50–59) to retrieve keywords. The approach extracts bigrams from documents and uses the bigrams to construct a PAT-tree to retrieve keywords. The proposed approach can retrieve any type of keywords such as technical keywords and a person’s name. Effectiveness of the proposed approach is demonstrated by comparing how effective are the keywords found by both this approach and the PAT-tree based approach. This comparison reveals that our keyword retrieval approach is as accurate as the PAT-tree based approach, yet our approach is faster and uses less memory. The study then applies genetic algorithms to tune the weight of retrieved keywords. Moreover, several documents obtained from web sites are tested and experimental results are compared with those of other approaches, indicating that the proposed approach is highly promising for applications.

Personal paging area design based on mobile's moving behaviors

We propose a new location tracking strategy called behavior-based strategy (BBS) based on each mobiles moving behavior. With the help of data mining technologies the moving behavior of each mobile could be mined from long-term collection of the mobiles moving logs. From the moving behavior of each mobile, we first estimate the time-varying probability of the mobile and then the optimal paging area of each time region is derived. To reduce unnecessary computation, we consider the location tracking and computational cost and then derive a cost model. A heuristics is proposed to minimize the cost model through finding the appropriate moving period checkpoints of each mobile. The experimental results show our strategy outperforms fixed paging area strategy currently used in the GSM system and time-based strategy for highly regular moving mobiles.

Genetic-based search for error-correcting graph isomorphism

Error-correcting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting graph isomorphism. By applying genetic algorithms, some local search strategies are amalgamated to improve convergence speed. Besides, a selection operator is proposed to prevent premature convergence. The proposed approach has been implemented to verify its validity. Experimental results reveal the superiority of this new technique than several other well-known algorithms.

Applying Family Competition to Evolution Strategies for Constrained Optimization

This paper applies family competition to evolution strategies to solve constrained optimization problems. The family competition of Family Competition Evolution Strategy (FCES) can be viewed as a local competition involving the children generated from the same parent, while the selection is a global competition among all of the members in the population. According to our experimental results, the self-adaptation of strategy parameters with deterministic elitist selection may trap ESs into local optima when they are applied to heavy constrained optimization problems. By controlling strategy parameters with non-self adaptive rule, FCES can reduce the computation time of self-adaptive Gaussian mutation, diminish the complexity of selection from (m+1) to (m+m), and avoid to be premature. Therefore, FCES is capable of obtaining better performance and saving the computation time. In this paper, FCES is compared with other evolutionary algorithms on various benchmark problems and the results indicate that FCES is a powerful optimization technique.

PGTdb: a database providing growth temperatures of prokaryotes

UNLABELLED Included in Prokaryotic Growth Temperature database (PGTdb) are a total of 1334 temperature data from 1072 prokaryotic organisms, Bacteria and Archaea: PGTdb integrates microbial growth temperature data from literature survey with their nucleotide/protein sequence and protein structure data from related databases. A direct correlation is observed between the average growth temperature of an organism and the melting temperature of proteins from the organism. Therefore, this database is useful not only for microbiologists to obtain cultivation condition, but also for biochemists and structure biologists to study the correlation between protein sequences/structures and their thermostability. In addition, the taxonomy and ribosomal RNA sequence(s) of an organism are linked through NCBI Taxonomy and the Ribosomal RNA Operon Copy Number Database umdb, respectively. PGTdb is the only integrated database on the Internet to provide the growth temperature data of the prokaryotes and the combined information of their nucleotide/protein sequences, protein structures, taxonomy and phylogeny. AVAILABILITY http://pgtdb.csie.ncu.edu.tw

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.

Incorporating hidden markov models for identifying protein kinase-specific phosphorylation sites

Protein phosphorylation, which is an important mechanism in posttranslational modification, affects essential cellular processes such as metabolism, cell signaling, differentiation, and membrane transportation. Proteins are phosphorylated by a variety of protein kinases. In this investigation, we develop a novel tool to computationally predict catalytic kinase‐specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the concepts of profile Hidden Markov Models (HMM), computational models are trained from the kinase‐specific groups of phosphorylation sites. After evaluating the trained models, we select the model with highest accuracy in each kinase‐specific group and provide a Web‐based prediction tool for identifying protein phosphorylation sites. The main contribution here is that we have developed a kinase‐specific phosphorylation site prediction tool with both high sensitivity and specificity.

On solving rectangle bin packing problems using genetic algorithms

This paper presents an application of genetic algorithms in solving rectangle bin packing problems which belong to the class of NP-hard optimization problems. There are three versions of rectangle bin packing problems to be discussed in this paper: the first version is to minimize the packing area, the second version is to minimize the height of a strip packing, and the final version is to minimize the number of bins used to pack the given items. Different versions of genetic algorithms are developed to solve the three versions of problems. Among these versions of genetic algorithms, we have demonstrated two ways of applying the genetic algorithms, either to solve the problem directly or to tune an existing, heuristic algorithm so that the performance is improved, Experimental results are compared to well-known packing heuristics FFDH and HFF. From these results, we know that both methods can be useful in practice.<<ETX>>

An expert system to classify microarray gene expression data using gene selection by decision tree

Gene selection can help the analysis of microarray gene expression data. However, it is very difficult to obtain a satisfactory classification result by machine learning techniques because of both the curse-of-dimensionality problem and the over-fitting problem. That is, the dimensions of the features are too large but the samples are too few. In this study, we designed an approach that attempts to avoid these two problems and then used it to select a small set of significant biomarker genes for diagnosis. Finally, we attempted to use these markers for the classification of cancer. This approach was tested the approach on a number of microarray datasets in order to demonstrate that it performs well and is both useful and reliable.