MOTIVATION: High-accuracy de novo assembly of the short sequencing reads from RNA-Seq technology is very challenging. We introduce a de novo assembly algorithm, EBARDenovo, which stands for Extension, Bridging And Repeat-sensing Denovo. This algorithm uses an efficient chimera-detection function to abrogate the effect of aberrant chimeric reads in RNA-Seq data. RESULTS: EBARDenovo resolves the complications of RNA-Seq assembly arising from sequencing errors, repetitive sequences and aberrant chimeric amplicons. In a series of assembly experiments, our algorithm is the most accurate among the examined programs, including de Bruijn graph assemblers, Trinity and Oases. AVAILABILITY AND IMPLEMENTATION: EBARDenovo is available at http://ebardenovo.sourceforge.net/. This software package (with patent pending) is free of charge for academic use only. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multiclass classification is an important technique to many complex biomedicine problems. Genetic algorithms (GA) are proven to be effective to select features prior to multiclass classification by support vector machines (SVM). However, their use is computation intensive. Based on SOA (Service Oriented Architecture) design principles, this paper proposes a cloud computing framework that exploits the inherent parallelism of GA-SVM classification to speed up the work. The performance evaluations on an mRNA benchmark cancer dataset have shown the effectiveness and efficiency of the framework. With a user-friendly web interface, the framework provides researchers an easy way to investigate the unrevealed secrets in the fast-growing repository of biomedical data.

Metagenomics enables the study of unculturable microorganisms in different environments directly. Discriminating between the compositional differences of metagenomes is an important and challenging problem. Several distance functions have been proposed to estimate the differences based on functional profiles or taxonomic distributions; however, the strengths and limitations of such functions are still unclear. Initially, we analyzed three well-known distance functions and found very little difference between them in the clustering of samples. This motivated us to incorporate suitable normalizations and phylogenetic information into the functions so that we could cluster samples from both real and synthetic data sets. The results indicate significant improvement in sample clustering over that derived by rank-based normalization with phylogenetic information, regardless of whether the samples are from real or synthetic microbiomes. Furthermore, our findings suggest that considering suitable normalizations and phylogenetic information is essential when designing distance functions for estimating the differences between metagenomes. We conclude that incorporating rank-based normalization with phylogenetic information into the distance functions helps achieve reliable clustering results.

SUMMARY: MetaABC is a metagenomic platform that integrates several binning tools coupled with methods for removing artifacts, analyzing unassigned reads and controlling sampling biases. It allows users to arrive at a better interpretation via series of distinct combinations of analysis tools. After execution, MetaABC provides outputs in various visual formats such as tables, pie and bar charts as well as clustering result diagrams. AVAILABILITY: MetaABC source code and documentation are available at http://bits2.iis.sinica.edu.tw/MetaABC/ CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed. RESULTS: In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes. CONCLUSION: In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).

BACKGROUND: Schizophrenia, bipolar disorder, and major depression are devastating mental diseases, each with distinctive yet overlapping epidemiologic characteristics. Microarray and proteomics data have revealed genes which expressed abnormally in patients. Several single nucleotide polymorphisms (SNPs) and mutations are associated with one or more of the three diseases. Nevertheless, there are few studies on the interactions among the disease-associated genes and proteins. RESULTS: This study, for the first time, incorporated microarray and protein-protein interaction (PPI) databases to construct the PPI network of abnormally expressed genes in postmortem brain samples of schizophrenia, bipolar disorder, and major depression patients. The samples were collected from Brodmann area (BA) 10 of the prefrontal cortex. Abnormally expressed disease genes were selected by t-tests comparing the disease and control samples. These genes were involved in housekeeping functions (e.g. translation, transcription, energy conversion, and metabolism), in brain specific functions (e.g. signal transduction, neuron cell differentiation, and cytoskeleton), or in stress responses (e.g. heat shocks and biotic stress).The diseases were interconnected through several "switchboard"-like nodes in the PPI network or shared abnormally expressed genes. A "core" functional module which consisted of a tightly knitted sub-network of clique-5 and -4s was also observed. These cliques were formed by 12 genes highly expressed in both disease and control samples. CONCLUSIONS: Several previously unidentified disease marker genes and drug targets, such as SBNO2 (schizophrenia), SEC24C (bipolar disorder), and SRRT (major depression), were identified based on statistical and topological analyses of the PPI network. The shared or interconnecting marker genes may explain the shared symptoms of the studied diseases. Furthermore, the "switchboard" genes, such as APP, UBC, and YWHAZ, are proposed as potential targets for developing new treatments due to their functional and topological significance.

Mathematical models in systems biology can use quantitative techniques to study the integrated behaviors of biological systems in macro level. Agent-based modeling provides a qualitative framework, focusing on how each individual molecule behaves in micro level. We are motivated to combine their features together to describe the gene regulatory networks, in particular the emergence of macro phenomena from micro interactions. An agent-based model will be well defined as general as possible, including of many agents of various molecular species interacting in it. The mathematical premises, which are grounded in the micro behaviors of agents, can derive the chemical master equations and the stochastic differential equations for modeling the integrated behaviors of systems in macro level. Combing agent-based models with stochastic differential equations affords a more complete perspective on gent regulatory networks. In addition, the sources and magnitudes of deterministic dynamics and random noises are explicitly characterized.

BACKGROUND: Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools. RESULTS: We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles. CONCLUSION: The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at (http://poinet.bioinformatics.tw/).

BACKGROUND: DNA-binding proteins are of utmost importance to gene regulation. The identification of DNA-binding domains is useful for understanding the regulation mechanisms of DNA-binding proteins. In this study, we proposed a method to determine whether a domain or a protein can has DNA binding capability by considering evolutionary conservation of DNA-binding residues. RESULTS: Our method achieves high precision and recall for 66 families of DNA-binding domains, with a false positive rate less than 5% for 250 non-DNA-binding proteins. In addition, experimental results show that our method is able to identify the different DNA-binding behaviors of proteins in the same SCOP family based on the use of evolutionary conservation of DNA-contact residues. CONCLUSION: This study shows the conservation of DNA-contact residues in DNA-binding domains. We conclude that the members in the same subfamily bind DNA specifically and the members in different subfamilies often recognize different DNA targets. Additionally, we observe the co-evolution of DNA-contact residues and interacting DNA base-pairs.

BACKGROUND: The rapid growth of protein-protein interaction (PPI) data has led to the emergence of PPI network analysis. Despite advances in high-throughput techniques, the interactomes of several model organisms are still far from complete. Therefore, it is desirable to expand these interactomes with ortholog-based and other methods. RESULTS: Orthologous pairs of 18 eukaryotic species were expanded and merged with experimental PPI datasets. The contributions of interologs from each species were evaluated. The expanded orthologous pairs enable the inference of interologs for various species. For example, more than 32,000 human interactions can be predicted. The same dataset has also been applied to the prediction of host-pathogen interactions. PPIs between P. falciparum calmodulin and several H. sapiens proteins are predicted, and these interactions may contribute to the maintenance of host cell Ca2+ concentration. Using comparisons with Bayesian and structure-based approaches, interactions between putative HSP40 homologs of P. falciparum and the H. sapiens TNF receptor associated factor family are revealed, suggesting a role for these interactions in the interference of the human immune response to P. falciparum. CONCLUSION: The PPI datasets are available from POINT http://point.bioinformatics.tw/ and POINeT http://poinet.bioinformatics.tw/. Further development of methods to predict host-pathogen interactions should incorporate multiple approaches in order to improve sensitivity, and should facilitate the identification of targets for drug discovery and design.

This paper formally presents the resident physician scheduling problem, which is one of the most important scheduling problems in hospital. The resident physician scheduling problem is characterized as satisfying the fair schedule constraint, the physician specification constraint and the safe schedule constraint simultaneously. To minimize the penalties from violating the constraints, this study adopts the evolutionary approach to propose a genetic algorithm for solving the problems. In addition the well-known genetic operators, this study proposed a new mutation operator called dynamic mutation for solving the resident physician scheduling problem. The experimental results show that the proposed algorithm performs well in searching optimal schedules. Categories and Subject Descriptors

BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO http://ehco.iis.sinica.edu.tw, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.

BACKGROUND: The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog). This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction. RESULTS: This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms--rat, mouse, fly, worm, thale cress and baker's yeast. CONCLUSION: Evaluation results of the proposed method using functional keyword and Gene Ontology (GO) annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods.

This paper focuses on solving the group flexible flow-shop scheduling problem with more than two machine centers, which have the same number of parallel machines. The problem is solved at two levels, one for the job sequence in each group, and the other for the group sequence. One single-pass heuristic, the Palmer procedure, and two multiple-pass heuristics, the CDS and the LN procedures, are combined to solve it. Multiple-pass heuristics in general need more computation time but get a better solution than single-pass ones. Four heuristic algorithms, each with a single-pass heuristic (the Palmer heuristic) and a multiple-pass one (the CDS or LN heuristic) are then proposed and a comparative analysis is then made among them. Experiments are also made to analyze the performance of these four algorithms.

Edge Assembly Crossover (EAX) is by far the most successful crossover operator in solving the traveling salesman problem (TSP) with Genetic Algorithms (GAs). Various improvements have been proposed for EAX in GA. However, some of the improvements have to make compromises between performance and solution quality. In this work, we have combined several improvements proposed in the past, including heterogeneous pair selection (HpS), iterative child generation (ICG), and 2-opt. We also incorporate 2-opt into EAX, and restricted the 2-opt local searches to sub-tours in the intermediates generated by EAX.Our proposed method can improve the performance of EAX with decreased number of generations, error rates, and computation time. The applications of conventional 2-opt and our restricted 2-opt concurrently have additive effect on the performance gain, and this performance improvement is more obvious in larger problems. The proposed method also enhanced the solution quality of EAX. The significances of the restricted 2-opt and the conventional 2-opt in EAX were analyzed and discussed.

This paper introduces a novel scheduling problem called the active interval scheduling problem in hierarchical wireless sensor networks for long-term periodical monitoring applications. To improve the report sensitivity of the hierarchical wireless sensor networks, an efficient scheduling algorithm is desired. In this paper, we propose a compact genetic algorithm (CGA) to optimize the solution quality for sensor network maintenance. The experimental result shows that the proposed CGA brings better solutions in acceptable calculation time.

MOTIVATION: The explosion of microarray studies has promised to shed light on the temporal expression patterns of thousands of genes simultaneously. However, available methods are far from adequate in efficiently extracting useful information to aid in a greater understanding of transcriptional regulatory network. Biological systems have been modeled as dynamic systems for a long history, such as genetic networks and cell regulatory network. This study evaluated if the stochastic differential equation (SDE), which is prominent for modeling dynamic diffusion process originating from the irregular Brownian motion, can be applied in modeling the transcriptional regulatory network in Saccharomyces cerevisiae. RESULTS: To model the time-continuous gene-expression datasets, a model of SDE is applied to depict irregular patterns. Our goal is to fit a generalized linear model by combining putative regulators to estimate the transcriptional pattern of a target gene. Goodness-of-fit is evaluated by log-likelihood and Akaike Information Criterion. Moreover, estimations of the contribution of regulators and inference of transcriptional pattern are implemented by statistical approaches. Our SDE model is basic but the test results agree well with the observed dynamic expression patterns. It implies that advanced SDE model might be perfectly suited to portray transcriptional regulatory networks. AVAILABILITY: The R code is available on request. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: http://www.csie.ntu.edu.tw/~b89x035/yeast/

MOTIVATION: Differential detection on symptom-related pathogens (SRP) is critical for fast identification and accurate control against epidemic diseases. Conventional polymerase chain reaction (PCR) requires a large number of unique primers to amplify selected SRP target sequences. With multiple-use primers (mu-primers), multiple targets can be amplified and detected in one PCR experiment under standard reaction condition and reduced detection complexity. However, the time complexity of designing mu-primers with the best heuristic method available is too vast. We have formulated minimum-set mu-primer design problem as a set covering problem (SCP), and used modified compact genetic algorithm (MCGA) to solve this problem optimally and efficiently. We have also proposed new strategies of primer/probe design algorithm (PDA) on combining both minimum-set (MS) mu-primers and unique (UniQ) probes. Designed primer/probe set by PDA-MS/UniQ can amplify multiple genes simultaneously upon physical presence with minimum-set mu-primer amplification (MMA) before intended differential detection with probes-array hybridization (PAH) on the selected target set of SRP. RESULTS: The proposed PDA-MS/UniQ method pursues a much smaller number of primers set compared with conventional PCR. In the simulation experiment for amplifying 12 669 target sequences, the performance of our method with 68% reduction on required mu-primers number seems to be superior to the compared heuristic approaches in both computation efficiency and reduction percentage. Our integrated PDA-MS/UniQ method is applied to the differential detection on 9 plant viruses from 4 genera with MMA and PAH of 11 mu-primers instead of 18 unique ones in conventional PCR while amplifying overall 9 target sequences. The results of wet lab experiments with integrated MMA-PAH system have successfully validated the specificity and sensitivity of the primers/probes designed with our integrated PDA-MS/UniQ method.

SUMMARY: Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. In this study, a descriptor derived from the sequential distance between oxidized cysteines (denoted as DOC) is proposed. An approach using support vector machine (SVM) method based on weighted graph matching was further developed to predict the disulfide connectivity pattern in proteins. When DOC was applied, prediction accuracy of 63% for our SVM models could be achieved, which is significantly higher than those obtained from previous approaches. The results show that using the non-local descriptor DOC coupled with local sequence profiles significantly improves the prediction accuracy. These improvements demonstrate that DOC, with a proper scaling scheme, is an effective feature for the prediction of disulfide connectivity. The method developed in this work is available at the web server PreCys (prediction of cys-cys linkages of proteins).

MOTIVATION: Disulfide bonds play an important role in protein folding. A precise prediction of disulfide connectivity can strongly reduce the conformational search space and increase the accuracy in protein structure prediction. Conventional disulfide connectivity predictions use sequence information, and prediction accuracy is limited. Here, by using an alternative scheme with global information for disulfide connectivity prediction, higher performance is obtained with respect to other approaches. RESULT: Cysteine separation profiles have been used to predict the disulfide connectivity of proteins. The separations among oxidized cysteine residues on a protein sequence have been encoded into vectors named cysteine separation profiles (CSPs). Through comparisons of their CSPs, the disulfide connectivity of a test protein is inferred from a non-redundant template set. For non-redundant proteins in SwissProt 39 (SP39) sharing less than 30% sequence identity, the prediction accuracy of a fourfold cross-validation is 49%. The prediction accuracy of disulfide connectivity for proteins in SwissProt 43 (SP43) is even higher (53%). The relationship between the similarity of CSPs and the prediction accuracy is also discussed. The method proposed in this work is relatively simple and can generate higher accuracies compared to conventional methods. It may be also combined with other algorithms for further improvements in protein structure prediction. AVAILABILITY: The program and datasets are available from the authors upon request. CONTACT: [email protected].

Variable number of tandem repeats (VNTR) stands for a tandem repeat which has variation in length and the number of repeated segments between individuals. VNTRs are useful as molecule markers in many applications, such as DNA fingerprinting, genetic disease analysis and molecular typing of prokaryotes. It costs a large amount of money and time to identify these special loci by using traditional biological experiments. Here we develop a novel tool, VNTR analyzer, to identify and analyze VNTR in genome sequence by comparing genomic sequences of different strains. In this study, we demonstrate its ability to detect VNTRs by analyzing 3 bacterial species: Staphylococcus aureus, Xylella fastidiosa and Salmonella enterica. The results showed that our program could find VNTRs accurately in a short time. Moreover, the program provides biologists a colorful visualization tool for further analysis.

Microarray technology is a recent development in experimental molecular biology which can produce quantitative expression measurements for thousands of genes in a single, cellular mRNA sample. These many gene expression measurements form a composite profile of the sample, which can be used to differentiate samples from different classes such as tissue types or treatments. However, for the gene expression profile data obtained in a specific comparison, most likely only some of the genes will, be differentially expressed between the classes, while many other genes have similar expression levels. Selecting a list of informative differential genes from these data is important for microarray data analysis. In this paper, we describe a framework for selecting informative genes, called ranking and combination analysis (RAC), which combines various existing informative gene selection methods. We conducted experiments using three data sets and six existing feature selection methods. The results show that the RAC framework is a robust and efficient approach to identify informative gene for microarray data. The combination approach on two selecting methods almost always performed better than the less efficient individual, and in many cases, better than both. More significantly, when considering all three data sets together, the combination approach, on average, outperforms each individual feature selection method. All of these indicate that RCA might be a viable and feasible approach for the microarray gene expression analysis.

One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.

This study presents an evolutionary algorithm, called a heterogeneous selection genetic algorithm (HeSGA), for analyzing the patterns of gene expression on microarray data. Microarray technologies have provided the means to monitor the expression levels of a large number of genes simultaneously. Gene clustering and gene ordering are important in analyzing a large body of microarray expression data. The proposed method simultaneously solves gene clustering and gene-ordering problems by integrating global and local search mechanisms. Clustering and ordering information is used to identify functionally related genes and to infer genetic networks from immense microarray expression data. HeSGA was tested on eight test microarray datasets, ranging in size from 147 to 6221 genes. The experimental clustering and visual results indicate that HeSGA not only ordered genes smoothly but also grouped genes with similar gene expressions. Visualized results and a new scoring function that references predefined functional categories were employed to confirm the biological interpretations of results yielded using HeSGA and other methods. These results indicate that HeSGA has potential in analyzing gene expression patterns.

This work proposes an evolutionary algorithm, called the heterogeneous selection evolutionary algorithm (HeSEA), for solving large traveling salesman problems (TSP). The strengths and limitations of numerous well-known genetic operators are first analyzed, along with local search methods for TSPs from their solution qualities and mechanisms for preserving and adding edges. Based on this analysis, a new approach, HeSEA is proposed which integrates edge assembly crossover (EAX) and Lin-Kernighan (LK) local search, through family competition and heterogeneous pairing selection. This study demonstrates experimentally that EAX and LK can compensate for each other's disadvantages. Family competition and heterogeneous pairing selections are used to maintain the diversity of the population, which is especially useful for evolutionary algorithms in solving large TSPs. The proposed method was evaluated on 16 well-known TSPs in which the numbers of cities range from 318 to 13509. Experimental results indicate that HeSEA performs well and is very competitive with other approaches. The proposed method can determine the optimum path when the number of cities is under 10,000 and the mean solution quality is within 0.0074% above the optimum for each test problem. These findings imply that the proposed method can find tours robustly with a fixed small population and a limited family competition length in reasonable time, when used to solve large TSPs.

Very large-scale gene expression analysis, i.e., UniGene and dbEST, are provided to find those genes with significantly differential expression in specific tissues. The differentially expressed genes in a specific tissue are potentially regulated concurrently by a combination of transcription factors. This study attempts to mine putative binding sites on how combinations of the known regulatory sites homologs and over-represented repetitive elements are distributed in the promoter regions of considered groups of differentially expressed genes. We propose a data mining approach to statistically discover the significantly tissue-specific combinations of known site homologs and over-represented repetitive sequences, which are distributed in the promoter regions of differential gene groups. The association rules mined would facilitate to predict putative regulatory elements and identify genes potentially co-regulated by the putative regulatory elements.

In this paper an evolutionary algorithm for the traveling salesman problem is proposed. The key idea is to enhance the ability of exploration and exploitation by incorporating global search with local search. A new local search, called the neighbor-join (NJ) operator, is proposed to improve the solution quality of the edge assembly crossover (EAX) considered as a global search mechanism in this paper. Our method is applied to 15 well-known traveling salesman problems with numbers of cities ranging from 101 to 3038 cities. The experimental results indicate that the neighbor-join operator is very competitive with related operators surveyed in this paper. Incorporating the NJ into the EAX significantly outperforms the method incorporating 2-opt into the EAX for some hard problems. For each test instance the average value of solution quality stays within 0.03% from the optimum. For the notorious hard problem att532, it is able to find the optimum solution 23 times in 30 independent runs.

Some structural motifs, like tetra-loops, in ribosomal RNA are known to functionally implicate in virtually every aspect of protein synthesis. Our aim in this study is to discover common structural motifs (CSMs), which are related to specific domains or functions, within the secondary structures of ribosomal RNAs in a data set constructed. After applying data mining techniques to mine the common structural motifs, a machine learning approach is used to find significant discriminating common structural motifs from groups of organisms. By applying to several data sets constructed in this study, it suggests that the CSMs can provide effective information to classify organisms and help biologists understand the functions of ribosomal RNA. From the experiments of the classification of organisms and the construction of phylogenetic trees by CSMs mined, we find our approach is promising.

A flexible ligand docking protocol based on evolutionary algorithms is investigated. The proposed approach integrates decreasing-based mutations and self-adaptive mutations with differential evolution. This approach possesses global and local search strategies to balance the trade-off between exploitation and exploration of the search. The proposed approach is applied to a dihydrofolate reductase enzyme with the anti-cancer drug methotrexate and two analogues of antibacterial drug trimethoprim. Numerical results indicate that the new approach is very robust.

A robust evolutionary approach, called the Family Competition Evolutionary Algorithm (FCEA), is described for the synthesis of optical thin-film designs. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing mutations and self-adaptive mutations. The method is applied to three different optical coating designs with complex spectral quantities. Numerical results indicate that the proposed approach performs very robustly and is very competitive with other approaches.

In this paper, we present a new evolutionary technique to train three general neural networks. Based on family competition principles and adaptive rules, the proposed approach integrates decreasing-based mutations and self-adaptive mutations to collaborate with each other. Different mutations act as global and local strategies respectively to balance the trade-off between solution quality and convergence speed. Our algorithm is then applied to three different task domains: Boolean functions, regular language recognition, and artificial ant problems. Experimental results indicate that the proposed algorithm is very competitive with comparable evolutionary algorithms. We also discuss the search power of our proposed approach.

In this paper, we study an evolutionary algorithm for flexible ligand docking. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing mutations and self-adaptive mutations. To demonstrate the robustness of the proposed approach, we apply it to the problems of the first international contests on evolutionary optimization. Following the description of function optimization, our approach is applied to a dihydrofolate reductase enzyme with the anti-cancer drug methotrexate and with two analogs of the antibacterial drug trimethoprim. Our numerical results indicate that the proposed approach is robust. The docked lowest energy structures have rms derivations ranging from 0.72 A to 1.98 A with respect to the corresponding crystal structure.

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.

This investigation presents an adapted genetic algorithm to resolve simple plant location problems. The proposed algorithm applies a clustering technique as mutation guidance and a novel local search method to enhance the solution quality. The proposed algorithm is then applied to the fifteen test problems taken from Beasley's OR-Library (J.E. Beasley, 1990). Empirical results indicate that the error rate of the proposed adapted GA is less than 0.3 percent. In addition, the computational time is bounded by a polynomial function of the problem size.

This paper explores the use of neural networks to control robots in tasks requiring sequential and learning behavior. We propose a family competition evolutionary algorithm (FCEA) to evolve networks that can integrate these different types of behavior in a smooth and continuous manner. The approach integrates self-adaptive Gaussian mutation, self-adaptive Cauchy mutation, decreasing-based Gaussian mutation, and family competition. In order to illustrate the power of the approach, we apply this approach to two different task domains: the "artificial ant" problem and a sequential behavior problem - an agent learns to play football. From the experimental results, we find our approach performs much better than other evolutionary algorithms in these two tasks. Based on the results from our experiments, it is shown that our approach can evolve neural networks to provide a means of integrating, sequencing and learning within a single control system.

The molecular binding problem, one of the most important problems in structure based drug design, can be formulated as a global energy optimization problem by using molecular mechanics. A novel computational algorithm is proposed to address the molecular binding problem. The algorithm is derived from genetic algorithms (GA) plus simulated annealing (SA) hybrid techniques, namely population based annealing genetic algorithms (PAG). We have applied the algorithm to find binding structures for three drug protein molecular pairs. One of the three drugs is an anti cancer drug methotrexate (MTX) and the other two are analogues of the antibacterial drug trimethoprim. Moreover, we have also studied two other well resolved ligand receptor molecular complex which are obtained from the Protein Data Bank (PDB): Thermolysin-HONH-benzylmalonyl-L-Ala-Gly-p-nitroanilide complex (5tln) and HIV-1 protease-Hydroxyethylene isostere inhibitor complex. Hydroxyethylene isostere inhibitor is one of new potential HIV-1 protease inhibitors synthesized. Through our experiments, all of the binding results not only keep the energy at low levels, but also have a promising binding geometrical structure in terms of number of hydrogen bonds formed.

Molecular binding, important in drug design, explores the accurate binding structures between molecules. This exploration can be formulated as a global optimization problem. However, the problem in molecular binding is that the search space is very large and the computational cost increases tremendously with the growth of the degrees of freedom. In this paper, we utilize a new algorithm called the annealing genetic algorithm to solve the global optimization problem in molecular binding. Using a protein with three anti-cancer drugs in our model, our algorithm can find a binding structure with a complicated energy computation within a couple of hours and the experimental results indicate that the solutions are reasonable.>

A stochastic approach called the annealing-genetic algorithm is presented for solving some well-known combinatorial optimization problems. This approach incorporates genetic algorithms into simulated annealing to improve the performance of simulated annealing. The authors' approach can be viewed as a simulated annealing algorithm with population-based state transition and with genetic-operator-based quasi-equilibrium control and as a genetic algorithm with the Boltzmann-type selection operator. The goals of efficiency in the algorithm are (1) the gap between final solution and the optimal solution should be around 3% or less, and (2) the computation time should be bounded by a polynomial function of the problem size. Empirically, the error rate of the proposed annealing-genetic algorithm for solving the multiconstraint zero-one knapsack problem is less than 1%, for solving the set partitioning problem is less than 0.1%, and for solving the traveling salesman problem is around 3%.>