Conferences related to Amino acids

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2020 IEEE International Conference on Plasma Science (ICOPS)

IEEE International Conference on Plasma Science (ICOPS) is an annual conference coordinated by the Plasma Science and Application Committee (PSAC) of the IEEE Nuclear & Plasma Sciences Society.


2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.


2020 IEEE International Magnetic Conference (INTERMAG)

INTERMAG is the premier conference on all aspects of applied magnetism and provides a range of oral and poster presentations, invited talks and symposia, a tutorial session, and exhibits reviewing the latest developments in magnetism.


2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore


2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)

Science, technology and applications spanning the millimeter-waves, terahertz and infrared spectral regions


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Periodicals related to Amino acids

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Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Dielectrics and Electrical Insulation, IEEE Transactions on

Electrical insulation common to the design and construction of components and equipment for use in electric and electronic circuits and distribution systems at all frequencies.


Engineering in Medicine and Biology Magazine, IEEE

Both general and technical articles on current technologies and methods used in biomedical and clinical engineering; societal implications of medical technologies; current news items; book reviews; patent descriptions; and correspondence. Special interest departments, students, law, clinical engineering, ethics, new products, society news, historical features and government.


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Most published Xplore authors for Amino acids

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Xplore Articles related to Amino acids

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Automatic construction of 3D structural motifs for protein function prediction

[{u'author_order': 1, u'affiliation': u'Dept. of Genetics, Stanford Med. Inf., CA, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/37277069300', u'full_name': u'M.P. Liang', u'id': 37277069300}, {u'author_order': 2, u'authorUrl': u'https://ieeexplore.ieee.org/author/37277081900', u'full_name': u'D.L. Brutlag', u'id': 37277081900}, {u'author_order': 3, u'authorUrl': u'https://ieeexplore.ieee.org/author/37277083500', u'full_name': u'R.B. Altman', u'id': 37277083500}] Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003, 2003

Structural genomics initiatives are on the verge of generating a vast number of protein structures. The biological roles for many of these proteins are still unknown, and high-throughput methods for determining their function are necessary. Understanding the function of these proteins will have profound impact in drug development and protein engineering. Current methods for protein function prediction on structures require ...


High sensitivity radiation detector for capillary electrophoresis

[{u'author_order': 1, u'affiliation': u'Radiation Monitoring Devices, Inc., Watertown, MA, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/37357396900', u'full_name': u'J.S. Gordon', u'id': 37357396900}, {u'author_order': 2, u'affiliation': u'Radiation Monitoring Devices, Inc., Watertown, MA, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/37295861900', u'full_name': u'S. Vasile', u'id': 37295861900}, {u'author_order': 3, u'affiliation': u'Radiation Monitoring Devices, Inc., Watertown, MA, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/37389593300', u'full_name': u'T. Hazlett', u'id': 37389593300}, {u'author_order': 4, u'affiliation': u'Radiation Monitoring Devices, Inc., Watertown, MA, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/37273083000', u'full_name': u'M. Squillante', u'id': 37273083000}] IEEE Transactions on Nuclear Science, 1993

Capillary electrophoresis (CE) is an instrumental technique capable of high resolution separation and analysis of small quantities of nucleotides, amino acids, peptides, and proteins with very high efficiency and throughput. The unprecedented sensitivity of this technique will be useful for such new applications as in vivo labeling and identification of trace substances and single cell work. The principal limitation of ...


Using Emerging Subsequence in Classifying Protein Structural Class

[{u'author_order': 1, u'authorUrl': u'https://ieeexplore.ieee.org/author/37299048700', u'full_name': u'Khalid E.K. Saeed', u'id': 37299048700}, {u'author_order': 2, u'authorUrl': u'https://ieeexplore.ieee.org/author/37676724900', u'full_name': u'Heon Gyu Lee', u'id': 37676724900}, {u'author_order': 3, u'authorUrl': u'https://ieeexplore.ieee.org/author/37674580900', u'full_name': u'Wun-Jae Kim', u'id': 37674580900}, {u'author_order': 4, u'authorUrl': u'https://ieeexplore.ieee.org/author/37671735400', u'full_name': u'Eun-Jong Cha', u'id': 37671735400}, {u'author_order': 5, u'authorUrl': u'https://ieeexplore.ieee.org/author/37306876400', u'full_name': u'Keun Ho Ryu', u'id': 37306876400}] 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009

Knowledge about protein's structure can help in understanding its function and has many applications in computer-aided drug design and protein engineering. In this paper we introduce a new methodology for predicting protein structural class using Emerging Subsequences (ES). In a sequence database, an emerging subsequence of data class is a subsequence which occurs more frequently in that class rather than ...


A Kohonen self-organizing map for the functional classification of proteins based on one-dimensional sequence information

[{u'author_order': 1, u'affiliation': u'Dept. of Biochem., Otago Univ., Dunedin, New Zealand', u'authorUrl': u'https://ieeexplore.ieee.org/author/37339487700', u'full_name': u'R. Pollock', u'id': 37339487700}, {u'author_order': 2, u'authorUrl': u'https://ieeexplore.ieee.org/author/37337791500', u'full_name': u'T. Lane', u'id': 37337791500}, {u'author_order': 3, u'authorUrl': u'https://ieeexplore.ieee.org/author/37333285500', u'full_name': u'M. Watts', u'id': 37333285500}] Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2002

There are many examples where neural networks have been effectively used to predict protein secondary and tertiary structure from the primary sequence data. We describe the use of a Kohonen self-organizing map (SOM) to categorise proteins based on secondary structure, and attempt to relate this information to functional data.


Reply to ``Comments on `Semiautomatic Quantification of Sharpness of EEG Phenomena'''

[{u'author_order': 1, u'affiliation': u'Department of Physiology and Anatomy and the Brain, Research Institute, University of California, Los Angeles, Calif. 90024.', u'authorUrl': u'https://ieeexplore.ieee.org/author/37301353500', u'full_name': u'D. 0. Walter', u'id': 37301353500}, {u'author_order': 2, u'affiliation': u'Douglas Hospital, McGill University, Montreal, Que., Canada.', u'authorUrl': u'https://ieeexplore.ieee.org/author/37287785700', u'full_name': u'H. F. Muller', u'id': 37287785700}, {u'author_order': 3, u'affiliation': u'Department of Physiology, University of Manitoba, Winnipeg, Man., Canada.', u'authorUrl': u'https://ieeexplore.ieee.org/author/37301294400', u'full_name': u'R. M. Jell', u'id': 37301294400}] IEEE Transactions on Biomedical Engineering, 1973

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IEEE-USA E-Books

  • Discovering 3D Protein Structures for Optimal Structure Alignment

    Analyzing three-dimensional protein structures is a very important task in molecular biology. It has been proved that structurally similar proteins tend to have similar functions even if their amino acid sequences are not similar to one another. Thus, it is very important to find proteins with similar structures from the growing database to analyze protein functions. Currently there exist several protein databases publicly available online. These databases assemble various data about proteins, protein structures, protein functions, protein relationships, and other information. This chapter describes the procedure for building the matrix representing the vector model index file. It discusses the suffix tree data structure-its definition, construction algorithms, and main characteristics. The data for protein 3D structures indexing are retrieved from Protein Data Bank (PDB) database, which consists of proteins, nucleic acids, and complex assemblies. The chapter further explains the algorithm for measuring protein similarity on the basis of their tertiary structure.

  • Scoring Functions for Predicting Structure and Binding of Proteins

    This chapter first discusses the general framework of developing knowledge- based potential functions in terms of molecular descriptors, functional form, and parameter calculations. It also discusses the underlying thermodynamic hypothesis of protein folding. With the assumption that frequently observed protein features in a database of structures correspond to a low energy state, frequency of observed interactions can be converted to energy terms. The chapter then describes in detail the models behind the Miyazawa-Jernigan contact potential, distance-dependent potentials, and geometric potentials. Next, it shows how to weight sample structures of varying degree of sequence similarity in the structural database. The chapter also describes general geometric models for the problem of obtaining optimized knowledge-based potential functions, as well as methods for developing optimal linear and nonlinear potential functions. It presents several applications of the knowledge-based potential functions. Finally, the chapter provides general limitations and possible improvements for the statistical and optimized potential functions.

  • 6 Beetles, Beasties, and Bunnies in Your Back Pocket

    As a single footstep will not make a path on the earth, so a single thought will not make a pathway in the mind. To make a deep physical path, we walk again and again. To make a deep mental path, we must think over and over the kind of thoughts we wish to dominate our lives. —Wilferd Arlan Peterson

  • Networks in Biology

    This chapter contains sections titled:IntroductionBiology 101Systems BiologyProperties of Biological NetworksSummaryExercisesReferences

  • Rough Fuzzy -Medoids and Amino Acid Sequence Analysis

    The problem with using most pattern recognition algorithms to analyze biological subsequences is that they cannot recognize nonnumerical features such as the biochemical codes of amino acids. Investigating a proper encoding process before modeling the amino acids is then critical. This chapter uses the rough-fuzzy c-medoids to select a minimum set of most informative bio- basis strings. It introduces necessary notions of bio-basis function and the bio-basis string selection methods, and gives a description of hard c-medoids, fuzzy c-medoids, possibilistic c-medoids, and fuzzy-possibilistic c-medoids. The chapter describes the rough-fuzzy c-medoids algorithm, along with the rough c-medoids algorithm, for relational data clustering. It also gives an overview of the application of different c-medoids algorithms for bio-basis string selection, along with the initialization method for the c-medoids algorithm. The chapter presents some quantitative measures to select most informative bio-basis strings, and reports on a few case studies and a comparison among different methods. fuzzy set theory; pattern clustering; rough set theory



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