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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Jeremy D. Scheff Ioannis P. Androulakis Steve E. Calvano Stephen F. Lowry Department of Biomedical Engineering Rutgers University Piscataway NJ USA Department of Surgery Robert Wood Johnson Medical School University of Medicine and Dentistry of New Jersey New Brunswick NJ USA

Due to the inherent complexities involved in understanding a redundant, non-linear process like inflammation, systems biology modeling approaches have been proposed. While these models have produced encouraging results, they do not take into account the circadian nature of many of their components. Circadian rhythms are daily variations in a wide number of biological processes, including many of the hormones and cytokines responsible for the regulation of inflammation. Thus, to work towards the development of a more complete and useful model of inflammation, we extend previous modeling efforts by incorporating important interactions that give rise to circadian variability in inflammation. This is predicated on the assumption that hormones regulated by the central circadian pacemaker ultimately govern diurnal variations in the inflammatory response. The model is then used to simulate a normal resting state, a healthy self-limited inflammatory response, and an unresolved inflammatory response, by analyzing how these responses change throughout the day, clinically-relevant insight is gained.

关键词： Circadian rhythm Biological system modeling Power system modeling Biochemistry Mathematical model Systems biology Biomedical engineering USA Councils Biological processes computational modeling

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Identification of Full and Partial Class Relevant Genes

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2010年第2期7卷 263-277页

作者： Zhu, Zexuan Ong, Yew-Soon Zurada, Jacek M. Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Univ Louisville Dept Elect & Comp Engn Computat Intelligence Lab Louisville KY 40292 USA

Multiclass cancer classification on microarray data has provided the feasibility of cancer diagnosis across all of the common malignancies in parallel. Using multiclass cancer feature selection approaches, it is now possible to identify genes relevant to a set of cancer types. However, besides identifying the relevant genes for the set of all cancer types, it is deemed to be more informative to biologists if the relevance of each gene to specific cancer or subset of cancer types could be revealed or pinpointed. In this paper, we introduce two new definitions of multiclass relevancy features, i.e., full class relevant (FCR) and partial class relevant (PCR) features. Particularly, FCR denotes genes that serve as candidate biomarkers for discriminating all cancer types. PCR, on the other hand, are genes that distinguish subsets of cancer types. Subsequently, a Markov blanket embedded memetic algorithm is proposed for the simultaneous identification of both FCR and PCR genes. Results obtained on commonly used synthetic and real-world microarray data sets show that the proposed approach converges to valid FCR and PCR genes that would assist biologists in their research work. The identification of both FCR and PCR genes is found to generate improvement in classification accuracy on many microarray data sets. Further comparison study to existing state-of-the-art feature selection algorithms also reveals the effectiveness and efficiency of the proposed approach.

关键词： bioinformatics microarray multiclass cancer classification feature/gene selection memetic algorithm Markov blanket

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2006 ieee symposium on computational intelligence in bioinformatics and computational biology

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ieee Transactions on Neural Networks 2006年第3期17卷 827-827页

Provides notice of upcoming conference events of interest to practitioners and researchers.

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2006 ieee symposium on computational intelligence in bioinformatics and computational biology

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ieee Transactions on Evolutionary Computation 2006年第2期10卷 210-210页

Provides notice of upcoming conference events of interest to practitioners and researchers.

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2006 ieee symposium on computational intelligence in bioinformatics and computational biology

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ieee Transactions on Evolutionary Computation 2006年第1期10卷 101-101页

Provides notice of upcoming conference events of interest to practitioners and researchers.

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Ethanol Effects on Transcription Factor Network Regulating Stem Cell Differentiation

Ethanol Effects on Transcription Factor Network Regulating S...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Rajanikanth Vadigepalli Joshua Ogony Helen Anni Department of Pathology Anatomy and Cell Biology Thomas Jefferson University Philadelphia USA

This study probes the effects of ethanol on the molecular mechanisms regulating the differentiation of embryonic stem (ES) cells towards neuroectodermal state, which may be responsible for the abnormalities observed in fetal alcohol spectrum disorders (FASD). The effects of ethanol on the early phase of ES cell differentiation have not been well characterized. Here, we investigate the stage-specific action of ethanol during early embryogenesis by an integrated experimental and computational modeling approach. Our experimental system consists of mouse ES cells and directed differentiation to neuroectodermal fate in the presence of ethanol. Experimental single-cell multiplex data on the expression of the ES core transcription factors (TFs), Sox2, Oct4 and Nanog were obtained simultaneously by multicolor flow cytometry in live cells. Single-cell flow cytometric data were analyzed by ARACNE probabilistic modeling to construct transcriptional regulatory networks and quantify the TFs interactions in a pairwisemanner. Our analysis indicates that during differentiation towards neuroectodermal fate ethanol accelerates (i) the decline of the expression levels of Sox2 and Nanog, and (ii) the decreasing strength of the correlative interactions between the core TFs which is also reflected in (iii) an advanced differentiation phenotype.

关键词： Ethanol Stem cells Embryo Birth disorders Mice Nanobioscience Acceleration Fluid flow measurement Fluorescence bioinformatics

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Identifying Hidden Confounders in Gene Networks by Bayesian Networks

Identifying Hidden Confounders in Gene Networks by Bayesian ...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Tomoya Higashigaki Kaname Kojima Rui Yamaguchi Masato Inoue Seiya Imoto Satoru Miyano Department of Electrical Engineering and Bioscience Waseda University Shinjuku Tokyo Japan Human Genome Center Institute of Medical Science University of Tokyo Minato Tokyo Japan

In the estimation of gene networks from microarray gene expression data, we propose a statistical method for quantification of the hidden confounders in gene networks, which were possibly removed from the set of genes on the gene networks or are novel biological elements that are not measured by microarrays. Due to high computational cost of the structural learning of Bayesian networks and the limited source of the microarray data, it is usual to perform gene selection prior to the estimation of gene networks. Therefore, there exist missing genes that decrease accuracy and interpretability of the estimated gene networks. The proposed method can identify hidden confounders based on the conflicts of the estimated local Bayesian network structures and estimate their ideal profiles based on the proposed Bayesian networks with hidden variables with an EM algorithm. From the estimated ideal profiles, we can identify genes which are missing in the network or suggest the existence of the novel biological elements if the ideal profiles are not significantly correlated with any expression profiles of genes. To the best of our knowledge, this research is the first study to theoretically characterize missing genes in gene networks and practically utilize this information to refine network estimation.

关键词： Bayesian methods Gene expression bioinformatics Humans Genomics Statistical analysis Testing biology computing Computer networks Biomedical engineering

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Resolving clustered worms via probabilistic shape models

Resolving clustered worms via probabilistic shape models

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ieee International symposium on Biomedical Imaging

作者： Carolina Wählby Tammy Riklin-Raviv Vebjorn Ljosa Annie L. Conery Polina Golland Frederick M. Ausubel Anne E. Carpenter Imaging Platform Broad Institute of MIT and Harvard Cambridge MA USA Computer Science and Artificial Intelligence Laboratory MIT Cambridge MA USA Department of Molecular Biology and Center for Computational and Integrative Biology MGH Boston MA USA

ISBN: (纸本)9781424441259;9781424441266

The roundworm Caenorhabditis elegans is an effective model system for biological processes such as immunity, behavior, and metabolism. Robotic sample preparation together with automated microscopy and image analysis has recently enabled high-throughput screening experiments using C. elegans. So far, such experiments have been limited to per-image measurements due to the tendency of the worms to cluster, which prevents extracting features from individual animals. We present a novel approach for the extraction of individual C. elegans from clusters of worms in high-throughput microscopy images. The key ideas are the construction of a low-dimensional shape-descriptor space and the definition of a probability measure on it. Promising segmentation results are shown.

关键词： Shape Biological system modeling Microscopy Biological processes Immune system Biochemistry Robotics and automation Image analysis Feature extraction Animals

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Towards Temperature Dependent Coarse-grained Potential of Side-chain Interactions for Protein Folding Simulations

Towards Temperature Dependent Coarse-grained Potential of Si...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Stanisław Ołdziej Cezary Czaplewski Adam Liwo Harold A. Scheraga Laboratory of Biopolymer Structure Intercollegiate Faculty of Biotechology Medical University of Gdańsk ul GdaDsk Poland Intercollegiate Faculty of Biotechology Cornell University Ithaca NY Poland Faculty of Chemistry University of Gdańsk GdaDsk Poland Faculty of Chemistry Cornell University Ithaca NY Poland Baker Laboratory of Chemistry and Chemical Biology Cornell University Ithaca NY USA

Based on the results of our recent work on the determination of the potentials of mean force of pairs of models of amino-acid side chains in water, in this work we make an attempt at introducing temperature-dependent side chain - side chain interaction potentials in our coarse-grained UNRES energy function. For hydrophobic pairs as well as oppositely-charged pairs, two functional forms are introduced, one of which implies a linear dependence of the free energy of interactions on temperature and the other one a hyperbolic-tangent dependence. The free energy of the interactions of other pairs is assumed to be independent of temperature. With the example of the N-terminal part of the B-domain of staphylococcal protein A, we demonstrate that, with this temperature dependence, the radius of gyration and the root-mean-square deviation from the native structure grow less steeply with temperature and the heat-capacity peak is lower than that obtained with temperature-independent side chain - side chain potentials. This demonstrates that ignoring the increase of the strength of hydrophobic interactions with increasing temperature in coarse-grained force fields is likely to result in grossly wrong predictions of the thermodynamics of folding and of the process of thermal unfolding made with such force fields.

关键词： Temperature dependence Proteins Laboratories Chemistry Chemicals Thermal force Medical simulation Biological system modeling computational biology Temperature sensors

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Assessment of the Binding Characteristics of Human Immunodeficiency Virus Type 1 Glycoprotein120 and Host Cluster of Differentiation4 Using Digital Signal Processing

Assessment of the Binding Characteristics of Human Immunodef...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Norbert Nwankwo Huseyin Seker Centre of Computational Intelligence Department of Informatics Faculty of Technology De Montfort University Leicester UK

Protein residues which contribute to bio-recognition and binding interaction between HIV Surface protein also called Glycoprotein120 (gp120) and Cluster of Differentiation4 (CD4) have been identified. However, this was with limited number of isolates. Notwithstanding, the particular HIV isolate that harbors the gp120 with the greatest binding force to the CD4 has not been investigated. In this paper, protein sequences of gp120 from 43 HIV-1 isolates, 5 isolates each from the HIV-2 and SIV as well as the CD4 of 25 HIV host organisms were analyzed using Resonant Recognition Method (RMM). The results re-confirmed that protein sequences of the HIV and CD4 share common spectral features in relation to bio-recognition and binding. From the large dataset of the HIV and SIV isolates used, MFA group M subtype B (HIV-1) isolate was found to have the greatest affinity for the CD4. Furthermore, the CD4 of the human and chimpanzee were established to possess about same level of binding force to the HIV gp120. Also the CD4 of other species offered more attractive force to another protein in such a manner that the approach taken in this study has also shown to be a useful and reliable tool for clear categorization of species. Finally, clinically experimented results were found to correlate with the computationally obtained results as the gp120 of the HIV-2 and SIV which were recognized to circumvent the CD4 during infection were found to have low peak amplitude. This low peak amplitude observed in the HIV-2 and SIV implies that they have weak affinity or attraction for the host CD4.

关键词： Human immunodeficiency virus Digital signal processing Proteins Sequences Amino acids Organisms Resonance Informatics Peptides Drugs

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