دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — بخش بیست و ششم
منطق فازی (Fuzzy Logic) اولین بار در پی تنظیم نظریه مجموعه‌های فازی به وسیله پروفسور لطفی زاده (۱۹۶۵ میلادی) در صحنه محاسبات نو ظاهر شد. در واقع منطق فازی از منطق ارزش‌های «صفر و یک» نرم‌افزارهای کلاسیک فراتر رفته و درگاهی جدید برای دنیای علوم نرم‌افزاری و رایانه‌ها می‌گشاید، زیرا فضای شناور و نامحدود بین اعداد صفر و یک را نیز در منطق و استدلال‌های خود به کار می‌گیرد. در ادامه مقالات علمی انتشارات بین المللی اشپرینگر (Springer) در زمینه منطق فازی (Fuzzy Logic) برای دانلود آمده است. می توانید برای دانلود هر یک از مقالات از سرور دانلود متلب سایت، بر روی لینک دانلود هر یک از آن ها، کلیک کنید.
این نوشته حاوی بخشی از مجموعه کامل مقالات است. برای دریافت سایر بخش ها، به لینک زیر مراجعه نمایید:
دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — فهرست اصلی
| عنوان اصلی مقاله | Accelerated search for biomolecular network models to interpret high-throughput experimental data |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Suman Datta, Bahrad A Sokhansanj |
| چکیده / توضیح | The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables. |
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| عنوان اصلی مقاله | Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Xiaohua Hu, Fang-Xiang Wu |
| چکیده / توضیح | Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model. |
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| عنوان اصلی مقاله | Selection of long oligonucleotides for gene expression microarrays using weighted rank-sum strategy |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Guangan Hu, Manuel Llinás, Jingguang Li, Peter Rainer Preiser, Zbynek Bozdech |
| چکیده / توضیح | The design of long oligonucleotides for spotted DNA microarrays requires detailed attention to ensure their optimal performance in the hybridization process. The main challenge is to select an optimal oligonucleotide element that represents each genetic locus/gene in the genome and is unique, devoid of internal structures and repetitive sequences and its Tm is uniform with all other elements on the microarray. Currently, all of the publicly available programs for DNA long oligonucleotide microarray selection utilize various combinations of cutoffs in which each parameter (uniqueness, Tm, and secondary structure) is evaluated and filtered individually. The use of the cutoffs can, however, lead to information loss and to selection of suboptimal oligonucleotides, especially for genomes with extreme distribution of the GC content, a large proportion of repetitive sequences or the presence of large gene families with highly homologous members. |
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| عنوان اصلی مقاله | Fuzzy association rules for biological data analysis: A case study on yeast |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Francisco J Lopez, Armando Blanco, Fernando Garcia, Carlos Cano, Antonio Marin |
| چکیده / توضیح | Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data. |
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| عنوان اصلی مقاله | Novel computational methods for increasing PCR primer design effectiveness in directed sequencing |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Kelvin Li, Anushka Brownley, Timothy B Stockwell, Karen Beeson, Tina C McIntosh, Dana Busam, Steve Ferriera, Sean Murphy, Samuel Levy |
| چکیده / توضیح | Polymerase chain reaction (PCR) is used in directed sequencing for the discovery of novel polymorphisms. As the first step in PCR directed sequencing, effective PCR primer design is crucial for obtaining high-quality sequence data for target regions. Since current computational primer design tools are not fully tuned with stable underlying laboratory protocols, researchers may still be forced to iteratively optimize protocols for failed amplifications after the primers have been ordered. Furthermore, potentially identifiable factors which contribute to PCR failures have yet to be elucidated. This inefficient approach to primer design is further intensified in a high-throughput laboratory, where hundreds of genes may be targeted in one experiment. |
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| عنوان اصلی مقاله | Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Weijun Luo, Kurt D Hankenson, Peter J Woolf |
| چکیده / توضیح | Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mechanistic relationships from quantitative biological data. In this work we introduce a new statistical learning strategy, MI3 that addresses three common issues in previous methods simultaneously: (1) handling of continuous variables, (2) detection of more complex three-way relationships and (3) better differentiation of causal versus confounding relationships. With these improvements, we provide a more realistic representation of the underlying biological system. |
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| عنوان اصلی مقاله | Reverse engineering module networks by PSO-RNN hybrid modeling |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Yuji Zhang, Jianhua Xuan, Benildo G de los Reyes, Robert Clarke, Habtom W Ressom |
| چکیده / توضیح | Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell; (2) complex dynamic and nonlinear relationships exist among genes; (3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules. |
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| عنوان اصلی مقاله | 2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Jack Y Yang, Andrzej Niemierko, Ruzena Bajcsy, Dong Xu, Brian D Athey, Aidong Zhang, Okan K Ersoy, Guo-zheng Li, Mark Borodovsky, Joe C Zhang, Hamid R Arabnia, Youping Deng, A Keith Dunker, Yunlong Liu, Arif Ghafoor |
| چکیده / توضیح | Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine. |
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| عنوان اصلی مقاله | Immunoproteomic analysis of outer membrane proteins and extracellular proteins of Actinobacillus pleuropneumoniae JL03 serotype 3 |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Yonghong Liao, Junhua Deng, Anding Zhang, Mingguang Zhou, Yong Hu, Huanchun Chen, Meilin Jin |
| چکیده / توضیح | Actinobacillus pleuropneumoniae is the causative agent of porcine contagious pleuropneumonia, a highly contagious respiratory infection in pigs, and all the 15 serotypes are able to cause disease. Current vaccines including subunit vaccines could not provide satisfactory protection against A. pleuropneumoniae. In this study, the immunoproteomic approach was applied to the analysis of extracellular and outer membrane proteins of A. pleuropneumoniae JL03 serotype 3 for the identification of novel immunogenic proteins for A. pleuropneumoniae. |
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| عنوان اصلی مقاله | NeRvolver: a computational intelligence-based system for automated construction, tuning, and analysis of neuronal models |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Emlyne Forren, Myles Johnson-Gray, Parth Patel, Tomasz G Smolinski |
| چکیده / توضیح | Building neuronal models, even on the single-cell level, requires tremendous amounts of effort invested in the design of a realistic and functioning structure (e.g., the number and characteristics of compartments) and proper tuning of the model’s parameter values (e.g., membrane and axial conductances). Due to the ever-increasing computational power of modern computers facilitating the use of more and more complex neuronal models, hand-tuning of models’ parameters is becoming less and less feasible. Therefore, automated methods for neuronal model construction have been lately gaining much attention. Although a significant number of techniques for automatic model parameter estimation have been recently proposed (e.g., [1, 3, 4]), they are predominantly limited to “fine-tuning” of already designed models in a predetermined search space of parameter values. Furthermore, although unquestionably successful, present approaches are almost exclusively domain-specific, designed to deal with par ... |
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