2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献

{"title":"A Bayesian approach to construct Context-Specific Gene Ontology: Application to protein function prediction","authors":"Hasna Njah, Salma Jamoussi, W. Mahdi, M. Elati","doi":"10.1109/CIBCB.2016.7758127","DOIUrl":"https://doi.org/10.1109/CIBCB.2016.7758127","url":null,"abstract":"The annotation of protein provides a considerable knowledge for the biologists in order to understand life at the molecular level. The computational annotation of protein function has therefore emerged as an important alternative given that the biological experiments are extremely laborious. A number of methods have been developed to computationally annotate proteins using standardized nomenclatures such as Gene Ontology. These methods are based on various independency assumptions for modeling the annotation problem. However, the recent network analysis reveals that the same protein with different interactions may perform different functions. In this paper, we take into account the topology of the protein-protein interaction network in order to propose a new representation of functions' ontology. We use the Bayesian network in order to model and to alter the structure of this ontology so as to create the new context specific ontology. We use this newly proposed structure for predicting the functions of the unlabeled proteins. We evaluate our method, called Context-Specific Ontology by the use of the Bayesian Network (ConSOn-BN), on the Saccharomyces cerevisiae protein-protein interaction network and we find that ConSOn-BN has enhanced results as compared to some known methods.","PeriodicalId":368740,"journal":{"name":"2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133335131","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":"Gene selection using interaction information for microarray-based cancer classification","authors":"Songyot Nakariyakul","doi":"10.1109/CIBCB.2016.7758100","DOIUrl":"https://doi.org/10.1109/CIBCB.2016.7758100","url":null,"abstract":"Gene selection is an important pre-processing step in microarray analysis and classification. While traditional gene selection algorithms focus on identifying relevant and irredundant genes, we present a new gene selection algorithm that chooses gene subsets based on their interaction information. Many individual genes may be irrelevant with the class, but when combined together, they can interact and provide information useful for classification. Our proposed gene selection algorithm is tested on four well-known cancer microarray datasets. Initial results show that our algorithm selects effective gene subsets and outperforms prior gene selection algorithm in terms of classification accuracy.","PeriodicalId":368740,"journal":{"name":"2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"709 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121998364","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":"Wearable antenna design for bioinformation","authors":"Rui Pei, J. Wang, M. Leach, Zhao Wang, Sanghyuk Lee, E. Lim","doi":"10.1109/CIBCB.2016.7758129","DOIUrl":"https://doi.org/10.1109/CIBCB.2016.7758129","url":null,"abstract":"This paper is a study of wearable antenna design for medical applications. A literature review of existing wearable systems is performed, with specific attention paid to the antenna element. Two antennas working at 2.4 GHz were simulated using a software tool; firstly a basic rectangular patch on FR4 substrate and the other on soft textile material. The bending performance of the soft textile antenna was investigated.","PeriodicalId":368740,"journal":{"name":"2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127976157","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":"Using Bayesian modeling on molecular fragments features for virtual screening","authors":"D. Hoksza, P. Škoda","doi":"10.1109/CIBCB.2016.7758111","DOIUrl":"https://doi.org/10.1109/CIBCB.2016.7758111","url":null,"abstract":"Virtual screening enables to search large small-molecule compound libraries for active molecules with respect to given macromolecular target. In ligand-based virtual screening, this goal is achieved by utilizing information about fragments or patterns present in existing known active compounds. Typically, the patterns are encoded as fingerprints which are used to screen a database of candidate compounds. In this work, we introduce an approach which uses Bayesian inference to encode activity-related information. Unlike previous approaches, our method does not utilize simple fragments, but rather uses features of these fragments. For each molecule, we generate a set of molecular fragments and extract molecular features for each of them. Next, we remove correlated features and use the remaining ones to build a Bayes model of activity. To score a previously unseen molecule, the molecule's fragment feature vectors are passed to the model and a score is obtained as the aggregation of their probability scores. When screening a database, this score is used to rank the compounds database. We show on datasets with various levels of difficulty that using fragments features rather then fragments themselves results in improvement of retrieval rates with respect to the best state-of-the art molecular fingerprints.","PeriodicalId":368740,"journal":{"name":"2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130057879","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":"Partial Logistic Artificial Neural Network with automatic relevance determination and Markov Chain Monte Carlo methods applied in medical survival studies","authors":"Corneliu T. C. Arsene","doi":"10.1109/CIBCB.2016.7758122","DOIUrl":"https://doi.org/10.1109/CIBCB.2016.7758122","url":null,"abstract":"This paper builds on previous work involving different Bayesian Neural Networks namely Partial Logistic Artificial Neural Network with Automatic Relevance Determination (PLANN-ARD) for Single Risk (SR) and Competing Risk (CR) [1, 6, 15, 16] and applied in the medical survival studies. The results obtained with these PLANN-ARD/PLANN-CR-ARD models are compared with the results obtained with a different set of Bayesian Neural Networks namely the Markov Chain Monte Carlo (MCMC) methods [19, 20]. This work is done in the recent context of evaluation of large number of classification models [25] on medical dataset(s) and more specifically in the context of evaluation of different types of Bayesian Neural Networks [23, 26-29] in the medical survival analysis domain. There is such an interest in studying various classification and regression models for outcome prediction in medical survival analysis. Two medical datasets are used herein: a node negative breast cancer dataset (SR analysis) and a Primary Biliary Cirrhosis (PBC) dataset (CR analysis). The PLANN-ARD/PLANN-CR-ARD models form a group of two neural network models which are based on gradient type of optimization algorithms for the calculus of the neural network parameters. The MCMC sampling methods represent another set of models which are used in this paper for SR study (MCMC-SR algorithm) and CR study (MCMC-CR algorithm) in medical survival domain. The MCMC sampling methods are implemented by a MCMC toolbox available in the literature [10, 11, 19-21]. The MCMC methods are sampling from the prior probability distributions of the model parameters and they include the Gibbs sampler, the Metropolis-Hastings sampler, the Hybrid Markov Chain Monte Carlo (HMCMC) sampler or the Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler. The results obtained by the four BNNs are compared with the non-parametric estimates obtained through the survival study of the two medical datasets from above. The results show a superiority of the PLANN-ARD/PLANN-CR-ARD models with regard to the MCMC-SR/MCMC-CR algorithms from the point of view of the model selection task which was less computational expensive for the PLANN-ARD/PLANN-CR-ARD models.","PeriodicalId":368740,"journal":{"name":"2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131583614","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":"MotifGP: Using multi-objective evolutionary computing for mining network expressions in DNA sequences","authors":"Manuel Belmadani, M. Turcotte","doi":"10.1109/CIBCB.2016.7758133","DOIUrl":"https://doi.org/10.1109/CIBCB.2016.7758133","url":null,"abstract":"This paper describes and evaluates a multi-objective strongly typed genetic programming algorithm for the discovery of network expressions in DNA sequences. Using 13 realistic data sets, we compare the results of our tool, MotifGP, to that of DREME, a state-of-the-art program. MotifGP outperforms DREME when the motifs to be sought are long, and the specificity is distributed over the length of the motif. For shorter motifs, the performance of MotifGP compares favourably with the state-of-the-art method. Finally, we discuss the advantages of multi-objective optimization in the context of this specific motif discovery problem.","PeriodicalId":368740,"journal":{"name":"2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131869709","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":"An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy","authors":"Xiangdong Zhou, Keith C. C. Chan","doi":"10.1109/CIBCB.2016.7758094","DOIUrl":"https://doi.org/10.1109/CIBCB.2016.7758094","url":null,"abstract":"Multifactor dimensionality reduction (MDR) is originally proposed to identify gene-gene and gene-environment interactions associated with binary traits. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these methods are still not computationally efficient or effective. In this paper, we propose Fuzzy Quantitative trait based Ordinal MDR (QOMDR) to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then using a fuzzy balance accuracy measure based on generalized member function of fuzzy sets to select best sets of SNPs as having strong association with the trait. Experimental results on two real datasets show that our algorithm has better consistency and classification accuracy in identifying gene-gene interactions associated with QTs.","PeriodicalId":368740,"journal":{"name":"2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114444776","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":"GPU-powered Bat Algorithm for the parameter estimation of biochemical kinetic values","authors":"A. Tangherloni, Marco S. Nobile, P. Cazzaniga","doi":"10.1109/CIBCB.2016.7758103","DOIUrl":"https://doi.org/10.1109/CIBCB.2016.7758103","url":null,"abstract":"The emergent behavior of biochemical systems can be investigated by means of mathematical modeling and computational analyses, which usually require the automatic inference of the unknown values of the model's parameters. This problem, known as Parameter Estimation (PE), is usually tackled with bio-inspired meta-heuristics for global optimization, most notably Particle Swarm Optimization (PSO). In this work we assess the performances of PSO and Bat Algorithm with differential operator and Lévy flights trajectories (DLBA). In particular, we compared these meta-heuristics for the PE using two biochemical models: the expression of genes in prokaryotes and the heat shock response in eukaryotes. In our tests, we also evaluated the impact on PE of different strategies for the initial positioning of individuals within the search space. Our results show that DLBA achieves comparable results with respect to PSO, but it converges to better results when a uniform initialization is employed. Since every iteration of DLBA requires three fitness evaluations for each bat, the whole methodology is built around a GPU-powered biochemical simulator (cupSODA) which is able to parallelize the process. We show that the acceleration achieved with cupSODA strongly reduces the running time, with an empirical 61× speedup that has been obtained comparing a Nvidia GeForce Titan GTX with respect to a CPU Intel Core i7-4790K. Moreover, we show that DLBA always outperforms PSO with respect to the computational time required to execute the optimization process.","PeriodicalId":368740,"journal":{"name":"2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129310232","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":"Situation-based servo braking assistance for a manual wheelchair","authors":"D. Chugo, Noburiho Goto, S. Yokota, S. Muramatsu, H. Hashimoto","doi":"10.1109/CIBCB.2016.7758112","DOIUrl":"https://doi.org/10.1109/CIBCB.2016.7758112","url":null,"abstract":"This paper proposes a novel driving-assistance system for manual wheelchairs with consideration of both uphill and downhill conditions. On an inclined road, there is a high risk of a wheelchair moving in a direction that the user does not intend. In our previous works, the user has driven our assistive wheelchair in the usual manner. Our proposed system estimates its user's intentions and passively works to complement their intentional force by negating the wheel traction that is generated by the road's inclination using only the servo brakes on each wheel. Nevertheless, in some cases, our system fails to assist the driving motion of its user because the user drives the wheelchair in several ways that depend upon the environmental condition, for example, during uphill or downhill driving. The required assistance is not constant according to the situation, and it is difficult to assist with one wheel-control algorithm. Therefore, in this study, we first investigate the required assistance condition according to the driving situation by conducting a preliminary experiment with wheelchair users. Considering the results of this investigation, we then propose a novel user interface that intuitively shows the system information and a wheel-control algorithm that selects a suitable wheel controller according to the driving situation.","PeriodicalId":368740,"journal":{"name":"2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127134608","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 meta decision tree approach for B-cell epitope mining","authors":"Yuh-Jyh Hu, Shun-Ning You","doi":"10.1109/CIBCB.2016.7758110","DOIUrl":"https://doi.org/10.1109/CIBCB.2016.7758110","url":null,"abstract":"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.","PeriodicalId":368740,"journal":{"name":"2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121201563","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}