Search results for 'Proteomics' (try it on Scholar)

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  1. Hendrik G. Stunnenberg & Michiel Vermeulen (2011). Towards Cracking the Epigenetic Code Using a Combination of High‐Throughput Epigenomics and Quantitative Mass Spectrometry‐Based Proteomics. Bioessays 33 (7):547-551.
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    Sandra Orchard, Rolf Apweiler, Robert Barkovich, Dawn Field, John S. Garavelli, David Horn, Andy Jones, Philip Jones, Randall Julian, Ruth McNally, Jason Nerothin, Norman Paton, Angel Pizarro, Sean Seymour, Chris Taylor, Stefan Wiemann & Henning Hermjakob, Proteomics and Beyond : A Report on the 3rd Annual Spring Workshop of the HUPO-PSI 21-23 April 2006, San Francisco, CA, USA. [REVIEW]
    The theme of the third annual Spring workshop of the HUPO-PSI was proteomics and beyond and its underlying goal was to reach beyond the boundaries of the proteomics community to interact with groups working on the similar issues of developing interchange standards and minimal reporting requirements. Significant developments in many of the HUPO-PSI XML interchange formats, minimal reporting requirements and accompanying controlled vocabularies were reported, with many of these now feeding into the broader efforts of the Functional Genomics (...)
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  3.  8
    Bent Honoré, Morten Østergaard & Henrik Vorum (2004). Functional Genomics Studied by Proteomics. Bioessays 26 (8):901-915.
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  4.  8
    Subhash C. Basak, Brian D. Gute & Frank Witzmann (2005). Information-Theoretic Biodescriptors for Proteomics Maps: Development and Applications in Predictive Toxicology. Complexity 1:2.
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  5.  2
    K. K. Jain (2004). Book Review: Proteins and Proteomics: A Laboratory Manual and Purifying Proteins for Proteomics: A Laboratory Manual. [REVIEW] Bioessays 26 (12):1366-1367.
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    Miguel A. Andrade‐Navarro (2009). The Dictionary of Genomics, Transcriptomics, and Proteomics. Bioessays 31 (12):1367-1369.
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  7. Geoffrey D. Findlay & Willie J. Swanson (2010). Proteomics Enhances Evolutionary and Functional Analysis of Reproductive Proteins. Bioessays 32 (1):26-36.
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  8.  18
    Ulrich Krohs & Werner Callebaut (2007). Data Without Models Merging with Models Without Data. In Fred C. Boogerd, Frank J. Bruggeman, Jan-Hendrik S. Hofmeyr & Hans V. Westerhoff (eds.), Systems Biology: Philosophical Foundations. Elsevier 181--213.
    Systems biology is largely tributary to genomics and other “omic” disciplines that generate vast amounts of structural data. “Omics”, however, lack a theoretical framework that would allow using these data sets as such (rather than just tiny bits that are extracted by advanced data-mining techniques) to build explanatory models that help understand physiological processes. Systems biology provides such a framework by adding a dynamic dimension to merely structural “omics”. It makes use of bottom-up and top-down models. The former are based (...)
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  9.  36
    Darren Natale, Cecilia N. Arighi, Winona C. Barker, Judith A. Blake, Carol J. Bult, Michael Caudy, Harold J. Drabkin, Peter D’Eustachio, Alexei V. Evsikov, Hongzhan Huang, Jules Nchoutmboube, Natalia V. Roberts, Barry Smith, Jian Zhang & Cathy H. Wu (2011). The Protein Ontology: A Structured Representation of Protein Forms and Complexes. Nucleic Acids Research 39 (1):D539-D545.
    The Protein Ontology (PRO) provides a formal, logically-based classification of specific protein classes including structured representations of protein isoforms, variants and modified forms. Initially focused on proteins found in human, mouse and Escherichia coli, PRO now includes representations of protein complexes. The PRO Consortium works in concert with the developers of other biomedical ontologies and protein knowledge bases to provide the ability to formally organize and integrate representations of precise protein forms so as to enhance accessibility to results of protein (...)
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  10.  5
    Ron Milo (2013). What is the Total Number of Protein Molecules Per Cell Volume? A Call to Rethink Some Published Values. Bioessays 35 (12):1050-1055.
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  11.  3
    Madhvi Patel, Mohamed If Shariff, Nimzing G. Ladep, Andrew V. Thillainayagam, Howard C. Thomas, Shahid A. Khan & Simon D. Taylor‐Robinson (2012). Hepatocellular Carcinoma: Diagnostics and Screening. Journal of Evaluation in Clinical Practice 18 (2):335-342.
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  12. Massimo Pigliucci (2007). Post-Genomic Musings. [REVIEW] Science 317:1172-1173.
    Everyone in biology keeps predicting that the next few years will bring answers to some of the major open questions in evolutionary biology, but there seems to be disagreement on what, exactly, those questions are. Enthusiasts of the various “-omics” (genomics, proteomics, transcriptomics, metabolomics, and even phenomics) believe, as Michael Lynch puts it in the final chapter of The Origins of Genome Architecture, that “we can be confident of two things: the basic theoretical machinery for understanding the evolutionary process (...)
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  13.  7
    Yan Leychkis, Stephen R. Munzer & Jessica L. Richardson (2009). What is Stemness? Studies in History and Philosophy of Science Part C 40 (4):312-320.
    This paper, addressed to both philosophers of science and stem cell biologists, aims to reduce the obscurity of and disagreements over the nature of stemness. The two most prominent current theories of stemness—the entity theory and the state theory—are both biologically and philosophically unsatisfactory. Improved versions of these theories are likely to converge. Philosophers of science can perform a much needed service in clarifying and formulating ways of testing entity and state theories of stemness. To do so, however, philosophers should (...)
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  14.  36
    Koffi N. Maglo (2012). Group-Based and Personalized Care in an Age of Genomic and Evidence-Based Medicine: A Reappraisal. Perspectives in Biology and Medicine 55 (1):137-154.
    Individualized care and equality of care remain two imperatives for formulating any scientifically and morally informed public health policy. Yet both continue to be elusive goals, even in the age of genomics, proteomics, and evidence-based medicine. Nonetheless, with the rapid growth and improvement of human biotechnologies, the need to individualize therapies while allocating medical care equally may result partly from our biological constitution. Human beings are all unique, and their biological differences significantly influence variability in disease causation and therapeutic (...)
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  15.  3
    L. A. Eckenwiler, C. Ells, D. Feinholz & T. Schonfeld (2008). Hopes for Helsinki: Reconsidering "Vulnerability". Journal of Medical Ethics 34 (10):765-766.
    The Declaration of Helsinki is recognised worldwide as a cornerstone of research ethics. Working in the wake of the Nazi doctors’ trials at Nuremberg, drafters of the Declaration set out to codify the obligations of physician-researchers to research participants. Its significance cannot be overstated. Indeed, it is cited in most major guidelines on research involving humans and in the regulations of over a dozen countries.Although it has undergone five revisions,1 and most recently incorporated language aimed at addressing concerns over research (...)
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  16.  19
    Branko Kozulić, Proteins and Genes, Singletons and Species.
    Recent experimental data from proteomics and genomics are interpreted here in ways that challenge the predominant viewpoint in biology according to which the four evolutionary processes, including mutation, recombination, natural selection and genetic drift, are sufficient to explain the origination of species. The predominant viewpoint appears incompatible with the finding that the sequenced genome of each species contains hundreds, or even thousands, of unique genes - the genes that are not shared with any other species. These unique genes and (...)
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  17.  4
    Sven Ove Hansson (2004). The Ethics of Biobanks. Cambridge Quarterly of Healthcare Ethics 13 (4):319-326.
    Due to modern biochemistry and, in particular, recent developments in genomics, proteomics, and bioinformatics, human samples have become the most important raw materials for advancement in the health sciences. Such material has been at the center of fundamental biomedical research for a long time. What is new is its increased usefulness in research with direct clinical relevance, such as the development of drugs. Because of the larger commercial involvement in such research, this has also led to greater economic interests (...)
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  18.  4
    Lisa A. Eckenwiler, Carolyn Ells, Dafna Feinholz & Toby Schonfeld (2008). Hopes for Helsinki: Reconsidering “Vulnerability”. Journal of Medical Ethics 34 (10):765-766.
    The Declaration of Helsinki is recognised worldwide as a cornerstone of research ethics. Working in the wake of the Nazi doctors’ trials at Nuremberg, drafters of the Declaration set out to codify the obligations of physician-researchers to research participants. Its significance cannot be overstated. Indeed, it is cited in most major guidelines on research involving humans and in the regulations of over a dozen countries.Although it has undergone five revisions,1 and most recently incorporated language aimed at addressing concerns over research (...)
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  19.  8
    Sanjay Goel & Stephen F. S. F. Bush (2004). Biological Models of Security for Virus Propagation in Computer Networks. Login, December 29 (6):49--56.
    This aricle discusses the similarity between the propagation of pathogens (viruses and worms) on computer networks and the proliferation of pathogens in cellular organisms (organisms with genetic material contained within a membrane-encased nucleus). It introduces several biological mechanisms which are used in these organisms to protect against such pathogens and presents security models for networked computers inspired by several biological paradigms, including genomics (RNA interference), proteomics (pathway mapping), and physiology (immune system). In addition, the study of epidemiological models for (...)
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  20.  4
    A. Mackenzie & R. McNally (2013). Living Multiples: How Large-Scale Scientific Data-Mining Pursues Identity and Differences. Theory, Culture and Society 30 (4):72-91.
    This article responds to two problems confronting social and human sciences: how to relate to digital data, inasmuch as it challenges established social science methods; and how to relate to life sciences, insofar as they produce knowledge that impinges on our own ways of knowing. In a case study of proteomics, we explore how digital devices grapple with large-scale multiples – of molecules, databases, machines and people. We analyse one particular visual device, a cluster-heatmap, produced by scientists by mining (...)
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