Conferences related to Chromosome mapping

Back to Top

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

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


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


2019 IEEE 19th International Conference on Nanotechnology (IEEE-NANO)

DNA Nanotechnology Micro-to-nano-scale Bridging Nanobiology and Nanomedicine Nanoelectronics Nanomanufacturing and Nanofabrication Nano Robotics and Automation Nanomaterials Nano-optics, Nano-optoelectronics and Nanophotonics Nanofluidics Nanomagnetics Nano/Molecular Heat Transfer & Energy Conversion Nanoscale Communication and Networks Nano/Molecular Sensors, Actuators and Systems


2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)

This conference presents excellent, novel, and contemporary papers covering all aspects of Data ? including Scientific Theory and Technology-Based Applications. New data analytic algorithms, technologies, and tools are sought to be able to manage, integrate, and utilize large amounts of data despite hardware, software, and/or bandwidth constraints; to construct models yielding important data insights; and, to create visualizations to aid in presenting and understanding the data. System development and integration needs to also adapt to these new algorithms, technologies, tools, and needs. This conference and its constituents support the development of technologies and applications for peaceful purposes to improve the human condition.


More Conferences

Periodicals related to Chromosome mapping

Back to Top

Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


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; ...


Computers, IEEE Transactions on

Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...


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.


More Periodicals

Most published Xplore authors for Chromosome mapping

Back to Top

Xplore Articles related to Chromosome mapping

Back to Top

Deducing Causal Relationships among Different Histone Modifications, DNA Methylation and Gene Expression

2009 Fifth International Conference on Natural Computation, 2009

Histone modifications and DNA methylation are two major epigenetic factors regulating gene expression. However, the mechanism in which DNA methylation and histone modifications co-regulate gene expression was little studied. In our study, classifications of DNA methylation and gene expression showed the complicated relationship between gene expression and epigenetic factors. A Bayesian network was constructed by using the high-resolution maps of ...


Failure restoration and array synthesis using genetic algorithms

Proceedings of the Eighteenth National Radio Science Conference. NRSC'2001 (IEEE Cat. No.01EX462), 2001

This paper demonstrates the application of genetic algorithms (GAs) in synthesizing linear antenna arrays to achieve a given pattern. It is applied for both Dolf-Chebychev and Gauss patterns. Genetic algorithms were also used to obtain an enhanced Dolf-Chebychev pattern. Also they were finally applied to restore the Dolf-Chebychev pattern behavior in the case of element failure.


Classification of mouse chromosomes using artificial neural networks

Proceedings of International Conference on Neural Networks (ICNN'96), 1996

This paper presents the results of our experiments for classification of mouse chromosomes using a radial basis function (RBF) and a probabilistic neural network (PNN). The fast orthogonal search (FOS) was utilized for training of the RBF network. There were 840 training chromosomes and 540 testing chromosomes. The best classification error rate was recorded at 16.4% for the RBF network. ...


Cortical bone morphology and mechanosensitivity are modulated by genetic variations

2007 IEEE 33rd Annual Northeast Bioengineering Conference, 2007

Genetic variations are a principal factor controlling bone morphology and mechanosensitivity. Genetically distinct inbred strains of mice provide an effective model to separate genetic from epigenetic factors. For example, BALE/cByJ mice not only have different morphology than C3H mice, but also lose most of their trabecular bone volume in the femur during disuse while C3H mice are comparatively unresponsive. Double-crossing ...


A set-oriented genetic algorithm and the knapsack problem

Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2001

Genetic algorithms (GAs) have been used to solve NP-complete problems, such as the knapsack problem, effectively. One difficulty in applying GAs to the knapsack problem is that the bit-string representation of the canonical GA chromosome does not provide a direct mapping of the problem on to the GA chromosome. In this paper, a new chromosome representation of the GA is ...


More Xplore Articles

Educational Resources on Chromosome mapping

Back to Top

IEEE.tv Videos

Mapping Human to Robot Motion with Functional Anthropomorphism for Teleoperation and Telemanipulation with Robot Arm Hand Systems
3D Body-Mapping for Severely Burned Patients - Julia Loegering - IEEE EMBS at NIH, 2019
Road-Mapping Session with Deepa Prahalad at Internet Inclusion: Advancing Solutions, Delhi, 2016
Q&A with Jack Gallant: IEEE Brain Podcast, Episode 11
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
NEREID: Systems Design & Heterogeneous Integration: Danilo Demarchi at INC 2019
LPIRC: A Facebook Approach to Benchmarking ML Workload
2011 IEEE Awards Matt Ettus HKN Eminent Member Recognition
Neato Robotics' Roomba Competitor
Application of Connectivity in Automated Driving - Gaurav Bansal: Brooklyn 5G Summit 2017
Applying Control Theory to the Design of Cancer Therapy
Closing Remarks & Robot Demos - VIC Summit 2019
Massive MIMO Active Antenna Arrays for Advanced Wireless Communications: IEEE CAS lecture by Dr. Mihai Banu
Panel: IoT - Smart Networks & Social Innovations - VIC Summit 2019
IROS TV 2019-U.S. Military Academy, West Point- Robotics Research Center
A Bayesian Approach for Spatial Clustering - IEEE CIS Webinar
Light Our Future - IEEE Photonics Society
Connecting to Thrive Panel - Internet Inclusion: Global Connect Stakeholders Advancing Solutions, Washington DC, 2016
Setting the Conditions for 2020 - Internet Inclusion: Global Connect Stakeholders Advancing Solutions, Washington DC, 2016
Robotics History: Narratives and Networks Oral Histories: Gary Bradsky

IEEE-USA E-Books

  • Deducing Causal Relationships among Different Histone Modifications, DNA Methylation and Gene Expression

    Histone modifications and DNA methylation are two major epigenetic factors regulating gene expression. However, the mechanism in which DNA methylation and histone modifications co-regulate gene expression was little studied. In our study, classifications of DNA methylation and gene expression showed the complicated relationship between gene expression and epigenetic factors. A Bayesian network was constructed by using the high-resolution maps of histone modifications, DNA methylation and gene expression in human CD4+ T cells to deduce causal and combinatorial relationships among them. PolII was found as the only direct regulator to gene expression, which was not found in prior studies. Our Bayesian network showed that epigenetic factors such as H3K4me3, H3K27me3 and DNA methylation are key regulators of gene expression, though indirectly. However they were considered to combinatorially stabilize the state and structure of chromatin.

  • Failure restoration and array synthesis using genetic algorithms

    This paper demonstrates the application of genetic algorithms (GAs) in synthesizing linear antenna arrays to achieve a given pattern. It is applied for both Dolf-Chebychev and Gauss patterns. Genetic algorithms were also used to obtain an enhanced Dolf-Chebychev pattern. Also they were finally applied to restore the Dolf-Chebychev pattern behavior in the case of element failure.

  • Classification of mouse chromosomes using artificial neural networks

    This paper presents the results of our experiments for classification of mouse chromosomes using a radial basis function (RBF) and a probabilistic neural network (PNN). The fast orthogonal search (FOS) was utilized for training of the RBF network. There were 840 training chromosomes and 540 testing chromosomes. The best classification error rate was recorded at 16.4% for the RBF network. This result is better than the best available result of 18.3% which was achieved with much more training chromosomes.

  • Cortical bone morphology and mechanosensitivity are modulated by genetic variations

    Genetic variations are a principal factor controlling bone morphology and mechanosensitivity. Genetically distinct inbred strains of mice provide an effective model to separate genetic from epigenetic factors. For example, BALE/cByJ mice not only have different morphology than C3H mice, but also lose most of their trabecular bone volume in the femur during disuse while C3H mice are comparatively unresponsive. Double-crossing these two strains, and thus generating a new population of mice with a wide range mechanosensitivity, allows the identification of the specific chromosomal regions that define the (distinct) response of the tissue to an environmental stimulus. Here, we report disuse-induced changes in femoral mid-diaphysial morphology of F2 mice population that were scanned by in-vivo muCT at baseline, upon 3wk disuse and 3wk of subsequent reambulation. Results indicate heterogeneous population was achieved for both baseline morphology and mechanosensitivity (longitudinal change during disuse and upon reambulation). Preliminary QTL maps indicate several chromosomal regions that may be responsible for the skeletal morphology.

  • A set-oriented genetic algorithm and the knapsack problem

    Genetic algorithms (GAs) have been used to solve NP-complete problems, such as the knapsack problem, effectively. One difficulty in applying GAs to the knapsack problem is that the bit-string representation of the canonical GA chromosome does not provide a direct mapping of the problem on to the GA chromosome. In this paper, a new chromosome representation of the GA is proposed, called the "set-oriented GA". A chromosome in the set-oriented GA is a set, while in the canonical GA it is a bit-string. Crossover and mutation operators are described using the combinations of set operations, such as union, intersection and complement. A performance comparison of the canonical GA and the set-oriented GA on the knapsack problem is presented. The set- oriented GA turns out to be not only effective in representing the problem but also efficient in finding the solution.

  • Genetic algorithm operators effect in optimizing the antenna array pattern synthesis

    This paper demonstrates the genetic algorithms (GA's) operators for providing optimal solution with minimum runtime in the pattern synthesis problem for an antenna array. Dolf-Chebyshev is considered to be the target pattern. Generation number importance in avoiding extra or infinite loops is clarified. A comparison between the effect of different selection strategies, crossover and mutation methods in reaching the final solution is shown. Finally the role of each operator in reaching the optimal solution is illustrated.

  • Conversion Of Helix-turn-helix Motif Sequence-specific DNA Binding Proteins Into Site-specific DNA Cleavage Agents

    None

  • A genetic algorithm based approach for multi-objective data-flow graph optimization

    This paper presents a genetic algorithm based approach for algebraic optimization of behavioral system specifications. We introduce a chromosomal representation of data-flow graphs (DFG) which ensures that the correctness of algebraic transformations realized by the underlying genetic operators selection, recombination, and mutation is always preserved. We present substantial fitness functions for both the minimization of overall resource costs and critical path length. We also demonstrate that, due to their flexibility, genetic algorithms can be simply adapted to different objective functions which is shown for power optimization. In order to avoid inferior results caused by the counteracting demands on resources of different basic blocks, all DFGs of the input description are optimized concurrently. Experimental results for several standard benchmarks prove the efficiency of our approach.

  • A RFID Network Planning Method Based on Genetic Algorithm

    Radio frequency identification (RFID) has a widespread application in reality, and RFID wireless network planning is one of the most important issues in this field. In this paper, we propose a RFID network planning method based on genetic algorithm. The main points include mapping RFID network, planning issues into genetic algorithms, presenting problem states by using gene and chromosome, and implementing the mechanisms of individual selection and genetic operation. Experiments show this approach is feasible and practical.

  • Solving four-colouring map problem using genetic algorithm

    The authors outline an approach to four-coloring of maps using a genetic algorithm. The objective of this map coloring problem is to shade each region of the map with a color such that no adjacent regions are of the same color. Simulation results show that the 48-region USA map problem can be solved on a PC platform within 400 generations.<<ETX>>



Standards related to Chromosome mapping

Back to Top

No standards are currently tagged "Chromosome mapping"


Jobs related to Chromosome mapping

Back to Top