Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献

A Probabilistic Programming Approach to Protein Structure Superposition. 蛋白质结构叠加的概率编程方法

Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2019-07-01 Epub Date: 2019-08-08 DOI: 10.1109/cibcb.2019.8791469

Lys Sanz Moreta, Ahmad Salim Al-Sibahi, Douglas Theobald, William Bullock, Basile Nicolas Rommes, Andreas Manoukian, Thomas Hamelryck

{"title":"A Probabilistic Programming Approach to Protein Structure Superposition.","authors":"Lys Sanz Moreta, Ahmad Salim Al-Sibahi, Douglas Theobald, William Bullock, Basile Nicolas Rommes, Andreas Manoukian, Thomas Hamelryck","doi":"10.1109/cibcb.2019.8791469","DOIUrl":"10.1109/cibcb.2019.8791469","url":null,"abstract":"Optimal superposition of protein structures or other biological molecules is crucial for understanding their structure, function, dynamics and evolution. Here, we investigate the use of probabilistic programming to superimpose protein structures guided by a Bayesian model. Our model THESEUS-PP is based on the THESEUS model, a probabilistic model of protein superposition based on rotation, translation and perturbation of an underlying, latent mean structure. The model was implemented in the probabilistic programming language Pyro. Unlike conventional methods that minimize the sum of the squared distances, THESEUS takes into account correlated atom positions and heteroscedasticity (ie. atom positions can feature different variances). THESEUS performs maximum likelihood estimation using iterative expectation-maximization. In contrast, THESEUS-PP allows automated maximum a-posteriori (MAP) estimation using suitable priors over rotation, translation, variances and latent mean structure. The results indicate that probabilistic programming is a powerful new paradigm for the formulation of Bayesian probabilistic models concerning biomolecular structure. Specifically, we envision the use of the THESEUS-PP model as a suitable error model or likelihood in Bayesian protein structure prediction using deep probabilistic programming.","PeriodicalId":88962,"journal":{"name":"Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"2019 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d5/8b/nihms-1744718.PMC8515897.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39530143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linkage Disequilibrium in Genetic Association Studies Improves the Performance of Grammatical Evolution Neural Networks. 遗传关联研究中的连锁不平衡提高了语法进化神经网络的性能。

Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2007-04-01

Alison A Motsinger, David M Reif, Theresa J Fanelli, Anna C Davis, Marylyn D Ritchie

{"title":"Linkage Disequilibrium in Genetic Association Studies Improves the Performance of Grammatical Evolution Neural Networks.","authors":"Alison A Motsinger, David M Reif, Theresa J Fanelli, Anna C Davis, Marylyn D Ritchie","doi":"","DOIUrl":"","url":null,"abstract":"One of the most important goals in genetic epidemiology is the identification of genetic factors/features that predict complex diseases. The ubiquitous nature of gene-gene interactions in the underlying etiology of common diseases creates an important analytical challenge, spurring the introduction of novel, computational approaches. One such method is a grammatical evolution neural network (GENN) approach. GENN has been shown to have high power to detect such interactions in simulation studies, but previous studies have ignored an important feature of most genetic data: linkage disequilibrium (LD). LD describes the non-random association of alleles not necessarily on the same chromosome. This results in strong correlation between variables in a dataset, which can complicate analysis. In the current study, data simulations with a range of LD patterns are used to assess the impact of such correlated variables on the performance of GENN. Our results show that not only do patterns of strong LD not decrease the power of GENN to detect genetic associations, they actually increase its power.","PeriodicalId":88962,"journal":{"name":"Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"2007 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3092290/pdf/nihms-160137.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29883735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biological Sequence Mining Using Plausible Neural Network and its Application to Exon/intron Boundaries Prediction. 基于似然神经网络的生物序列挖掘及其在外显子/内含子边界预测中的应用

Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2007-01-01 DOI: 10.1901/jaba.2007.2007-165

Kuochen Li, Dar-Jen Chang, Eric Rouchka, Yuan Yan Chen

引用次数: 0

Understanding the Evolutionary Process of Grammatical Evolution Neural Networks for Feature Selection in Genetic Epidemiology. 理解遗传流行病学中用于特征选择的语法进化神经网络的进化过程。

Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2006-09-28 DOI: 10.1109/CIBCB.2006.330945

Alison A Motsinger, David M Reif, Scott M Dudek, Marylyn D Ritchie

{"title":"Understanding the Evolutionary Process of Grammatical Evolution Neural Networks for Feature Selection in Genetic Epidemiology.","authors":"Alison A Motsinger, David M Reif, Scott M Dudek, Marylyn D Ritchie","doi":"10.1109/CIBCB.2006.330945","DOIUrl":"10.1109/CIBCB.2006.330945","url":null,"abstract":"The identification of genetic factors/features that predict complex diseases is an important goal of human genetics. The commonality of gene-gene interactions in the underlying genetic architecture of common diseases presents a daunting analytical challenge. Previously, we introduced a grammatical evolution neural network (GENN) approach that has high power to detect such interactions in the absence of any marginal main effects. While the success of this method is encouraging, it elicits questions regarding the evolutionary process of the algorithm itself and the feasibility of scaling the method to account for the immense dimensionality of datasets with enormous numbers of features. When the features of interest show no main effects, how is GENN able to build correct models? How and when should evolutionary parameters be adjusted according to the scale of a particular dataset? In the current study, we monitor the performance of GENN during its evolutionary process using different population sizes and numbers of generations. We also compare the evolutionary characteristics of GENN to that of a random search neural network strategy to better understand the benefits provided by the evolutionary learning process-including advantages with respect to chromosome size and the representation of functional versus non-functional features within the models generated by the two approaches. Finally, we apply lessons from the characterization of GENN to analyses of datasets containing increasing numbers of features to demonstrate the scalability of the method.","PeriodicalId":88962,"journal":{"name":"Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"2006 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2006-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2903766/pdf/nihms-160134.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29127016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gene Expression Correlation and Gene Ontology-Based Similarity: An Assessment of Quantitative Relationships. 基因表达相关性和基于基因本体的相似性：定量关系的评估。

Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2004-10-07 DOI: 10.1109/CIBCB.2004.1393927

Haiying Wang, Francisco Azuaje, Olivier Bodenreider, Joaquín Dopazo

{"title":"Gene Expression Correlation and Gene Ontology-Based Similarity: An Assessment of Quantitative Relationships.","authors":"Haiying Wang, Francisco Azuaje, Olivier Bodenreider, Joaquín Dopazo","doi":"10.1109/CIBCB.2004.1393927","DOIUrl":"10.1109/CIBCB.2004.1393927","url":null,"abstract":"The Gene Ontology and annotations derived from the S. cerivisiae Genome Database were analyzed to calculate functional similarity of gene products. Three methods for measuring similarity (including a distance-based approach) were implemented. Significant, quantitative relationships between similarity and expression correlation of pairs of genes were detected. Using a known gene expression dataset in yeast, this study compared more than three million pairs of gene products on the basis of these functional properties. Highly correlated genes exhibit strong similarity based on information originating from the gene ontology taxonomies. Such a similarity is significantly stronger than that observed between weakly correlated genes. This study supports the feasibility of applying gene ontology-driven similarity methods to functional prediction tasks, such as the validation of gene expression analyses and the identification of false positives in protein interaction studies.","PeriodicalId":88962,"journal":{"name":"Proceedings of the ... IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology : CIBCB. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"2004 ","pages":"25-31"},"PeriodicalIF":0.0,"publicationDate":"2004-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4317290/pdf/nihms654692.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33039841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0