دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — بخش بیست و پنجم
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 | An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Antonio d’Acierno, Massimo Esposito, Giuseppe De Pietro |
| چکیده / توضیح | The diagnosis of many diseases can be often formulated as a decision problem; uncertainty affects these problems so that many computerized Diagnostic Decision Support Systems (in the following, DDSSs) have been developed to aid the physician in interpreting clinical data and thus to improve the quality of the whole process. Fuzzy logic, a well established attempt at the formalization and mechanization of human capabilities in reasoning and deciding with noisy information, can be profitably used. Recently, we informally proposed a general methodology to automatically build DDSSs on the top of fuzzy knowledge extracted from data. |
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| عنوان اصلی مقاله | Fuzzy clustering of CPP family in plants with evolution and interaction analyses |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Tao Lu, Yongchao Dou, Chi Zhang |
| چکیده / توضیح | Transcription factors have been studied intensively because they play an important role in gene expression regulation. However, the transcription factors in the CPP family (cystein-rich polycomb-like protein), compared with other transcription factor families, have not received sufficient attention, despite their wide prevalence in a broad spectrum of species, from plants to animals. The total number of known CPP transcription factors in plants is 111 from 16 plants, but only 2 of them have been studied so far, namely TSO1 and CPP1 in Arabidopsis thaliana and soybean, respectively. |
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| عنوان اصلی مقاله | In silico experimental evolution: a tool to test evolutionary scenarios |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Bérénice Batut, David P Parsons, Stephan Fischer, Guillaume Beslon, Carole Knibbe |
| چکیده / توضیح | Comparative genomics has revealed that some species have exceptional genomes, compared to their closest relatives. For instance, some species have undergone a strong reduction of their genome with a drastic reduction of their genic repertoire. Deciphering the causes of these atypical trajectories can be very difficult because of the many phenomena that are intertwined during their evolution (e.g. changes of population size, environment structure and dynamics, selection strength, mutation rates...). Here we propose a methodology based on synthetic experiments to test the individual effect of these phenomena on a population of simulated organisms. We developed an evolutionary model - aevol - in which evolutionary conditions can be changed one at a time to test their effects on genome size and organization (e.g. coding ratio). To illustrate the proposed approach, we used aevol to test the effects of a strong reduction in the selection strength on a population of (simulated) bacteria. Our results show that this reduction of selection strength leads to a genome reduction of ~35% with a slight loss of coding sequences (~15% of the genes are lost - mainly those for which the contribution to fitness is the lowest). More surprisingly, under a low selection strength, genomes undergo a strong reduction of the noncoding compartment (~55% of the noncoding sequences being lost). These results are consistent with what is observed in reduced Prochlorococcus strains (marine cyanobacteria) when compared to close relatives. |
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| عنوان اصلی مقاله | Improved multi-level protein–protein interaction prediction with semantic-based regularization |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Claudio Saccà, Stefano Teso, Michelangelo Diligenti, Andrea Passerini |
| چکیده / توضیح | Protein–protein interactions can be seen as a hierarchical process occurring at three related levels: proteins bind by means of specific domains, which in turn form interfaces through patches of residues. Detailed knowledge about which domains and residues are involved in a given interaction has extensive applications to biology, including better understanding of the binding process and more efficient drug/enzyme design. Alas, most current interaction prediction methods do not identify which parts of a protein actually instantiate an interaction. Furthermore, they also fail to leverage the hierarchical nature of the problem, ignoring otherwise useful information available at the lower levels; when they do, they do not generate predictions that are guaranteed to be consistent between levels. |
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| عنوان اصلی مقاله | Quality versus accuracy: result of a reanalysis of protein-binding microarrays from the DREAM5 challenge by using BayesPI2 including dinucleotide interdependence |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Junbai Wang |
| چکیده / توضیح | Computational modeling transcription factor (TF) sequence specificity is an important research topic in regulatory genomics. A systematic comparison of 26 algorithms to learn TF-DNA binding specificity in in vitro protein-binding microarray (PBM) data was published recently, but the quality of those examined PBMs was not evaluated completely. |
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| عنوان اصلی مقاله | Linear fuzzy gene network models obtained from microarray data by exhaustive search |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Bahrad A Sokhansanj, J Patrick Fitch, Judy N Quong, Andrew A Quong |
| چکیده / توضیح | Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling. |
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| عنوان اصلی مقاله | An Entropy-based gene selection method for cancer classification using microarray data |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Xiaoxing Liu, Arun Krishnan, Adrian Mondry |
| چکیده / توضیح | Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult. |
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| عنوان اصلی مقاله | Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Susmita Datta, Somnath Datta |
| چکیده / توضیح | A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species. |
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| عنوان اصلی مقاله | Evaluation of clustering algorithms for gene expression data |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Susmita Datta, Somnath Datta |
| چکیده / توضیح | Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped together according to their expression profiles using one of numerous clustering algorithms that exist in the statistics and machine learning literature. A closely related problem is that of selecting a clustering algorithm that is "optimal" in some sense from a rather impressive list of clustering algorithms that currently exist. |
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| عنوان اصلی مقاله | How accurate and statistically robust are catalytic site predictions based on closeness centrality? |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Eric Chea, Dennis R Livesay |
| چکیده / توضیح | We examine the accuracy of enzyme catalytic residue predictions from a network representation of protein structure. In this model, amino acid α-carbons specify vertices within a graph and edges connect vertices that are proximal in structure. Closeness centrality, which has shown promise in previous investigations, is used to identify important positions within the network. Closeness centrality, a global measure of network centrality, is calculated as the reciprocal of the average distance between vertex i and all other vertices. |
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