{"title":"Predicting protein-protein interactions using numerical associational features","authors":"Waleed Aljandal, W. Hsu, Jing Xia","doi":"10.1109/CIBCB.2009.4925719","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925719","url":null,"abstract":"We investigate the problem of predicting protein-protein interaction (PPI) using numerical features constructed from parent-child relation of a partial network constructed from known protein interactions. For each pair of proteins, we use a validation-based approach to normalize these features, which are based on association rule interestingness measures. The primary contribution of this work is the parametric normalization formula we derive and calibrate using data for the PPI task. This formula improves basic interestingness measures through taking sizes of itemset into account. Our derived itemset size-sensitive measures consider those rare but significant relationships among the children and the parents of set of proteins. We evaluate our work using k-nearest neighbor and rule-based classification approach.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124603026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fuzzy rule base classifier fusion for protein mass spectra based ovarian cancer diagnosis","authors":"A. Assareh, L. Volkert","doi":"10.1109/CIBCB.2009.4925728","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925728","url":null,"abstract":"Fuzzy rule base classification systems have been the focus of increased attention in recent years, due to their unique capability of providing human experts with outcomes by means of linguistic rules. In the same time period classifier fusion approaches have been shown to enhance the performance of pattern recognition systems. In the present study we applied a hybrid random subspace fusion scheme that constructs a set of different fuzzy classifiers utilizing different subsets of both the feature space and the sample domain, combining the results of these classifiers using appropriate decision functions. Experimental results using two protein mass spectra datasets of ovarian cancer demonstrate the usefulness of this approach in comparison to other classifier fusion approaches.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116799199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The analysis of microarray datasets using a genetic programming","authors":"Chun-Gui Xu, Kun-hong Liu, De-shuang Huang","doi":"10.1109/CIBCB.2009.4925725","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925725","url":null,"abstract":"Microarray technology has been widely applied to search for biomarkers of diseases, diagnose diseases and analyze gene regulatory network. Abundance of expression data from microarray experiments are processed by informatics tools, such as Supporting Vector Machines (SVM), Artificial Neural Network (ANN), and so on. These methods achieve good results in single dataset. Nevertheless, most analyses of microarray data are only focused on a series of data obtained from the same lab or gene chip. Then the discoveries may only be suitable for data they experimented on but lack of general sense. In this paper, we propose a genetic programming (GP) based approach to analyze microarray datasets. The GP implements classification and feature selection at the same time. To validate the significance of the selected genes and generated classification rules, the results are tested on different datasets obtained from different experimental conditions. The results confirm the efficiency of GP in the classification of different samples.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124027082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matthew Conforth, Y. Meng, C. Valmikinathan, Xiaojun Yu
{"title":"Nerve graft selection for peripheral nerve regeneration using neural networks trained by a hybrid ACO/PSO method","authors":"Matthew Conforth, Y. Meng, C. Valmikinathan, Xiaojun Yu","doi":"10.1109/CIBCB.2009.4925730","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925730","url":null,"abstract":"Identification of the most successful strategy for applications in tissue engineering is often confusing, with a wide variety of options and variables available, that can fit into an ideal graft or scaffold. The complexity of the problem is multifold in application of grafts for regeneration of peripheral nerve injuries, with many variables that affect the regeneration process and thereby the success of regeneration. Here, we develop a Swarm Intelligence based artificial neural network (SWIRL) to predict the outcome of success of a nerve graft, thus providing critical information on the ability of a nerve graft to succeed under certain circumstances. Over 30 independent variables were identified and used as features for training the network and estimation of outcomes. Specific parameters such as the critical regeneration length and the ratio of the actual length to critical length were used in the evaluation and estimation of the success of the nerve grafts. Using the SWIRL, we estimate the success of regeneration of any nerve grafts to approximately 92.59 % accuracy. This system could allow for the estimation of the best possible outcome with a fixed set of variables or identification of best possible combinations with the multitude of options available, aiding researchers to perform experiments and test hypothesis efficiently and ethically.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124170755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improved prediction of trans-membrane spans in proteins using an artificial neural network","authors":"J. Koehler, Ralf Mueller, J. Meiler","doi":"10.1109/CIBCB.2009.4925709","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925709","url":null,"abstract":"Tools for the identification of trans-membrane spans from the protein sequence are widely used in the experimental community. Computational structural biology seeks to increase the prediction accuracy of such methods since they represent a first step towards membrane protein tertiary structure prediction from the amino acid sequence. We introduce a predictor that is able to identify trans-membrane spans from the sequence of a protein. The novelty of the approach presented here is the simultaneous prediction of trans-membrane spanning α-helices and β-strands within a single tool. An artificial neural network was trained on databases of 102 membrane proteins and 3499 soluble proteins. Prediction accuracies of up to 92% for soluble residues, 75% for residues in the interface, and 73% for TM residues are achieved. On average the algorithm predicts 79% of the residues correctly which is a substantial improvement from a previously published implementation which achieved 57% accuracy (Koehler et al., Proteins: Structure, Function, and Bioinformatics, 2008). The algorithm was applied to four membrane proteins to illustrate the applicability to both α-helical bundles and β-barrels.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131061251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A framework for the application of decision trees to the analysis of SNPs data","authors":"L. Fiaschi, J. Garibaldi, N. Krasnogor","doi":"10.1109/CIBCB.2009.4925715","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925715","url":null,"abstract":"Data mining is the analysis of experimental datasets to extract trends and relationships that can be meaningful for the user. In genetic studies these techniques have revealed interesting findings, especially in the heritable predisposition to contract specific diseases. One of these diseases which is still under extensive analysis is pre-eclampsia, a progressive disorder which occurs during pregnancy and soon after the birth, affecting both the mothers and their babies. There are many choices to be made in the application of the various data mining techniques that may be used to study general genotype-phenotype associations. The aim of this paper is to describe the general framework that we adopted in the application of decision tree algorithms to the analysis of SNPs data related to cases of pre-eclampsia. The results show the validity of this methodology to detect a subset of attributes associated with the predictable variable, providing a reduction in the size of the dataset. Moreover, from the clinical point of view, it confirmed the medical interpretation of the ‘corrected birth-weight centile’ (CBC) value of 10 being a meaningful cut-off and confirmed association between an infant's CBC and the ‘week of delivery’ parameter. We hope that the generic framework described here will be of use to other researchers analysing such data.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132052085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Assessing the impact of network depth on the analysis of PPI networks: A case study","authors":"J. Blayney, Haiying Wang, Huiru Zheng, F. Azuaje","doi":"10.1109/CIBCB.2009.4925729","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925729","url":null,"abstract":"Recent years have seen a growing interest in the incorporation of protein-protein interaction (PPI) networks to support functional genomic research. Often a default depth is assumed by network inference software. This case study considers the impact of network depth on the analysis of PPI networks using seven proteins known to be relevant to heart failure as inputs into the analysis. This paper analyses how the characteristics of a PPI network vary according to the level examined, suggesting that the investigation of network topology is an essential first step in PPI analysis. The classification of nodes, in terms of degree and betweenness centrality, within the network is also considered. The effect of network depth is also proved to be significant in the identification of potentially essential proteins with large connectivity and/or high betweenness centrality values.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122385896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chuan-Yu Chang, Jeng-Shiun Tsai, Chi-Jane Wang, P. Chung
{"title":"Emotion recognition with consideration of facial expression and physiological signals","authors":"Chuan-Yu Chang, Jeng-Shiun Tsai, Chi-Jane Wang, P. Chung","doi":"10.1109/CIBCB.2009.4925739","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925739","url":null,"abstract":"An emotion recognition system with consideration of facial expression and physiological signals is proposed in this paper. A specific designed mood induction experiment is performed to collect facial expressing images and physiological signals of subjects. We detected 14 feature points and extracted 12 facial features from facial expression images. Meanwhile, we measure the skin conductivity, finger temperature and heart rate from the subject. Both facial and physiological features are adopted to train the classifiers. Two learning vector quantization (LVQ) neural networks were applied to classify four emotions: love, joy, surprise and fear. Experimental results show the proposed recognition system is able to identify four emotions by facial expressions, physiological signals, and both of them.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127517588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Hallinan, M. Pocock, S. Addinall, D. Lydall, A. Wipat
{"title":"Clustering incorporating shortest paths identifies relevant modules in functional interaction networks","authors":"J. Hallinan, M. Pocock, S. Addinall, D. Lydall, A. Wipat","doi":"10.1109/CIBCB.2009.4925733","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925733","url":null,"abstract":"Many biological systems can be modeled as networks. Hence, network analysis is of increasing importance to systems biology. We describe an evolutionary algorithm for selecting clusters of nodes within a large network based upon network topology together with a measure of the relevance of nodes to a set of independently identified genes of interest. We apply the algorithm to a previously published integrated functional network of yeast genes, using a set of query genes derived from a whole genome screen of yeast strains with a mutation in a telomere uncapping gene. We find that the algorithm identifies biologically plausible clusters of genes which are related to the cell cycle, and which contain interactions not previously identified as potentially important. We conclude that the algorithm is valuable for the querying of complex networks, and the generation of biological hypotheses.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128942332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"DNA error correcting codes: No crossover.","authors":"D. Ashlock, S. Houghten","doi":"10.1109/CIBCB.2009.4925705","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925705","url":null,"abstract":"DNA error correcting codes over the edit metric create embeddable markers for sequencing projects that are tolerant of sequencing errors. When a sequence library has multiple sources for its sequences, use of embedded markers permit tracking of sequence origin. Evolutionary algorithms are currently the best known technique for optimizing DNA error correcting codes. In this study we resolve the question of the utility of the crossover operator used in earlier studies on optimizing DNA error correcting codes. The crossover operator in question is found to be substantially counterproductive. A majority of crossover events produce results that violate minimum-distance constraints required for error correction. A new algorithm, a form of modified evolution strategy, is tested and is found to locate codes with record size. The table of best know sizes for DNA-error correcting codes is updated.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123924196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}