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10th Spanish symposium on bioinformatics for Personalized Medicine, JBI 2010

作者： Fernández, José M. Hoffmann, Robert Valencia, Alfonso CNIO Spain Structural Biology and Biocomputing Programme CNIO Spain Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY 10065 United States

ISBN: (纸本)9783642280610

iHOP provides fast, accurate, comprehensive, and up-to-date summary information on thousands of biological molecules by automatically extracting key sentences from millions of PubMed documents. iHOP web services are providing public programmatic access to all this information since their publication in 2007. This manuscript describes recent improvements on the iHOP web services family and some of the scenarios in which the web services have been applied. © 2012 Springer-Verlag.

关键词： Web services

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Weighted Markov Chain Based Aggregation of Biomolecule Orderings

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2012年第3期9卷 924-933页

作者： Sengupta, Debarka Maulik, Ujjwal Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit DST Sponsored Project Kolkata India Jadavpur Univ Dept Comp Sci Kolkata India Jadavpur Univ Dept Comp Sci & Technol Kolkata India

The scope and effectiveness of Rank Aggregation (RA) have already been established in contemporary bioinformatics research. Rank aggregation helps in meta-analysis of putative results collected from different analytic or experimental sources. For example, we often receive considerably differing ranked lists of genes or microRNAs from various target prediction algorithms or microarray studies. Sometimes combining them all, in some sense, yields more effective ordering of the set of objects. Also, assigning a certain level of confidence to each source of ranking is a natural demand of aggregation. Assignment of weights to the sources of orderings can be performed by experts. Several rank aggregation approaches like those based on Markov Chains (MCs), evolutionary algorithms, etc., exist in the literature. Markov chains, in general, are faster than the evolutionary approaches. Unlike the evolutionary computing approaches Markov chains have not been used for weighted aggregation scenarios. This is because of the absence of a formal framework of Weighted Markov Chain (WMC). In this paper, we propose the use of a modified version of MC4 (one of the Markov chains proposed by Dwork et al., 2001), followed by the weighted analog of local Kemenization for performing rank aggregation, where the sources of rankings can be prioritized by an expert. Effectiveness of the weighted Markov chain approach over the very recently proposed Genetic Algorithm (GA) and Cross-Entropy Monte Carlo (MC) algorithm-based techniques, has been established for gene orderings from microarray analysis and orderings of predicted microRNA targets.

关键词： Rank aggregation Markov chain Kendall's tau microRNA genes ordering

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De Novo Design of Potential RecA Inhibitors Using MultiObjective Optimization

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2012年第4期9卷 1139-1154页

作者： Sengupta, Soumi Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

De novo ligand design involves optimization of several ligand properties such as binding affinity, ligand volume, drug likeness, etc. Therefore, optimization of these properties independently and simultaneously seems appropriate. In this paper, the ligand design problem is modeled in a multiobjective using Archived MultiObjective Simulated Annealing (AMOSA) as the underlying search algorithm. The multiple objectives considered are the energy components similarity to a known inhibitor and a novel drug likeliness measure based on Lipinski's rule of five. RecA protein of Mycobacterium tuberculosis, causative agent of tuberculosis, is taken as the target for the drug design. To gauge the goodness of the results, they are compared to the outputs of LigBuilder, NEWLEAD, and Variable genetic algorithm (VGA). The same problem has also been modeled using a well-established genetic algorithm-based multiobjective optimization technique, Nondominated Sorting Genetic Algorithm-II (NSGA-II), to find the efficacy of AMOSA through comparative analysis. Results demonstrate that while some small molecules designed by the proposed approach are remarkably similar to the known inhibitors of RecA, some new ones are discovered that may be potential candidates for novel lead molecules against tuberculosis.

关键词： De novo ligand design multiobjective optimization genetic algorithm simulated annealing oral bioavailability Mycobacterium tuberculosis rational drug design

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An integrative bioinformatics approach for identifying subtypes and subtype-specific drivers in cancer

An integrative bioinformatics approach for identifying subty...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Peikai Chen Y. S. Hung Yubo Fan Stephen T.-C. Wong University of Hong Kong Hong Kong China Methodist Hospital Research Institute Houston TX USA

Cancer is a complex disease and within a cancer, subtypes of patients with distinct behaviors often exist. The subtypes might have been caused by different hits, such as copy number aberrations (CNAs) and point mutations, on different pathways/cells-of-origin in a common tissue/organ. Identifying the subtypes with subtype-specific drivers, i.e., hits, is key to the understanding of cancer and development of novel treatments. Here, we report the development of an integrative method to identify the subtypes of cancer. Specifically, we consider CNAs and their impact on gene expressions. Based on these relations, we propose an iterative approach that alternates between kernel based gene expression clustering and gene signature selection. We applied the method to datasets of the pediatric cancer medulloblastoma (MB). The consensus number of clusters quickly converges to three; and for each of these three subtypes, the signature detection also converges to a consistent set of a few hundred highly functionally related genes. For each of the subtypes, we correlate its signature with the set of within-subtype recurrent CNA-affected genes for identifying drivers. The top-ranked driver candidates are found to be enriched with known pathways in certain subtypes of MB as well as containing novel genes that might reveal new understandings for other subtypes.

关键词： Cancer Gene expression Smoothing methods Training Clustering algorithms Measurement Biological systems

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Utilizing stochastic model checking to analyze genetic circuits

Utilizing stochastic model checking to analyze genetic circu...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Curtis Madsen Chris J. Myers Nicholas Roehner Chris Winstead Zhen Zhang University of Utah Salt Lake UT USA Utah State University Logan UT USA

When designing and analyzing genetic circuits, researchers are often interested in the probability of the system reaching a given state within a certain amount of time. Usually, this involves simulating the system to produce some time series data and analyzing this data to discern the state probabilities. However, as the complexity of models of genetic circuits grow, it becomes more difficult for researchers to reason about the different states by looking only at time series simulation results of the models. To address this problem, this paper employs the use of stochastic model checking, a method for determining the likelihood that certain events occur in a system, with continuous stochastic logic (CSL) properties to obtain similar results. This goal is accomplished by the introduction of a methodology for converting a genetic circuit model (GCM) into a continuous-time Markov chain (CTMC). This CTMC is analyzed using transient Markov chain analysis to determine the likelihood that the circuit satisfies a given CSL property in a finite amount of time. This paper illustrates a use of this methodology to determine the likelihood of failure in a genetic toggle switch and compares these results to stochastic simulation-based analysis of this same circuit. Our results show that this method results in a substantial speedup as compared with conventional simulation-based approaches.

关键词： Integrated circuit modeling Genetics Stochastic processes Production Analytical models computational modeling Switches

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From Networks to Trees

From Networks to Trees

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6th International Conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Alves, Marco Alves, Joad Camacho, Rui Soares, Pedro Pereira, Luisa Univ Porto DEI Oporto Portugal Univ Porto IPATIM Inst Patol Imunol Mol Oporto Portugal Univ Porto LIAAD INESC Porto LA Lab Artificial Intelligence & Decision Support Oporto Portugal Univ Porto IPATIMUP Inst Patol Imunol Mol Fac Med Oporta Portugal

ISBN: (纸本)9783642288388

Phylogenetic networks are a useful way of displaying relationships between nucleotide or protein sequences. They diverge from phylogenetic trees as networks present cycles, several possible evolutionary histories of the sequences analysed, while a tree presents a single evolutionary relationship. Networks are especially useful in studying markers with a high level of homoplasy (same mutation happening more than once during evolution) like the control region of mitochondrial DNA (mtDNA), where the researcher does not need to compromise with a single explanation for the evolution suggested by the data. However in many instances, trees are required. One case where this happens is in the founder analysis methodology that aims at estimating migration times of human populations along history and prehistory. Currently, the founder analysis methodology implicates the creation of networks, from where a probable tree will be extracted by hand by the researcher, a time-consuming process, prone to errors and to the ambiguous decisions of the researcher. In order to automate the founder analysis methodology an algorithm that extracts a single probable tree from a network in a fast, systematic way is presented here.

关键词： Trees (mathematics)

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Pyicos: A flexible tool library for analyzing protein-nucleotide interactions with mapped reads from deep sequencing

Pyicos: A flexible tool library for analyzing protein-nucleo...

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10th Spanish symposium on bioinformatics for Personalized Medicine, JBI 2010

作者： González-Vallinas, Juan Althammer, Sonja Eyras, Eduardo Computational Genomics Group Universitat Pompeu Fabra PRBB Dr. Aiguader 88 E08003 Barcelona Spain Passeig Lluís Companys 23 08010 Barcelona Spain

ISBN: (纸本)9783642280610

Deep DNA or RNA sequencing and posterior mapping to a reference sequence is becoming a standard procedure in molecular biology research. Analyzing millions of mapped reads is a challenging task that doesn't have a unique solution, because experiments using deep sequencing technology vary a great deal among each other. This is why we have developed a flexible tool library called Pyicos, which aims to help biologists in their research when performing their analysis on mapped reads. © 2012 Springer-Verlag.

关键词： Genome

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bioinformatics Research and Applications - 8th International symposium, ISBRA 2012, Proceedings

Bioinformatics Research and Applications - 8th International...

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8th International symposium on bioinformatics Research and Applications, ISBRA 2012

ISBN: (纸本)9783642301902

The proceedings contain 28 papers. The topics discussed include: a mixed integer programming model for the parsimonious loss of heterozygosity problem;reconstruction of transcription regulatory networks by stability-based network component analysis;a transcript perspective on evolution;error propagation in sparse linear systems with peptide-protein incidence matrices;models and algorithmic tools for computational processes in cellular biology: recent developments and future directions;identifying rogue taxa through reduced consensus: NP-hardness and exact algorithms;GTP supertrees from unrooted gene trees: linear time algorithms for NNI based local searches;a Robinson-Foulds measure to compare unrooted trees with rooted trees;P-binder: a system for the protein-protein binding sites identification;and iterative piecewise linear regression to accurately assess statistical significance in batch confounded differential expression analysis.

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A non-Gaussian factor analysis approach to transcription Network Component Analysis

A non-Gaussian factor analysis approach to transcription Net...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Shikui Tu Dingsheng Luo Runsheng Chen Lei Xu Department of Computer Science and Engineering Chinese University of Hong Kong Sha Tin New Territories Hong Kong China Speech and Hearing Research Center Key Laboratory of Machine Perception (Ministry of Education) Peking University Beijing China Bioinformatics Laboratory and National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing China Speech and Hearing Research Center Key Laboratory of Machine Perception (Ministry of Education) Peking University China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong China

Transcription factor activities (TFAs), rather than expression levels, control gene expression and provide valuable information for investigating TF-gene regulations. Network Component Analysis (NCA) is a model based method to deduce TFAs and TF-gene control strengths from microarray data and a priori TF-gene connectivity data. We modify NCA to model gene expression regulation by non-Gaussian Factor Analysis (NFA), which assumes TFAs independently comes from Gaussian mixture densities. We properly incorporate a priori connectivity and/or sparsity on the mixing matrix of NFA, and derive, under Bayesian Ying-Yang (BYY) learning framework, a BYY-NFA algorithm that can not only uncover the latent TFA profile similar to NCA, but also is capable of automatically shutting off unnecessary connections. Simulation study demonstrates the effectiveness of BYY-NFA, and a preliminary application to two real world data sets shows that BYY-NFA improves NCA for the case when TF-gene connectivity is not available or not reliable, and may provide a preliminary set of candidate TF-gene interactions or double check unreliable connections for experimental verification.

关键词： Gene expression Sparse matrices Correlation Bayesian methods Educational institutions Electronic mail Analytical models

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A Biologically Inspired Validity Measure for Comparison of Clustering Methods over Metabolic Data Sets

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2012年第3期9卷 706-716页

作者： Stegmayer, Georgina Milone, Diego H. Kamenetzky, Laura Lopez, Mariana G. Carrari, Fernando Consejo Nacl Invest Cient & Tecn RA-3000 Santa Fe Argentina UTN FRSF Ctr Res & Dev Informat Syst CIDISI RA-3000 Santa Fe Argentina FICH UNL Res Ctr Signals Syst & Computat Intelligence RA-3000 Santa Fe Argentina Max Planck Inst Mol Plant Physiol INTA Inst Biotechnol Partner Grp Castelar Argentina

In the biological domain, clustering is based on the assumption that genes or metabolites involved in a common biological process are coexpressed/coaccumulated under the control of the same regulatory network. Thus, a detailed inspection of the grouped patterns to verify their memberships to well-known metabolic pathways could be very useful for the evaluation of clusters from a biological perspective. The aim of this work is to propose a novel approach for the comparison of clustering methods over metabolic data sets, including prior biological knowledge about the relation among elements that constitute the clusters. A way of measuring the biological significance of clustering solutions is proposed. This is addressed from the perspective of the usefulness of the clusters to identify those patterns that change in coordination and belong to common pathways of metabolic regulation. The measure summarizes in a compact way the objective analysis of clustering methods, which respects coherence and clusters distribution. It also evaluates the biological internal connections of such clusters considering common pathways. The proposed measure was tested in two biological databases using three clustering methods.

关键词： Clustering validation measure biological assessment metabolic pathways

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