2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献_第5页

De novo assembly of nucleotide sequences in a compressed feature space 压缩特征空间中核苷酸序列的从头组装

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058556

Avraam Tapinos, D. Robertson

引用次数: 1

Hybridization and ring optimization for larger sets of embeddable biomarkers 可嵌入生物标记物的杂交和环优化

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058532

D. Ashlock, S. Houghten

{"title":"Hybridization and ring optimization for larger sets of embeddable biomarkers","authors":"D. Ashlock, S. Houghten","doi":"10.1109/CIBCB.2017.8058532","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058532","url":null,"abstract":"Embeddable biomarkers are short strands of DNA that can be incorporated into genetic constructs to enable later identification. They are drawn from error correcting codes on the DNA alphabet relative to the Levenshtein metric. This study uses three types of evolutionary algorithms to improve the best known size of DNA error correcting codes, improving the bound for nine different code parameters. One of the algorithms is used on only one set of code parameters, correcting an oversight in an earlier study. The other two algorithms are a ring optimizer and a hybridizing evolutionary algorithm that exploits previously known codes. The ring optimizer improves two code size bounds and sets the stage for the hybridizer to improve four more. The hybridizer requires the results of a previous search as a starting point. Starting with known codes from earlier work, it improves a total of six bounds. The best results found by this algorithm used the results of the ring optimizer as a starting point. The paper discusses the issue of building a suite of cooperative code-search algorithms as a good target for future work.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"77 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":"114563824","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: 4

CVis — Towards a novel visualization tool to explore the relationship between input and output partitions in multi-objective clustering ensembles CVis -一种新的可视化工具，用于探索多目标聚类集成中输入和输出分区之间的关系

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058567

Katti Faceli, T. Sakata, J. Handl

{"title":"CVis — Towards a novel visualization tool to explore the relationship between input and output partitions in multi-objective clustering ensembles","authors":"Katti Faceli, T. Sakata, J. Handl","doi":"10.1109/CIBCB.2017.8058567","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058567","url":null,"abstract":"Ensemble methods for clustering take a collection of input partitions, produced for the same data set, and generate an ensemble partition that tries to preserve the information carried in this collective. Acceptance of the resulting partition(s) by decision makers can be a problem, due to the inherent complexity of ensemble techniques, and the associated lack of intuition on how a consensus has been derived from the original set of input partitions. This problem is exacerbated in multi-objective ensemble techniques, which generate a set of non-dominated consensus partitions. In this context, the selection of a final candidate clustering may require additional insight into the relationships between non-dominated output partitions. In this manuscript, we describe the first prototype of a novel visualization tool, CVis, which has been developed as a general tool to provide insight into the relationship between any set of partitions of a given data set. We proceed to demonstrate the specific use of this tool in understanding the relationship between the sets of input, the sets of outputs, and the input-output relationships for the multi-objective ensemble technique MOCLE. We discuss how the interlinked analysis of such sets of partitions can shed light onto the functioning, and the strengths and limitations of a particular ensemble technique. In particular, the tool facilitates the visual analysis of the level of support identified for individual consensus clusters, which is helpful in explaining final solutions to a decision maker.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"59 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":"120999880","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

An algebraic generalization for graph and tensor-based neural networks 基于图和张量的神经网络的代数推广

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058548

Ethan C. Jackson, J. Hughes, Mark Daley, M. Winter

{"title":"An algebraic generalization for graph and tensor-based neural networks","authors":"Ethan C. Jackson, J. Hughes, Mark Daley, M. Winter","doi":"10.1109/CIBCB.2017.8058548","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058548","url":null,"abstract":"Despite significant effort, there is currently no formal or de facto standard framework or format for constructing, representing, or manipulating general neural networks. In computational neuroscience, there have been some attempts to formalize connectionist notations and generative operations for neural networks, including Connection Set Algebra, but none are truly formal or general. In computational intelligence (CI), though the use of linear algebra and tensor-based models are widespread, graph-based frameworks are also popular and there is a lack of tools supporting the transfer of information between systems. To address these gaps, we exploited existing results about the connection between linear and relation algebras to define a concise, formal algebraic framework that generalizes graph and tensor-based neural networks. For simplicity and compatibility, this framework is purposefully defined as a minimal extension to linear algebra. We demonstrate the merits of this approach first by defining new operations for network composition along with proofs of their most important properties. An implementation of the algebraic framework is presented and applied to create an instance of an artificial neural network that is compatible with both graph and tensor based CI frameworks. The result is an algebraic framework for neural networks that generalizes the formats used in at least two systems, together with an example implementation.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 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":"129133576","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: 4

Modelling intracranial pressure with noninvasive physiological measures 用无创生理测量模拟颅内压

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058525

J. Hughes, Ethan C. Jackson, Mark Daley

{"title":"Modelling intracranial pressure with noninvasive physiological measures","authors":"J. Hughes, Ethan C. Jackson, Mark Daley","doi":"10.1109/CIBCB.2017.8058525","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058525","url":null,"abstract":"Patients who suffered a traumatic brain injury (TBI) require special care, and physicians often monitor intercranial pressure (ICP) as it can greatly aid in management. Although monitoring ICP can be critical, it requires neurosurgery, which presents additional significant risk. Monitoring ICP also aids in clinical situations beyond TBI, however the risk of neurosurgery can prevent physicians from gathering the data. The need for surgery may be eliminated if ICP could be accurately inferred using noninvasive physiological measures. Genetic programming (GP) and linear regression were used to develop nonlinear and linear mathematical models describing the relationships between intercranial pressure and a collection of physiological measurements from noninvasive instruments. Nonlinear models of ICP were generated that not only fit the subjects they were trained on, but generalized well across other subjects. The nonlinear models were analysed and provided insight into the studied underlying system which led to the creation of additional models. The new models were developed with a refined search, and were more accurate and general. It was also found that the relations between the features could be explained effectively with a simple linear model after GP refined the search.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"24 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":"130538547","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

Towards accurate de novo assembly for genomes with repeats 迈向精确的重复基因组从头组装

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058534

Doina Bucur

{"title":"Towards accurate de novo assembly for genomes with repeats","authors":"Doina Bucur","doi":"10.1109/CIBCB.2017.8058534","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058534","url":null,"abstract":"De novo genome assemblers designed for short k-mer length or using short raw reads are unlikely to recover complex features of the underlying genome, such as repeats hundreds of bases long. We implement a stochastic machine-learning method which obtains accurate assemblies with repeats and self-validates assemblies via consensus. For this, a prior assembler is extended with the ability to (a) assemble variable-length raw reads, which may span and unambiguously recover interspersed repeats in the genome, and (b) recognize long, direct terminal repeats during the assembly, then report an unambiguous circular assembly. Consensus is obtained via stochastically independent runs of the assembler on the same read library. We experiment on viral and mitochondrial genomes of up to 41 kbp, with synthetic raw-read libraries, to be able to evaluate the assembly against a reference. We show the prerequisites for obtaining accurate assemblies. For genomes with interspersed repeats, using raw reads of average length comparable to the repeat length likely gives an accurate genome. Genomes with long direct terminal repeats can be assembled accurately also with reads shorter than the repeat length. In both cases, a simple majority forms consensus, since over 70 % of independent runs on this set of genomes yield a correct assembly.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"117 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":"125472131","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: 1

Binning metagenomic reads with probabilistic sequence signatures based on spaced seeds 基于间隔种子的概率序列特征的宏基因组读取

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058538

Samuele Girotto, M. Comin, Cinzia Pizzi

{"title":"Binning metagenomic reads with probabilistic sequence signatures based on spaced seeds","authors":"Samuele Girotto, M. Comin, Cinzia Pizzi","doi":"10.1109/CIBCB.2017.8058538","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058538","url":null,"abstract":"The growing number of sequencing projects in medicine and environmental sciences calls for the development of efficient approaches for the analysis of very large sets of metagenomic reads. Among the challenging tasks in metagenomics, the ability to agglomerate, or “bin” together, reads of the same species, without reference genomes, plays a crucial role in building a comprehensive description of relative abundances and diversity of the species in the sample. Recently, we have proposed an algorithm, called MetaProb, for metagenomic reads binning that reaches a precision that is currently unmatched. The competitive advantage of MetaProb depends on the use of probabilistic sequence signatures based on contiguous fc-mers. In this work we explore the use of spaced seeds, rather than contiguous kmers, to build such signatures. The experimental results show that allowing mismatches in carefully chosen predefined positions leads to further benefits both in terms of improved accuracy and of reduction of the memory requirements. Availability: https://bitbucket.org/samu661/metaprob.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"105 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":"127333400","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

Finding optimal finite biological sequences over finite alphabets: The OptiFin toolbox 在有限字母上寻找最优的有限生物序列:OptiFin工具箱

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-06-25 DOI: 10.1109/CIBCB.2017.8058554

Régis Garnier, C. Guyeux, S. Chrétien

引用次数: 1