2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献_第3页

Modeling protein structural transitions as a multiobjective optimization problem 将蛋白质结构转变建模为多目标优化问题

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058536

Emmanuel Sapin, K. D. Jong, Amarda Shehu

{"title":"Modeling protein structural transitions as a multiobjective optimization problem","authors":"Emmanuel Sapin, K. D. Jong, Amarda Shehu","doi":"10.1109/CIBCB.2017.8058536","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058536","url":null,"abstract":"Proteins of importance to human biology can populate significantly different three-dimensional (3d) structures at equilibrium. By doing so, a protein is able to interface with different molecules in the cell and so modulate its function. A structure-by-structure characterization of a protein's transition between two structures is central to elucidate the role of structural dynamics in regulating molecular interactions, understand the impact of sequence mutations on function, and design molecular therapeutics. Much wet- and dry-laboratory research is devoted to characterizing structural transitions. Computational approaches rely on constructing a full or partial, structured representation of the energy landscape that organizes structures by potential energy. The representation readily yields one or more paths that consist of series of structures connecting start and goal structures of interest. In this paper, we propose instead to cast the problem of computing transition paths as a multiobjective optimization one. We identify two desired characteristics of computed paths, energetic cost and structural resolution, and propose a novel evolutionary algorithm (EA) to compute low-cost and highresolution paths. The EA evolves paths representing a specific structural excursion without a priori constructing the energy landscape. Preliminary applications suggest the EA is effective while operating under a reasonable computational budget.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127717861","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}

引用次数: 0

Marginalised stack denoising autoencoders for metagenomic data binning 用于宏基因组数据分组的边缘堆栈去噪自编码器

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058552

S. Kouchaki, Santosh Tirunagari, Avraam Tapinos, D. Robertson

引用次数: 2

A multivariate feature selection framework for high dimensional biomedical data classification 用于高维生物医学数据分类的多变量特征选择框架

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058528

Abeer Alzubaidi, G. Cosma

{"title":"A multivariate feature selection framework for high dimensional biomedical data classification","authors":"Abeer Alzubaidi, G. Cosma","doi":"10.1109/CIBCB.2017.8058528","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058528","url":null,"abstract":"High dimensional biomedical data are becoming common in various predictive models developed for disease diagnosis and prognosis. Extracting knowledge from high dimensional data which contain a large number of features and a small sample size presents intrinsic challenges for classification models. Genetic Algorithms can be successfully adopted to efficiently search through high dimensional spaces, and multivariate classification methods can be utilized to evaluate combinations of features for constructing optimized predictive models. This paper proposes a framework which can be adopted for building prediction models for high dimensional biomedical data. The proposed framework comprises of three main phases. The feature filtering phase which filters out the noisy features; the feature selection phase which is based on multivariate machine learning techniques and the Genetic Algorithm to evaluate the filtered features and select the most informative subsets of features for achieving maximum classification performance; and the predictive modeling phase during which machine learning algorithms are trained on the selected features to construct a reliable prediction model. Experiments were conducted using four high dimensional biomedical datasets including protein and geneexpression data. The results revealed optimistic performances for the multivariate selection approaches which utilize classification measurements based on implicit assumptions.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"11 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114966754","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}

引用次数: 3

Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: Beyond edge weights in psychological networks 基于全贝叶斯和图形套索方法的结构和参数不确定性:心理网络中超越边缘权重

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058566

G. Hullám, G. Juhász, J. Deakin, P. Antal

{"title":"Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: Beyond edge weights in psychological networks","authors":"G. Hullám, G. Juhász, J. Deakin, P. Antal","doi":"10.1109/CIBCB.2017.8058566","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058566","url":null,"abstract":"Uncertainty over model structures poses a challenge for many approaches exploring effect strength parameters at system-level. Monte Carlo methods for full Bayesian model averaging over model structures require considerable computational resources, whereas bootstrapped graphical lasso and its approximations offer scalable alternatives with lower complexity. Although the computational efficiency of graphical lasso based approaches has prompted growing number of applications, the restrictive assumptions of this approach are frequently ignored. We demonstrate using an artificial and a real-world example that full Bayesian averaging using Bayesian networks provides detailed estimates through posterior distributions for structural and parametric uncertainties and it is a feasible alternative, which is routinely applicable in mid-sized biomedical problems with hundreds of variables. We compare Bayesian estimates with corresponding frequentist quantities from bootstrapped graphical lasso using pairwise Markov Random Fields, discussing also their different interpretations. We present results using synthetic data from an artificial model and using the UK Biobank data set to construct a psychopathological network centered around depression (this research has been conducted using the UK Biobank Resource under Application Number 1602).","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133367417","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}

引用次数: 0

Infinite string block matching features for DNA classification DNA分类的无限串块匹配特征

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058529

D. Ashlock, Sierra Gillis, W. Ashlock

引用次数: 4

A note on population size inspired by the extinction of mammoths 由猛犸象灭绝启发的关于种群规模的注释

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058523

D. Ashlock, W. Ashlock

引用次数: 2

Hybrid feature selection method for autism spectrum disorder SNPs 自闭症谱系障碍snp的混合特征选择方法

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058526

R. Alzubi, N. Ramzan, Hadeel Alzoubi

{"title":"Hybrid feature selection method for autism spectrum disorder SNPs","authors":"R. Alzubi, N. Ramzan, Hadeel Alzoubi","doi":"10.1109/CIBCB.2017.8058526","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058526","url":null,"abstract":"Machine learning techniques have the potential to revolutionise medical diagnosis. Single Nucleotide Polymorphisms (SNPs) are one of the most important sources of human genome variability; thus, they have been implicated in several human diseases. To separate the affected samples from the normal ones, various techniques have been applied on SNPs. Achieving high classification accuracy in such a high-dimensional space is crucial for successful diagnosis and treatment. In this work, we propose an accurate hybrid feature selection method for detecting the most informative SNPs and selecting an optimal SNP subset. The proposed method is based on the fusion of a filter and a wrapper method, i.e. the Conditional Mutual Information Maximization (CMIM) method and the Support Vector Machine Recursive Feature Elimination (SVM-RFE) respectively. The performance of the proposed method was evaluated against three state-of-the-art feature selection methods; Minimum Redundancy Maximum Relevancy (mRMR), CMIM and ReliefF, using four classifiers, Support Vector Machine (SVM), Naive Bayes (NB), Linear Discriminant Analysis (LDA) and k Nearest Neighbors (k-NN) on Autism Spectrum Disorder(ASD) SNP dataset obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) genomics data repository. The experimental results demonstrate the efficiency of the adopted feature selection approach outperforming all of the compared feature selection algorithms and achieving up to 89% classification accuracy for the used dataset.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114590209","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}

引用次数: 10

Improving de novo protein structure prediction using contact maps information 利用接触图信息改进从头蛋白质结构预测

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058535

K. B. Santos, G. Rocha, F. L. Custódio, H. Barbosa, L. Dardenne

{"title":"Improving de novo protein structure prediction using contact maps information","authors":"K. B. Santos, G. Rocha, F. L. Custódio, H. Barbosa, L. Dardenne","doi":"10.1109/CIBCB.2017.8058535","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058535","url":null,"abstract":"The use of residue-residue contact maps in protein structure prediction (PSP) has proved promising during the last CASP editions (CASP10, 11 and 12). The goals of this work are to carry out an assessment of the information given by contact maps and to develop a strategy to use the contact constraints from these maps to improve the quality of the predicted models in a de novo PSP approach. A residue-residue potential, with information from contact maps, is proposed in the form of distance constraints. This potential is added to the fitness function of the GAPF program, which predicts protein structures using a genetic algorithm with phenotypic crowding in a free-modeling approach. Two contact maps were generated to evaluate the potential developed here: (i) a native contact map obtained directly from the experimental structure and, (ii) a filtered contact map with only the native contacts present in a map predicted by MetaPSICOV. The experiments performed indicate that the contact potential implemented in the GAPF program promoted an important improvement in the accuracy of the predictions, confirming the use of contact maps as a useful strategy for de novo PSP methodologies. Our results also stress the need to develop better strategies to filter and enhance the information of predicted contacts.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127837134","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}

引用次数: 2

Topological, functional, and structural analyses of protein-protein interaction networks of breast cancer lung and brain metastases 乳腺癌肺和脑转移的蛋白-蛋白相互作用网络的拓扑、功能和结构分析

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058539

Farideh Halakou, Attila Gürsoy, Emel Sen Kilic, O. Keskin

{"title":"Topological, functional, and structural analyses of protein-protein interaction networks of breast cancer lung and brain metastases","authors":"Farideh Halakou, Attila Gürsoy, Emel Sen Kilic, O. Keskin","doi":"10.1109/CIBCB.2017.8058539","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058539","url":null,"abstract":"Breast cancer is the second most common cause of death among women. However, it is not deadly if the cancerous cells remain in the breast. The life threat starts when cancerous cells travel to other parts of body like lung, liver, bone and brain. So, most breast cancer deaths derive from metastasis to other organs. In this study, we introduce novel proteins and cellular pathways that play important roles in brain and lung metastases of breast cancer using Protein-Protein Interaction (PPI) networks. Our topological analysis identified genes such as RPL5, MMP2 and DPP4 which are already known to be associated with lung or brain metastasis. Additionally, we found four and nine novel candidate genes that are specific to lung and brain metastases, respectively. The functional enrichment analysis showed that KEGG pathways associated with the immune system and infectious diseases, particularly the chemokine signaling pathway, are important for lung metastasis. On the other hand, pathways related to genetic information processing were more involved in brain metastasis. By enriching the traditional PPI network with protein structural data, we show the effects of mutations on specific protein-protein interactions. By using the different conformations of protein CXCL12, we show the effect of H25R mutation on CXCL12 dimerization.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122728638","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}

引用次数: 2

Positome: A method for improving protein-protein interaction quality and prediction accuracy 正体:一种提高蛋白质相互作用质量和预测精度的方法

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058545

K. Dick, F. Dehne, A. Golshani, J. Green

{"title":"Positome: A method for improving protein-protein interaction quality and prediction accuracy","authors":"K. Dick, F. Dehne, A. Golshani, J. Green","doi":"10.1109/CIBCB.2017.8058545","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058545","url":null,"abstract":"The progressive elucidation of positive protein-protein interactions (PPIs) as wet-lab techniques continue to improve in both throughput and precision has increased the number and quality of known PPIs across the spectrum of life. Creating high quality datasets of positive PPIs is critical for training PPI prediction algorithms and for assessing the performance of PPI detection efforts. We present the Positome, a web service to acquire sets of positive PPIs based on user-defined criteria pertaining to data provenance including interaction type, throughput level, and detection method selection in addition to filtration by multiple lines of evidence (i.e. PPIs reported by independent research groups). The Positome provides a tunable interface to obtain a specified subset of interacting PPIs from the BioGRlD database. Both intra- and inter-species PPIs are supported. Using a number of model organisms, we demonstrate the trade-off between data quality and quantity, and the benefit of higher data quality on PPI prediction precision and recall. A web interface and REST web service are available at http://bioinf.sce.carleton.ca/POSITOME/.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359447","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}

引用次数: 9