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NanoBioscience, IEEE Transactions on

Issue 3 • Date Sept. 2013

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Displaying Results 1 - 21 of 21
  • Table of Contents

    Publication Year: 2013 , Page(s): C1
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  • IEEE Transactions on NanoBioscience publication information

    Publication Year: 2013 , Page(s): C2
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  • Guest Editorial Special Section on the 2012 IEEE Conference on Bioinformatics and Biomedicine (BIBM)

    Publication Year: 2013 , Page(s): 141
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  • Identifying Context-Specific Transcription Factor Targets From Prior Knowledge and Gene Expression Data

    Publication Year: 2013 , Page(s): 142 - 149
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1062 KB) |  | HTML iconHTML  

    Numerous methodologies, assays, and databases presently provide candidate targets of transcription factors (TFs). However, TFs rarely regulate their targets universally. The context of activation of a TF can change the transcriptional response of targets. Direct multiple regulation typical to mammalian genes complicates direct inference of TF targets from gene expression data. We present a novel statistic that infers context-specific TF regulation based upon the CoGAPS algorithm, which infers overlapping gene expression patterns resulting from coregulation. Numerical experiments with simulated data showed that this statistic correctly inferred targets that are common to multiple TFs, except in cases where the signal from a TF is negligible relative to noise level and signal from other TFs. The statistic is robust to moderate levels of error in the simulated gene sets, identifying fewer false positives than false negatives. Significantly, the regulatory statistic refines the number of TF targets relevant to cell signaling in gastrointestinal stromal tumors (GIST) to genes consistent with the phosphorylation patterns of TFs identified in previous studies. As formulated, the proposed regulatory statistic has wide applicability to inferring set membership in integrated datasets. This statistic could be naturally extended to account for prior probabilities of set membership or to add candidate gene targets. View full abstract»

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  • Adjusting for Background Mutation Frequency Biases Improves the Identification of Cancer Driver Genes

    Publication Year: 2013 , Page(s): 150 - 157
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (483 KB) |  | HTML iconHTML  

    A common goal of tumor sequencing projects is finding genes whose mutations are selected for during tumor development. This is accomplished by choosing genes that have more non-synonymous mutations than expected from an estimated background mutation frequency. While this background frequency is unknown, it can be estimated using both the observed synonymous mutation frequency and the non-synonymous to synonymous mutation ratio. The synonymous mutation frequency can be determined across all genes or in a gene-specific manner. This choice introduces an interesting trade-off. A gene-specific frequency adjusts for an underlying mutation bias, but is difficult to estimate given missing synonymous mutation counts. Using a genome-wide synonymous frequency is more robust, but is less suited for adjusting biases. Studying four evaluation criteria for identifying genes with high non-synonymous mutation burden (reflecting preferential selection of expressed genes, genes with mutations in conserved bases, genes with many protein interactions, and genes that show loss of heterozygosity), we find that the gene-specific synonymous frequency is superior in the gene expression and protein interaction tests. In conclusion, the use of the gene-specific synonymous mutation frequency is well suited for assessing a gene's non-synonymous mutation burden. View full abstract»

    Open Access
  • Predicting Interacting Residues Using Long-Distance Information and Novel Decoding in Hidden Markov Models

    Publication Year: 2013 , Page(s): 158 - 164
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (864 KB) |  | HTML iconHTML  

    Identification of interacting residues involved in protein-protein and protein-ligand interaction is critical for the prediction and understanding of the interaction and has practical impact on mutagenesis and drug design. In this work, we introduce a new decoding algorithm, ETB-Viterbi, with an early traceback mechanism, and apply it to interaction profile hidden Markov models (ipHMMs) to enable optimized incorporation of long-distance correlations between interacting residues, leading to improved prediction accuracy. The method was applied and tested to a set of domain-domain interaction families from the 3DID database, and showed statistically significant improvement in accuracy measured by F-score. To gauge and assess the method's effectiveness and robustness in capturing the correlation signals, sets of simulated data based on the 3DID dataset with controllable correlation between interacting residues were also used, as well as reversed sequence orientation. It was demonstrated that the prediction consistently improves as the correlations increase and is not significantly affected by sequence orientation. View full abstract»

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  • Identifying Protein Complexes Based on Multiple Topological Structures in PPI Networks

    Publication Year: 2013 , Page(s): 165 - 172
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1361 KB) |  | HTML iconHTML  

    Various computational algorithms are developed to identify protein complexes based on only one of specific topological structures in protein-protein interaction (PPI) networks, such as cliques, dense subgraphs, core-attachment structures and starlike structures. However, protein complexes exhibit intricate connections in a PPI network. They cannot be fully detected by only single topological structure. In this paper, we propose an algorithm based on multiple topological structures to identify protein complexes from PPI networks. In the proposed algorithm, four single topological structure based algorithms are first employed to identify raw predictions with specific topological structures, respectively. Those raw predictions are trimmed according to their topological information or GO annotations. Similar results are carefully merged before generating final predictions. Numerical experiments are conducted on a yeast PPI network of DIP and a human PPI network of HPRD. The predicted results show that the multiple topological structure based algorithm can not only obtain a more number of predictions, but also generate results with high accuracy in terms of f-score, matching with known protein complexes and functional enrichments with GO. View full abstract»

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  • PPIExtractor: A Protein Interaction Extraction and Visualization System for Biomedical Literature

    Publication Year: 2013 , Page(s): 173 - 181
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1047 KB) |  | HTML iconHTML  

    Protein-protein interactions (PPIs) play a key role in various aspects of the structural and functional organization of the cell. Knowledge about them unveils the molecular mechanisms of biological processes. However, the amount of biomedical literature regarding protein interactions is increasing rapidly and it is difficult for interaction database curators to detect and curate protein interaction information manually. In this paper, we present a PPI extraction system, termed PPIExtractor, which automatically extracts PPIs from biomedical text and visualizes them. Given a Medline record dataset, PPIExtractor first applies Feature Coupling Generalization (FCG) to tag protein names in text, next uses the extended semantic similarity-based method to normalize them, then combines feature-based, convolution tree and graph kernels to extract PPIs, and finally visualizes the PPI network. Experimental evaluations show that PPIExtractor can achieve state-of-the-art performance on a DIP subset with respect to comparable evaluations. PPIExtractor is freely available for academic purposes at: http://202.118.75.18:8080/PPIExtractor/. View full abstract»

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  • Drug-Domain Interaction Networks in Myocardial Infarction

    Publication Year: 2013 , Page(s): 182 - 188
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1324 KB) |  | HTML iconHTML  

    It has been well recognized that the pace of the development of new drugs and therapeutic interventions lags far behind biological knowledge discovery. Network-based approaches have emerged as a promising alternative to accelerate the discovery of new safe and effective drugs. Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was firstly constructed, followed by topological analysis and functional characterization of the network. The results show that My-DDome has a very clear modular structure, where drugs interacting with the same domain(s) within each module tend to have similar therapeutic effects. Moreover it has been found that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome , indicating that by incorporating protein domain information into My-DTome, more detailed insights into the interplay between drugs, their known targets, and seemingly unrelated proteins can be revealed. View full abstract»

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  • A Local Poisson Graphical Model for Inferring Networks From Sequencing Data

    Publication Year: 2013 , Page(s): 189 - 198
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1753 KB) |  | HTML iconHTML  

    Gaussian graphical models, a class of undirected graphs or Markov Networks, are often used to infer gene networks based on microarray expression data. Many scientists, however, have begun using high-throughput sequencing technologies such as RNA-sequencing or next generation sequencing to measure gene expression. As the resulting data consists of counts of sequencing reads for each gene, Gaussian graphical models are not optimal for this discrete data. In this paper, we propose a novel method for inferring gene networks from sequencing data: the Local Poisson Graphical Model. Our model assumes a Local Markov property where each variable conditional on all other variables is Poisson distributed. We develop a neighborhood selection algorithm to fit our model locally by performing a series of l1 penalized Poisson, or log-linear, regressions. This yields a fast parallel algorithm for estimating networks from next generation sequencing data. In simulations, we illustrate the effectiveness of our methods for recovering network structure from count data. A case study on breast cancer microRNAs (miRNAs), a novel application of graphical models, finds known regulators of breast cancer genes and discovers novel miRNA clusters and hubs that are targets for future research. View full abstract»

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  • Comparison of Dimensional Reduction Methods for Detecting and Visualizing Novel Patterns in Human and Marine Microbiome

    Publication Year: 2013 , Page(s): 199 - 205
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (801 KB) |  | HTML iconHTML  

    Using metagenomics to detect the global structure of microbial community remains a significant challenge. The structure of a microbial community and its functions are complicated because of not only the complex interactions among microbes but also their interactions with confounding environmental factors. Recently dimension reduction methods have been employed extensively to investigate the complex structure embedded in metagenomic profiles which summarize the abundance of functional or taxonomic categorizations in metagenomic studies. However, metagenomic profiles are not necessary to meet the “Assumption of Linearity” behind these methods. Therefore it is worth to investigate whether nonlinear methods are appropriate methods which can be utilized in metagenomic analysis. In this paper, we compare the applications of several methods, including two linear methods (Principle component analysis and nonnegative matrix factorization) and a nonlinear manifold learning method-Isomap on visualizing and analyzing metagenomic profiles. These methods are applied and compared on a taxonomic profile from 33 human gut metagenomes and a large-scale Pfam profile which are derived from 45 metagenomes in Global Ocean Sampling expedition. We find that all three methods can discover interesting structures of the taxonomic profile from human gut. Furthermore, Isomap identified a novel nonlinear structure of protein families. The relationships among the identified nonlinear components and environmental factors of global ocean are explored. The results indicate that nonlinear methods could be a complementary technique to current linear methods in analyzing metagenomic dataset. View full abstract»

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  • Predicting Drug-Induced QT Prolongation Effects Using Multi-View Learning

    Publication Year: 2013 , Page(s): 206 - 213
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (867 KB) |  | HTML iconHTML  

    Drug-induced QT prolongation is a major life-threatening adverse drug effect. It is crucial to predict the QT prolongation effect as early as possible in drug development, however, data on drugs that induce QT prolongation are very limited and noisy. Multi-view learning (MVL) has been applied to many challenging machine learning and data mining problems, especially when complex data from diverse domains are involved and only limited labeled examples are available. Unlike existing MVL methods that use l2-norm co-regularization to obtain a smooth objective function, in this paper we proposed an l1-norm co-regularized MVL algorithm for predicting drug-induced QT prolongation effect and reformulate the l1-norm co-regularized objective function for deriving its gradient in the analytic form, and we can optimize the mapping functions on all views simultaneously and achieve 3-4 times higher computational efficiency, while previous l2-norm co-regularized MVL methods use alternate optimization that alternately optimizes one view with the other views fixed until convergence. l1-norm co-regularization enforces sparsity in the learned mapping functions and hence the results are expected to be more interpretable. Comprehensive experimental comparisons between our proposed method and previous MVL and single-view learning methods demonstrate that our method significantly outperforms those baseline methods more efficiently. View full abstract»

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  • Continuous Wavelet Transform Based Partial Least Squares Regression for Quantitative Analysis of Raman Spectrum

    Publication Year: 2013 , Page(s): 214 - 221
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1812 KB) |  | HTML iconHTML  

    Quantitative analysis of Raman spectra using surface-enhanced Raman scattering (SERS) nanoparticles has shown the potential and promising trend of development in in vivo molecular imaging. Partial least square regression (PLSR) methods have been reported as state-of-the-art methods. However, the approaches fully rely on the intensities of Raman spectra and can not avoid the influences of the unstable background. In this paper we design a new continuous wavelet transform based PLSR (CWT-PLSR) algorithm that uses mixing concentrations and the average CWT coefficients of Raman spectra to carry out PLSR. We elaborate and prove how the average CWT coefficients with a Mexican hat mother wavelet are robust representations of Raman peaks, and the method can reduce the influences of unstable baseline and random noises during the prediction process. The algorithm was tested using three Raman spectra data sets with three cross-validation methods in comparison with current leading methods, and the results show its robustness and effectiveness. View full abstract»

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  • Optical Capsule and Tweezer Array for Molecular Motor Use

    Publication Year: 2013 , Page(s): 222 - 227
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1419 KB) |  | HTML iconHTML  

    A generation of optical capsules and tweezers within a modified optical add-drop filter known as PANDA ring resonator with a new concept is proposed. By using dark and bright solitons, the orthogonal tweezers can be formed within the system and observed simultaneously at the output ports. Under the resonant condition, the optical capsules and tweezers generated by dark and bright soliton pair corresponding to the left-hand and right-hand rotating solitons (tweezers) can be generated. When a soliton is interacted by an object, an angular momentum of either bright or dark tweezers is imparted to the object, in which two possible spin states known as tweezer spins are exhibited. Furthermore, an array of molecular capsules and spins, i.e., trapped molecules can be generated and detected by using the proposed system, which can be used to form large scale tweezer or capsule spins. In application, the trapped molecules can be moved and rotated securely to the required destinations, which can be useful for many applications, especially, in medical diagnosis, therapy and surgery. View full abstract»

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  • Optical Spins and Nano-Antenna Array for Magnetic Therapy

    Publication Year: 2013 , Page(s): 228 - 232
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (909 KB) |  | HTML iconHTML  

    Magnetic therapy is an alternative medicine practice involving the use of magnetic fields subjected to certain parts of the body and stimulates healing from a range of health problems. In this paper, an embedded nano-antenna system using the optical spins generated from a particular configuration of microrings (PANDA) is proposed. The orthogonal solitons pairs corresponding to the left-hand and right-hand optical solitons (photons) produced from dark-bright soliton conversion can be simultaneously detected within the system at the output ports. Two possible spin states which are assigned as angular momentum of either +ħ or -ħ will be absorbed by an object whenever this set of orthogonal solitons is imparted to the object. Magnetic moments could indeed arise from the intrinsic property of spins. By controlling some important parameters of the system such as soliton input power, coupling coefficients and sizes of rings, output signals from microring resonator system can be tuned and optimized to be used as magnetic therapy array. View full abstract»

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  • Mega-Nano Detection of Foodborne Pathogens and Transgenes Using Molecular Beacon and Semiconductor Quantum Dot Technologies

    Publication Year: 2013 , Page(s): 233 - 238
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (630 KB) |  | HTML iconHTML  

    Signature molecules derived from Listeria monocytogenes, Bacillus thuringiensis, and Salmonella Typhimurium were detected directly on food substrates (mega) by coupling molecular beacon technology utilizing fluorescent resonance energy transfer (FRET), luminescent nanoscale semiconductor quantum dots, and nanoscale quenchers. We designed target DNA sequences for detecting hlyA, Bt cry1Ac, and invA genes from L. monocytogenes, B. thuringiensis and Salmonella Typhimurium, respectively, and prepared molecular beacons for specific targets for use in real-time monitoring. We successfully detected increased fluorescence in the presence of signature molecules at molecular beacon (MB) concentrations from 1.17 nM to 40 nM, depending upon system tested in (water, milk or plant leaves), respective target (hlyA, Bt cry1Ac, or invA) and genomic DNA target concentration (50-800 ng). We were able to detect bacterial genomic DNA derived from L. monocytogenes and Salmonella sp. in a food system, 2% milk (> 20% of total volume). Furthermore, we infiltrated the Bt cry1Ac beacon in the presence of genomic DNA extracted from B. thuringiensis into Arabidopsis thaliana leaves and observed increased fluorescence in the presence of the target, indicating the ability to use these beacons in a plant system. View full abstract»

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  • Extended Electrical Model for Impedance Characterization of Cultured HeLa Cells in Non-Confluent State Using ECIS Electrodes

    Publication Year: 2013 , Page(s): 239 - 246
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1305 KB) |  | HTML iconHTML  

    Electric cell substrate impedance sensing has been widely used as a label free quantitative platform to study various cell biological processes and it is extremely essential to extract the parameters like the variation of the cell substrate spacing, changing projected area of the cell on the electrode and approximate cluster size during the non-confluent state to understand the mechanism of proliferation of the cells. The distributed analytical models developed so far to extract these parameters are applicable only under the conditions when the cells have become confluent. There are some lumped electrical models which have been reported for the non-confluent state but they do not provide correct estimate of the changing cell substrate spacing and the cell cluster size during growth. In this paper we develop extended distributed electrical models to characterize the impedance spectroscopy behavior of cultured HeLa cells in 200 Hz to 1 MHz range using eight well ECIS electrodes in the non-confluent state. The distributed model introduces some pseudo regularity in the arrangement of the non-confluent cells to extract the average ensemble of the significant parameters. The parameters extracted from the distributed model after 10 hours, 20 hours, and 30 hours of HeLa cell growth have been compared with the lumped circuit model and has been observed to fit the experimental data with a seven times improved fit quality factor. Further, the changing cell radius and cluster radius extracted at three different instants of time from the distributed analytical model have been found to match closely the microscopic observation in contrast to the lumped circuit model. View full abstract»

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  • Cell Interactions at the Nanoscale: Piezoelectric Stimulation

    Publication Year: 2013 , Page(s): 247 - 254
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (941 KB) |  | HTML iconHTML  

    Nanometric movements of the substrate on which endothelial cells are growing, driven by periodic sinusoidal vibration from 1 Hz to 50 Hz applied by piezo actuators, upregulate endothelin-1 and Kruppel-like factor 2 expression, and increase cell adhesion. These movements are in the z (vertical) axis and ranges from 5 to 50 nm and are similar in vertical extent to protrusions from the cells themselves already reported in the literature. White noise vibrations do not to produce these effects. Vibrational sweeps, if suitably confined within a narrow frequency range, produce similar stimulatory effects but not at wider sweeps. These effects suggest that coherent vibration is crucial for driving these cellular responses. In addition to this, the applied stimulations are observed to be close to or below the random seismic noise of the surroundings, which may suggest stochastic resonance is being employed. The stimulations also interact with the effects of nanometric patterning of the substrates on cell adhesion and Kruppel-like factor 2 and endothelin-1 expression thus linking cell reactions to nanotopographically patterned surfaces with those to mechanical stimulation. View full abstract»

    Open Access
  • Normal Forms for Some Classes of Sequential Spiking Neural P Systems

    Publication Year: 2013 , Page(s): 255 - 264
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1990 KB) |  | HTML iconHTML  

    Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes, where each neuron can have several spiking rules and forgetting rules and neurons work in parallel in the sense that each neuron that can fire should fire at each computation step. In this work, we consider SN P systems with the restrictions: 1) systems are simple (resp. almost simple) in the sense that each neuron has only one rule (resp. except for one neuron); 2) at each step the neuron(s) with the maximum number of spikes among the neurons that can spike will fire. These restrictions correspond to that the systems are simple or almost simple and a global view of the whole network makes the systems sequential. The computation power of simple SN P systems and almost simple SN P systems working in the sequential mode induced by maximum spike number is investigated. Specifically, we prove that such systems are Turing universal as both number generating and accepting devices. The results improve the corresponding ones in Theor. Comput. Sci., 410 (2009), 2982-2991. View full abstract»

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  • IEEE Transactions on NanoBioscience information for authors

    Publication Year: 2013 , Page(s): C3
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  • [Blank page - back cover]

    Publication Year: 2013 , Page(s): C4
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Aims & Scope

The IEEE Transactions on NanoBioscience publishes basic and applied papers dealing both with engineering, physics, chemistry, modeling and computer science and with biology and medicine with respect to molecules, cells, tissues. The content of acceptable papers ranges from practical/clinical/environmental applications to formalized mathematical theory.

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Meet Our Editors

Editor-in-Chief
Henry Hess
Department of Biomedical Engineering
Columbia University