BACKGROUND: Although various machine learning-based predictors have been developed for estimating protein-protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: choice of learning algorithm, and the representation of protein pairs. To improve the performance of predicting protein-protein interactions, we exploit the synergy of multiple learning algorithms, and utilize the expressiveness of different protein-pair features. RESULTS: We developed a stacked generalization scheme that integrates five learning algorithms. We also designed three types of protein-pair features based on the physicochemical properties of amino acids, gene ontology annotations, and interaction network topologies. When tested on 19 published datasets collected from eight species, the proposed approach achieved a significantly higher or comparable overall performance, compared with seven competitive predictors. CONCLUSION: We introduced an ensemble learning approach for PPI prediction that integrated multiple learning algorithms and different protein-pair representations. The extensive comparisons with other state-of-the-art prediction tools demonstrated the feasibility and superiority of the proposed method.

Several factors contribute to individual variability in postoperative pain, therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. In this study involving 3052 patients, a multistrategy computational approach was developed for analgesic consumption prediction. This approach uses data on patient-controlled analgesia demand behavior over time and combines clustering, classification, and regression to mitigate the limitations of current statistical models. Cross-validation results indicated that the proposed approach significantly outperforms various existing regression methods. Moreover, a comparison between the predictions by anesthesiologists and medical specialists and those of the computational approach for an independent test data set of 60 patients further evidenced the superiority of the computational approach in predicting analgesic consumption because it produced markedly lower root mean squared errors.

The ability of antibodies to respond to an antigen depends on the antibodies' specific recognition of epitopes, which are sites of the antigen to which antibodies bind. An increase in the availability of protein sequences and structures has enabled the identification of conformational epitopes, using various computational methods. The meta learner, among various approaches, has proved its feasibility and comparable accuracy in B-cell epitope prediction in previous studies. Nevertheless, its performance highly depends on the classification results of its multiple epitope base predictors within the meta learning architecture. We here propose bagging meta decision trees for epitope prediction to avoid the dependence on epitope prediction tools, and introduce 3D sphere-based attributes to improve prediction accuracy. Our experimental results demonstrate the superior performance of the bagging meta decision tree approach in comparison with single epitope predictors.

Numerous theories and algorithms have been developed to solve vectorial data learning problems by searching for the hypothesis that best fits the observed training sample. However, many real-world applications involve samples that are not described as feature vectors, but as (dis)similarity data. Converting vectorial data into (dis)similarity data is more easily performed than converting (dis)similarity data into vectorial data. This study proposes a stochastic iterative distance transformation model for similarity-based learning. The proposed model can be used to identify a clear class boundary in data by modifying the (dis)similarities between examples. The experimental results indicate that the performance of the proposed method is comparable with those of various vector-based and proximity-based learning algorithms.

BACKGROUND: One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains. RESULTS: We developed the hierarchical meta-learning architectures based on stacked and cascade generalizations. The bottom level of the hierarchy consisted of four conformational and four linear epitope prediction tools that served as the base learners. To perform consistent and unbiased comparisons, we tested the meta-learning method on an independent set of antigen proteins that were not used previously to train the base epitope prediction tools. In addition, we conducted correlation and ablation studies of the base learners in the meta-learning model. Low correlation among the predictions of the base learners suggested that the eight base learners had complementary predictive capabilities. The ablation analysis indicated that the eight base learners differentially interacted and contributed to the final meta model. The results of the independent test demonstrated that the meta-learning approach markedly outperformed the single best-performing epitope predictor. CONCLUSIONS: Computational B-cell epitope prediction tools exhibit several differences that affect their performances when predicting epitopic regions in protein antigens. The proposed meta-learning approach for epitope prediction combines multiple prediction tools by integrating their complementary predictive strengths. Our experimental results demonstrate the superior performance of the combined approach in comparison with single epitope predictors.

Existing data dissemination protocols adopted flooding to propagate interests and find forwarding paths in wireless sensor networks (WSNs), which cause large energy consumption. To relieve this problem, we propose an efficient cluster-based data dissemination (ECDD) scheme. In the proposed ECDD, besides piggyback control information into interests to perform on-demand passive clustering, we also use control information to set each node a hop count for assisting a node to select next forwarding node with the least hop count to the sink. In this way, a shortest path to forward sensed data back to the sink can be found. Simulation results show that the proposed ECDD is 61.30% better than DD, a classical approach, and 22.33% better than ELPC in terms of average dissipated energy. Furthermore, our approach is 57.45% and 23.49% better than DD and ELPC, respectively, in terms of average delay. The proposed ECDD is feasible for applications of long-term monitoring and real-time response, such as a community health care system and rescue operation in a disaster area.

BACKGROUND: At present, the organization of system modules is typically limited to either a multilevel hierarchy that describes the "vertical" relationships between modules at different levels (e.g., module A at level two is included in module B at level one), or a single-level graph that represents the "horizontal" relationships among modules (e.g., genetic interactions between module A and module B). Both types of organizations fail to provide a broader and deeper view of the complex systems that arise from an integration of vertical and horizontal relationships. RESULTS: We propose a complex network analysis tool, Pyramabs, which was developed to integrate vertical and horizontal relationships and extract information at various granularities to create a pyramid from a complex system of interacting objects. The pyramid depicts the nested structure implied in a complex system, and shows the vertical relationships between abstract networks at different levels. In addition, at each level the abstract network of modules, which are connected by weighted links, represents the modules' horizontal relationships. We first tested Pyramabs on hierarchical random networks to verify its ability to find the module organization pre-embedded in the networks. We later tested it on a protein-protein interaction (PPI) network and a metabolic network. According to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the vertical relationships identified from the PPI and metabolic pathways correctly characterized the inclusion (i.e., part-of) relationship, and the horizontal relationships provided a good indication of the functional closeness between modules. Our experiments with Pyramabs demonstrated its ability to perform knowledge mining in complex systems. CONCLUSIONS: Networks are a flexible and convenient method of representing interactions in a complex system, and an increasing amount of information in real-world situations is described by complex networks. We considered the analysis of a complex network as an iterative process for extracting meaningful information at multiple granularities from a system of interacting objects. The quality of the interpretation of the networks depends on the completeness and expressiveness of the extracted knowledge representations. Pyramabs was designed to interpret a complex network through a disclosure of a pyramid of abstractions. The abstraction pyramid is a new knowledge representation that combines vertical and horizontal viewpoints at different degrees of abstraction. Interpretations in this form are more accurate and more meaningful than multilevel dendrograms or single-level graphs. Pyramabs can be accessed at http://140.113.166.165/pyramabs.php/.

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: Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA. RESULTS: We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at http://140.113.166.178/safast/. CONCLUSION: The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.

A number of biclustering approaches have been developed to mitigate the limitations of standard clustering algorithms. They have different problem formulation, search strategy and computational complexity. We proposed a new biclustering method based on the framework of market basket analysis in which a bicluster is described as a frequent itemset. As a feasibility test, we compared it with several standard clustering algorithms on a genome-wide yeast microarray dataset, and it showed very promising results. We later did a comparison between our approach and various current biclustering methods, following a systematic evaluation procedure recently published. The experimental results demonstrate that our new method outperforms the others

Post-transcriptional regulation, though less studied, is an important research topic in bioinformatics. In a set of post-transcriptionally coregulated RNAs, the basepair interactions can organize the molecules into domains and provide a framework for functional interactions. Their consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA motifs are more conserved in structures than in sequences. Knowing the structural motifs can help us better understand the regulation activities. We propose a novel data mining approach to RNA secondary structure prediction. To demonstrate the performance of our new approach, we first tested it on the same data sets previously used and published in literature. Secondly, to show the flexibility of our new approach, we also tested it on a data set that contains pseudoknot motifs that most current systems cannot identify.

UNLABELLED: Most of the current bioinformatics literature lacks an explicit and clear description of the data used in experiments when introducing or evaluating computational tools. Without the exact data set that was fed into computational tools in experiments, any mistaken preparation of the data for later experiments may lead to discrepancy in conclusion. The NCTU BioInfo Archive is a new web-based bioinformatic data archive. It serves as a test bed for evaluating computational tools, as a bridge to link other research communities with bioinformatics, and also as an environment full of motivations and possibilities to encourage more exploratory research in bioinformatics. AVAILABILITY: http://bioinfo.cis.nctu.edu.tw

Computational methods have become very important in analyzing biosequence data. However, most current computer-assisted methods are limited to finding motifs. In real life, genes can be regulated in many ways, involving various interactions among regulatory elements. We propose a new framework for analyzing gene behaviors in terms of the correlation between motifs and gene expressions. This framework consists of motif-finding algorithms, constructive induction and standard inductive learners. It complements the current development of microarray and genechip technology by providing a deeper insight of gene behaviors. A genome-wide gene expression analysis demonstrates the value of the studies of this new framework.

MOTIVATION: Computer-assisted methods are essential for the analysis of biosequences. Gene activity is regulated in part by the binding of regulatory molecules (transcription factors) to combinations of short motifs. The goal of our analysis is the development of algorithms to identify regulatory motifs and to predict the activity of combinations of those motifs. APPROACH: Our research begins with a new motif-finding method, using multiple objective functions and an improved stochastic iterative sampling strategy. Combinatorial motif analysis is accomplished by constructive induction that analyzes potential motif combinations. The hypothesis is generated by applying standard inductive learning algorithms. RESULTS: Tests using 10 previously identified regulons from budding yeast and 14 artificial families of sequences demonstrated the effectiveness of the new motif-finding method. Motif combination and classification approaches were used in the analysis of a sample DNA array data set derived from genome-wide gene expression analysis. AVAILABILITY: Programs will be available as executable files upon request. CONTACT: [email protected] [email protected]