Relevant bibliographies by topics / Proteome proteomics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Proteome proteomics'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Proteome proteomics"

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Krieg, Rene C., Cloud P. Paweletz, Lance A. Liotta, and Emanuel F. Petricoin. "Clinical Proteomics for Cancer Biomarker Discovery and Therapeutic Targeting." Technology in Cancer Research & Treatment 1, no. 4 (2002): 263–72. http://dx.doi.org/10.1177/153303460200100407.

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As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the “software” o
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Thanasupawat, Thatchawan, Aleksandra Glogowska, Christopher Pascoe, et al. "Slow Off-Rate Modified Aptamer (SOMAmer) Proteomic Analysis of Patient-Derived Malignant Glioma Identifies Distinct Cellular Proteomes." International Journal of Molecular Sciences 22, no. 17 (2021): 9566. http://dx.doi.org/10.3390/ijms22179566.

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Malignant gliomas derive from brain glial cells and represent >75% of primary brain tumors. This includes anaplastic astrocytoma (grade III; AS), the most common and fatal glioblastoma multiforme (grade IV; GBM), and oligodendroglioma (ODG). We have generated patient-derived AS, GBM, and ODG cell models to study disease mechanisms and test patient-centered therapeutic strategies. We have used an aptamer-based high-throughput SOMAscan® 1.3K assay to determine the proteomic profiles of 1307 different analytes. SOMAscan® proteomes of AS and GBM self-organized into closely adjacent proteomes wh
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Sadeesh, Nithin, Mauro Scaravilli, and Leena Latonen. "Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes." Cancers 13, no. 19 (2021): 4829. http://dx.doi.org/10.3390/cancers13194829.

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Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate
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Burat, Bastien, Audrey Reynaerts, Dominique Baiwir, et al. "Characterization of the Human Eccrine Sweat Proteome—A Focus on the Biological Variability of Individual Sweat Protein Profiles." International Journal of Molecular Sciences 22, no. 19 (2021): 10871. http://dx.doi.org/10.3390/ijms221910871.

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The potential of eccrine sweat as a bio-fluid of interest for diagnosis and personalized therapy has not yet been fully evaluated, due to the lack of in-depth sweat characterization studies. Thanks to recent developments in omics, together with the availability of accredited sweat collection methods, the analysis of human sweat may now be envisioned as a standardized, non-invasive test for individualized monitoring and personalized medicine. Here, we characterized individual sweat samples, collected from 28 healthy adult volunteers under the most standardized sampling methodology, by applying
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Senavirathna, Lakmini, Cheng Ma, Ru Chen, and Sheng Pan. "Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity." Cells 11, no. 15 (2022): 2450. http://dx.doi.org/10.3390/cells11152450.

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Dissecting the proteome of cell types and states at single-cell resolution, while being highly challenging, has significant implications in basic science and biomedicine. Mass spectrometry (MS)-based single-cell proteomics represents an emerging technology for system-wide, unbiased profiling of proteins in single cells. However, significant challenges remain in analyzing an extremely small amount of proteins collected from a single cell, as a proteome-wide amplification of proteins is not currently feasible. Here, we report an integrated spectral library-based single-cell proteomics (SLB-SCP)
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Solovyeva, Elizaveta M., Julia A. Bubis, Irina A. Tarasova, et al. "On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics." Biochemistry (Moscow) 87, no. 11 (2022): 1342–53. http://dx.doi.org/10.1134/s000629792211013x.

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Abstract Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry
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Masood, Afshan, Hicham Benabdelkamel, and Assim Alfadda. "Obesity Proteomics: An Update on the Strategies and Tools Employed in the Study of Human Obesity." High-Throughput 7, no. 3 (2018): 27. http://dx.doi.org/10.3390/ht7030027.

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Proteomics has become one of the most important disciplines for characterizing cellular protein composition, building functional linkages between protein molecules, and providing insight into the mechanisms of biological processes in a high-throughput manner. Mass spectrometry-based proteomic advances have made it possible to study human diseases, including obesity, through the identification and biochemical characterization of alterations in proteins that are associated with it and its comorbidities. A sizeable number of proteomic studies have used the combination of large-scale separation te
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Oikonomou, Panos, Roberto Salatino, and Saeed Tavazoie. "In vivo mRNA display enables large-scale proteomics by next generation sequencing." Proceedings of the National Academy of Sciences 117, no. 43 (2020): 26710–18. http://dx.doi.org/10.1073/pnas.2002650117.

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Large-scale proteomic methods are essential for the functional characterization of proteins in their native cellular context. However, proteomics has lagged far behind genomic approaches in scalability, standardization, and cost. Here, we introduce in vivo mRNA display, a technology that converts a variety of proteomics applications into a DNA sequencing problem. In vivo-expressed proteins are coupled with their encoding messenger RNAs (mRNAs) via a high-affinity stem-loop RNA binding domain interaction, enabling high-throughput identification of proteins with high sensitivity and specificity
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Stubbs, Keith A., and David J. Vocadlo. "Affinity-Based Proteomics Probes; Tools for Studying Carbohydrate-Processing Enzymes." Australian Journal of Chemistry 62, no. 6 (2009): 521. http://dx.doi.org/10.1071/ch09140.

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As more information becomes available through the efforts of high-throughput screens, there is increasing pressure on the three main ‘omic’ fields, genomics, proteomics, and metabolomics, to organize this material into useful libraries that enable further understanding of biological systems. Proteomics especially is faced with two highly challenging tasks. The first is assigning the activity of thousands of putative proteins, the existence of which has been suggested by genomics studies. The second is to serve as a link between genomics and metabolomics by demonstrating which enzymes play role
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Tjalsma, Harold, Haike Antelmann, Jan D. H. Jongbloed, et al. "Proteomics of Protein Secretion by Bacillus subtilis: Separating the “Secrets” of the Secretome." Microbiology and Molecular Biology Reviews 68, no. 2 (2004): 207–33. http://dx.doi.org/10.1128/mmbr.68.2.207-233.2004.

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SUMMARY Secretory proteins perform a variety of important“ remote-control” functions for bacterial survival in the environment. The availability of complete genome sequences has allowed us to make predictions about the composition of bacterial machinery for protein secretion as well as the extracellular complement of bacterial proteomes. Recently, the power of proteomics was successfully employed to evaluate genome-based models of these so-called secretomes. Progress in this field is well illustrated by the proteomic analysis of protein secretion by the gram-positive bacterium Bacillus subtili
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Dissertations / Theses on the topic "Proteome proteomics"

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Ciryam, Prajwal. "Proteome metastability in stress, aging, and disease." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708160.

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McDonald, Lucy. "Positional proteomics : advanced strategies for targeted proteome simplification." Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501607.

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Proteome complexity presents a major challenge in the field of proteomics. The majority of bottom-up methods begin with proteolysis, which increases the number of analytes in the mixture by about 30-50 fold. This level of complexity demands simplification, and there is an increasing requirement for strategies and reagents that reduce the complexity of a total proteome mixture. It may be argued that when analysing a complete protein digest, for instance by standard shotgun methods, more peptides are analysed than strictly necessary. An efficient proteomic strategy simplifies the proteome while
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Sun, Jin. "Characterization of the egg and embryonic proteome of Pomacea canaliculata, and responses of the proteome to environmental stressors." HKBU Institutional Repository, 2013. http://repository.hkbu.edu.hk/etd_ra/1519.

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Woolerton, Yvonne. "Quantitative proteomics strategies to explore the Saccharomyces cerevisiae proteome." Thesis, University of Liverpool, 2014. http://livrepository.liverpool.ac.uk/2025519/.

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Quantitative proteomics aims at not just identifying, but accurately quantifying the cellular proteome, and while technological advances towards accurate and reliable quantification of proteins is advancing, this alone does not provide an accurate picture of a proteins role within a cell. There is a far greater level of functionality in the cellular environment than there are protein coding genes in the genome, owing partly to the organisation of individual proteins into larger assemblies. A single protein can form interactions with, potentially, a large number of other proteins, leading to a
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Klammer, Aaron A. "Revealing the proteome : a machine learning approach to peptide identification /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/10278.

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Branson, Owen E. "Improved tag-count approaches for label-free quantitation of proteome differences in bottom-up proteomic experiments." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471553685.

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Yee, Fong-ying Anita, and 伊芳盈. "Transcriptome and proteome of the intervertebral disc in health and disease." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B43752354.

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Yee, Fong-ying Anita. "Transcriptome and proteome of the intervertebral disc in health and disease." Click to view the E-thesis via HKUTO, 2010. http://sunzi.lib.hku.hk/hkuto/record/B43752354.

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Fisher, Christal. "Quantitative analysis of the plasma proteome in pre-eclampsia." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/quantitative-analysis-of-the-plasma-proteome-in-preeclampsia(3e207341-ebb9-4cb0-b7ea-34b9b110eda6).html.

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There is currently no clinically useful screening test available to identify nulliparous women at high risk of developing pre-eclampsia. This study aimed to identify novel biomarkers using hypothesis generating proteomic methods applied to plasma samples obtained prior to clinical diagnosis of pre-eclampsia. Plasma samples taken at 15 weeks gestation from women who subsequently developed late pre-eclampsia (> 34 weeks), early pre-eclampsia (< 34 weeks) and two distinct groups of women with uncomplicated pregnancies (each n=12) were pooled. Pooled plasma was immunodepleted, labelled using iTRAQ
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Tabb, David L. "Bioinformatics of proteomic tandem mass spectra : selection, characterization, and identification /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/10847.

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Books on the topic "Proteome proteomics"

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Proteome bioinformatics. Humana, 2010.

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Mishra, Nawin C. Introduction to proteomics: Principles and applications. Wiley, 2010.

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Mishra, Nawin C. Introduction to proteomics: Principles and applications. John Wiley & Sons, 2010.

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1951-, Kamp R. M., Calvete Juan J, and Choli-Papadopoulou T. 1956-, eds. Methods in proteome and protein analysis. Springer-Verlag, 2004.

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The urinary proteome: Methods and protocols. Humana Press, 2010.

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Introduction to proteomics: Principles and applications. Wiley, 2010.

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Tom, Naven, ed. Proteomics in practice: A laboratory manual of proteome analysis. Wiley-VCH, 2002.

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S, Omenn Gilbert, ed. Exploring the human plasma proteome. Wiley-VCH, 2006.

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D, Pagel Mark, and Pomiankowski Andrew, eds. Evolutionary genomics and proteomics. Sinauer Associates, 2008.

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Tzortzatou, Stathopoulou Fotini, ed. Genome and proteome in oncology. Nova Science Publishers, 2005.

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Book chapters on the topic "Proteome proteomics"

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Briones, Carlos. "Proteome, Proteomics." In Encyclopedia of Astrobiology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11274-4_1289.

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Briones, Carlos. "Proteome, Proteomics." In Encyclopedia of Astrobiology. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44185-5_1289.

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Briones, Carlos. "Proteome, Proteomics." In Encyclopedia of Astrobiology. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-27833-4_1289-3.

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Sanchez, Jean-Charles, Yohann Couté, Laure Allard, Pierre Lescuyer, and Denis F. Hochstrasser. "Biomedical Applications of Proteomics." In Proteome Research. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72910-5_9.

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Langen, Hanno, and Peter Berndt. "Proteomics Databases." In Proteome Research: Mass Spectrometry. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56895-4_12.

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Minic, Zoran, Georges Boudart, Cécile Albenne, Hervé Canut, Elisabeth Jamet, and Rafael F. Pont-Lezica. "Cell Wall Proteome." In Plant Proteomics. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72617-3_12.

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Sénécaut, Nicolas, Pierre Poulain, Laurent Lignières, et al. "Quantitative Proteomics in Yeast: From bSLIM and Proteome Discoverer Outputs to Graphical Assessment of the Significance of Protein Quantification Scores." In Methods in Molecular Biology. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2257-5_16.

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AbstractSimple light isotope metabolic labeling (bSLIM) is an innovative method to accurately quantify differences in protein abundance at the proteome level in standard bottom-up experiments. The quantification process requires computation of the ratio of intensity of several isotopologs in the isotopic cluster of every identified peptide. Thus, appropriate bioinformatic workflows are required to extract the signals from the instrument files and calculate the required ratio to infer peptide/protein abundance. In a previous study (Sénécaut et al., J Proteome Res 20:1476–1487, 2021), we developed original open-source workflows based on OpenMS nodes implemented in a KNIME working environment. Here, we extend the use of the bSLIM labeling strategy in quantitative proteomics by presenting an alternative procedure to extract isotopolog intensities and process them by taking advantage of new functionalities integrated into the Minora node of Proteome Discoverer 2.4 software. We also present a graphical strategy to evaluate the statistical robustness of protein quantification scores and calculate the associated false discovery rates (FDR). We validated these approaches in a case study in which we compared the differences between the proteomes of two closely related yeast strains.
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Bendixen, Emøke. "Understanding the Proteome." In Proteomics in Foods. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5626-1_1.

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Senis, Yotis A. "The Platelet Membrane Proteome." In Platelet Proteomics. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470940297.ch5.

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Smalley, David M. "The Platelet Microparticle Proteome." In Platelet Proteomics. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470940297.ch7.

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Conference papers on the topic "Proteome proteomics"

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Bandeira, Nuno. "Revealing deep proteome diversity with community-scale proteomics big data." In CSBio '17: 8th International Conference on Computational Systems-Biology and Bioinformatics. ACM, 2017. http://dx.doi.org/10.1145/3156346.3156694.

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Pavlou, MP, AP Drabovich, A. Dimitromanolakis, and EP Diamandis. "Abstract P6-05-09: Unravelling the global effect of estrogen on breast cancer cell proteome using quantitative proteomics." In Abstracts: Thirty-Fifth Annual CTRC‐AACR San Antonio Breast Cancer Symposium‐‐ Dec 4‐8, 2012; San Antonio, TX. American Association for Cancer Research, 2012. http://dx.doi.org/10.1158/0008-5472.sabcs12-p6-05-09.

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Vowinckel, Jakob, Karel Novy, Thomas Corwin, et al. "Abstract 4266: Proteomics for precision oncology: Profiling the proteome of matching tumor and adjacent normal tissue using data-independent acquisition." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-4266.

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Lord, RW, RE Maher, V. Harman, et al. "S18 The sputum proteome and its relationship to cystic fibrosis lung disease: using global proteomics to develop clinically useful biomarkers." In British Thoracic Society Winter Meeting 2019, QEII Centre, Broad Sanctuary, Westminster, London SW1P 3EE, 4 to 6 December 2019, Programme and Abstracts. BMJ Publishing Group Ltd and British Thoracic Society, 2019. http://dx.doi.org/10.1136/thorax-2019-btsabstracts2019.24.

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Selvam, Anjan Panneer, and Shalini Prasad. "Single Molecule Analysis Tool (SMAT) for Multiplexed Label-Free Assessment of Rare Cell Populations." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-40225.

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A nanowell sensor for single molecular proteomic analysis of lung cancer has been designed. The nanowell sensor is an electrochemical immunoassay and comprises of a heterogenous nanoporous arrays integrated on to a gold microelectronic platform. The sensor operates on the principle of electrochemical impedance spectroscopy (EIS). Our approach to classification of lung cancer is based on screening for levels of expression of specific proteomic biomarkers associated with lung cancer stem cells. Proteomic activity for two lung cancer cell lines for two specific markers (ALDH1A1 and ALDH1A3) was q
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Costessi, Mr Adalberto, Mr Carlo Vascotto, Dr Alex Pines, et al. "Bone Proteomics experiment (BOP): the first proteomic analysis of mammalian cells cultured in weightlessness conditions." In 57th International Astronautical Congress. American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.iac-06-a1.4.08.

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Martens, William L., Philip Poronnik, and Darren Saunders. "Hypothesis-Driven Sonification of Proteomic Data Distributions Indicating Neurodegredation in Amyotrophic Lateral Sclerosis." In The 22nd International Conference on Auditory Display. The International Community for Auditory Display, 2016. http://dx.doi.org/10.21785/icad2016.024.

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Three alternative sonifications of proteomic data distributions were compared as a means to indicate the neuropathology associated with Amyotrophic Lateral Sclerosis (ALS) via auditory display (through exploration of the differentiation of induced pluripotent stem cell derived neurons). Pure visual displays of proteomic data often result in ”visual overload” such that detailed or subtle data important to describe ALS neurodegradation may be glossed over, and so three competing approaches to the sonification of proteomic data were designed to capitalize upon human auditory capacities that compl
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Schmitt, H., A. Warnecke, I. De Vries, et al. "Charakterisierung des Proteoms humaner Perilymphe mit dem Focus auf BDNF regulierte Proteine." In Abstract- und Posterband – 89. Jahresversammlung der Deutschen Gesellschaft für HNO-Heilkunde, Kopf- und Hals-Chirurgie e.V., Bonn – Forschung heute – Zukunft morgen. Georg Thieme Verlag KG, 2018. http://dx.doi.org/10.1055/s-0038-1640584.

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Tirloni, Lucas. "Proteomic characterization ofRhipicephalus microplussaliva." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.111459.

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Gnabasik, David, and Gita Alaghband. "Topological analysis of proteomic data." In 2012 International Conference on Collaboration Technologies and Systems (CTS). IEEE, 2012. http://dx.doi.org/10.1109/cts.2012.6261006.

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Reports on the topic "Proteome proteomics"

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Dahlgren, Kelsey, Colin Gates, Thomas Lee, and Jeffrey Cameron. Proximity-based proteomics reveals the thylakoid lumen proteome in the cyanobacterium Synechococcus sp. PCC 7002. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1900233.

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Ghanim, Murad, Joe Cicero, Judith K. Brown, and Henryk Czosnek. Dissection of Whitefly-geminivirus Interactions at the Transcriptomic, Proteomic and Cellular Levels. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7592654.bard.

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Our project focuses on gene expression and proteomics of the whitefly Bemisia tabaci (Gennadius) species complex in relation to the internal anatomy and localization of expressed genes and virions in the whitefly vector, which poses a major constraint to vegetable and fiber production in Israel and the USA. While many biological parameters are known for begomovirus transmission, nothing is known about vector proteins involved in the specific interactions between begomoviruses and their whitefly vectors. Identifying such proteins is expected to lead to the design of novel control methods that i
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Hayward, Simon W. Therapy Selection by Proteomic Profiling. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada502570.

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Hayward, Simon W. Therapy Selection by Proteomic Profiling. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada435061.

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Lamartiniere, Coral A. Proteomic Analysis of Genistein Mammary Cancer. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada442989.

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Lipton, Mary. Identification of Metal Reductases using Proteomic Analysis. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/896018.

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Wilson, Michael J. Proteomic Analysis of Prostate Cancer Field Effect. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada497256.

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Lamartiniere, Coral A. Proteomic Analysis of Genistein Mammary Cancer Chemoprevention. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada428933.

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Anderson, Mark J., Trisha Grevengoed, Steven M. Lonergan, and Elisabeth J. Huff-Lonergan. Proteomic Analysis of Bovine Muscles during Aging. Iowa State University, 2010. http://dx.doi.org/10.31274/ans_air-180814-1244.

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Turchi, John J. Proteomic Analysis of Cisplatin-Resistant Ovarian Cancer. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada425620.

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