International Journal of Computational Bioscience最新文献

{"title":"SPECIAL ISSUE ON DATA MINING AND PATTERN ANALYSIS IN COMPUTATIONAL BIOSCIENCE","authors":"S. Maulik, Jason T. L.Wang","doi":"10.2316/JOURNAL.210.2010.1.210(1001","DOIUrl":"https://doi.org/10.2316/JOURNAL.210.2010.1.210(1001","url":null,"abstract":"Computational bioscience aims to develop innovative methods for solving significant problems in the biological sciences. Some of the grand challenges in computational bioscience include mining molecular interactions, whole genome comparison, sequence and structure motif discovery , and gene expression microarray data analysis. This special issue provides a collection of papers that report recent advances in computational bioscience with a focus on biological pattern discovery and data mining. The special issue begins with a meeting report, followed by five articles. In \" The DNA–Proteome: Recent advances towards establishing the protein–DNA interaction space, \" Erich Grotewold and Herbert Auer summarize findings from the DNA–Proteome Barcelona BioMed Conference held in Barcelona in April 2009. This conference brought together 150 scientists from around the world, exploring advances in establishing the DNA–protein space in eukaryotic organisms, from humans to yeast and plants. Then the special issue contains two papers, both of which are related to molecular interactions. In \" Prediction of protein function from connectivity of protein interaction networks, \" Lei Shi et al. present an artificial neural network approach to predict protein functions through integration of several protein interaction data sets. The authors experimentally show that their approach outperforms other existing methods on MIPS functional categories. In \" Comparison of chemical descriptors for protein–compound interaction prediction, \" Jintao Zhang and Jun Huan report a case study on comparing the performance of several different chemical descriptors for predicting protein–compound interactions. The authors conclude that frequent subgraph-based descriptors and the signature molecular descriptor work well, and the appropriate selection of chemical descriptors is important in achieving good results. The next two papers are concerned with pattern search in genomic data. In \" Mining roX1 RNA in Drosophila genomes using covariance models, \" Kevin Byron et al. propose a methodology for finding roX1 non-coding RNAs in 12 Drosophila species by utilizing structural alignment and statistical profiles. The authors experimentally show that their methodology is more effective than Blast in detecting functional RNA homologs in Drosophila genomes. In \" An integrated bioinformatics approach to the discovery of cis-regulatory elements involved in plant gravitropic signal transduction, \" Xiaoyu Liang et al. describe techniques for identifying putative regulatory functional elements , including transcription factor binding sites and cis-regulatory modules involved in gravitropic signal transduc-tion. By analysing gene expression data from microarray experiments, the authors discover 32 putative regulatory elements and 55 putative regulatory modules, demonstrating the effectiveness of their techniques. …","PeriodicalId":330541,"journal":{"name":"International Journal of Computational Bioscience","volume":"7 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":"122465057","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 INTEGRATED BIOINFORMATICS APPROACH TO THE DISCOVERY OF CIS -REGULATORY ELEMENTS INVOLVED IN PLANT GRAVITROPIC SIGNAL TRANSDUCTION","authors":"Xiaoyu Liang, K. Shen, J. Lichtenberg, S. Wyatt, L. Welch","doi":"10.2316/JOURNAL.210.2010.1.210-1013","DOIUrl":"https://doi.org/10.2316/JOURNAL.210.2010.1.210-1013","url":null,"abstract":"Gravity is a common stimulus affecting plant growth and development, from seed germination to positioning of flowers for pollination and seeds for dispersal. Classic models of plant gravitropism have revolved around biophysical perception of the gravity stimulus and the effects of plant growth regulators on the growth response. Transcriptional regulation of the gravitropic mechanism has been largely ignored. The aim of this experiment is to identify putative regulatory functional elements, including transcription factor binding sites and cis-regulatory modules involved in gravitropic signal transduction. In this article, we detailed a strategy to identify putative cis-regulatory elements by analyzing gene expression data from microarray experiments. Genes involved in the gravitropic perception– response pathway were identified based on their changes in expression level after gravity stimulation. Genes were clustered according to their expression patterns (transcriptional regulation profiles), and gene promoter were analyzed using genomics regulatory analysis software to identify candidate cis-regulatory elements and cis-regulatory modules. Analysis of the microarray data indicated that 154 genes were involved in the gravitropic response. The genes were grouped into 9 clusters based on expression profile similarities. An analysis of the promoters of the 154 genes resulted in the identification of 32 putative regulatory elements and 55 putative regulatory modules. Some of the elements are associated with individual clusters and other elements are associated with multiple clusters, potentially indicating elements involved in specific and in general gravitropic response processes, respectively.","PeriodicalId":330541,"journal":{"name":"International Journal of Computational Bioscience","volume":"1 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":"130251677","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":"PREDICTION OF PROTEIN FUNCTION FROM CONNECTIVITY OF PROTEIN INTERACTION NETWORKS","authors":"L. Shi, Young-Rae Cho, A. Zhang","doi":"10.2316/JOURNAL.210.2010.1.210-1009","DOIUrl":"https://doi.org/10.2316/JOURNAL.210.2010.1.210-1009","url":null,"abstract":"Determining protein function on a proteomic scale is a major challenge in the post-genomic era. Right now only less than half of the actual functional annotations are available for a typical proteome. The recent high-throughput bio-techniques have provided us large-scale protein–protein interaction (PPI) data, and many studies have shown that function prediction from PPI data is a promising way as proteins are likely to collaborate for a common purpose. However, the protein interaction data is very noisy, which makes the task very challenging. In this paper, a distance matrix is proposed based on the smallworld property and connectivity of the PPI network. It measures the reliability of edges and filters the noise in the network. In addition, we design an ANN (artificial neural network) method to predict protein functions with integration of several protein interaction data sets. Our approach is tested with MIPS functional categories and the experiential results show that our approach has better performance than other existing methods in terms of precision and recall.","PeriodicalId":330541,"journal":{"name":"International Journal of Computational Bioscience","volume":"27 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":"132765403","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":"SAMPLE SIZE ESTIMATION FOR CANCER PROGRESSION MODELS","authors":"C. Netzer, J. Rahnenführer","doi":"10.2316/J.2012.210-1029","DOIUrl":"https://doi.org/10.2316/J.2012.210-1029","url":null,"abstract":"Human tumours are often associated with the accumulation of chromosomal alterations in the cancer cells. The identification of characteristic pathogenic routes improves prediction of survival times and optimal therapy choice. The simplest model assumes independent alterations. Then progression is measured by the count statistic, the total number of alterations. An advanced model is the oncogenetic trees mixture model. An oncogenetic tree allows both independent and sequential relationships between alterations, and the mixture model divides the patients into groups with different progression paths. Progression along such a model can be quantified univariately by the GPS (genetic progression score). On real cancer data, the GPS was shown to discriminate better than the count statistic between patient subgroups with different survival prognosis. Here, in a simulation study, we evaluate the necessary numbers of patients for detecting true relationships between genetic progression and survival time. We generate survival times correlated with count statistic and GPS, respectively. If the simple model is the correct one, misspecification with the advanced model requires about 20% larger sample size, independent from the number of events. In contrast, misspecification with the simple model leads with increasing numbers of events from 20% to 70% larger sample size. Additionally, if the true data-generating model is the mixture model, the absolute numbers are more than twice as large, thus favouring the advanced modelling approach especially in situations with limited model knowledge.","PeriodicalId":330541,"journal":{"name":"International Journal of Computational Bioscience","volume":"80 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":"130989332","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":"TWO–PREDATOR AND TWO–PREY SPECIES GROUP DEFENCE MODEL WITH SWITCHING EFFECT","authors":"Q. J. Khan, E. Krishnan, E. Balakrishnan","doi":"10.2316/J.2010.210-1003","DOIUrl":"https://doi.org/10.2316/J.2010.210-1003","url":null,"abstract":"A model which describes the interaction of two prey species with two predators is analysed. Prey species are of large size and exhibit group defence. Both predators are of same species and they select prey species which are numerically less and have insufficient defending capability. We found conditions for nontrivial equilibrium to be asymptotically stable and corresponding numerical results are","PeriodicalId":330541,"journal":{"name":"International Journal of Computational Bioscience","volume":"3 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":"128325240","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":"SIGNAL ANALYSIS OF MULTI-PARAMETRIC MR IMAGES IN HIGHER ORDER FOURIER SPACES","authors":"D. Assefa, H. Keller, David A. Jaffray","doi":"10.2316/J.2013.210-1055","DOIUrl":"https://doi.org/10.2316/J.2013.210-1055","url":null,"abstract":"","PeriodicalId":330541,"journal":{"name":"International Journal of Computational Bioscience","volume":"127 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":"116049017","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":"COMPARISON OF CHEMICAL DESCRIPTORS FOR PROTEIN–CHEMICAL INTERACTION PREDICTION","authors":"Jintao Zhang, Jun Huan","doi":"10.2316/JOURNAL.210.2010.1.210-1010","DOIUrl":"https://doi.org/10.2316/JOURNAL.210.2010.1.210-1010","url":null,"abstract":"Predicting protein–chemical interaction has been an important and challenging task in the bioinformatics community, and there are many related applications in biomedical research, including QSAR modelling and novel lead discovery. A fundamental hypothesis for predicting protein–chemical interaction is that chemical compounds sharing chemical similarity should also share protein target profiles, and the critical question is hence how to measure the distance (or similarity) between two chemicals. An increasing number of chemical descriptors have been invented in the past decades. As chemical descriptors play a critical role in predicting protein– chemical interaction, it is of great importance to compare chemical descriptors and evaluate their performance in such predictions. In this paper, we reported our case study on comparing the performance of DRAGON descriptors, the frequent subgraph-based descriptors (FFSM), and the signature molecular descriptor on predicting protein–chemical interaction using support vector machines over a large number of data sets. Our experiments demonstrated that FFSM and signature descriptors outperformed most DRAGON descriptor classes, and wisely selecting chemical descriptors will be beneficial for predicting protein–chemical interaction.","PeriodicalId":330541,"journal":{"name":"International Journal of Computational Bioscience","volume":"150 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":"134069870","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":"ONLINE BRAIN-MACHINE INTERFACE WITH AUTOMATIC DETERMINATION OF STOPPING TIME OF TRAINING PHASE","authors":"Yumi Dobashi, Atsushi Takemoto, Shu Shigezumi, Takumi Shiraki, Katsuki Nakamura, T. Matsumoto","doi":"10.2316/J.2012.210-1047","DOIUrl":"https://doi.org/10.2316/J.2012.210-1047","url":null,"abstract":"Electroencephalography (EEG) signals are one of the most popular signals used for brain–machine interfaces (BMIs). EEG-based BMI methods often work in batch mode, where a user must conduct the learning phase for a pre-determined period of time. This paper proposes an EEG-based sequential BMI system in which (i) the machine can determine when to end the learning phase automatically by monitoring the learning progress using the sequential error rate (SER) as an evaluation index and (ii) sequential learning in both the brain and the machine in a cooperative manner is employed. In the proposed approach, called brain–machine co-learning, subjects learn how to use the system by means of real-time visual feedback, whereas the machine learns the subjects’ EEG signals by Bayesian sequential learning. The SER refers to the average classification error rate windowed over a short time period, which was proposed in Hara et al. , Sequential error rate evaluation of SSVEP classification Problem with Bayesian sequential learning, Proc. 10th IEEE Int. Conf. on Information Technology and Applications in Biomedicine , Corfu, Greece, November 2–5, 2010, and it represents the status of Bayesian sequential learning in real time. In our proposed approach, subjects can use the system while eliminating unnecessary training. The proposed system was tested against a steady-state visual-evoked potential classification problem. The training phase varied for each subject and was sometimes short, yet satisfactory, leading to high classification accuracy.","PeriodicalId":330541,"journal":{"name":"International Journal of Computational Bioscience","volume":"27 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":"125502916","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}