• MeSH
• algoritmy MeSH
• artefakty MeSH
• diagnostické zobrazování trendy   MeSH
• entropie * MeSH
• Fourierova analýza MeSH
• lidé MeSH
• nukleární magnetická rezonance biomolekulární * metody   MeSH
• počítačové zpracování obrazu * metody   využití   MeSH
• poměr signál - šum MeSH
• spektrální analýza trendy   využití   MeSH
• Check Tag
• lidé MeSH

• MeSH
• algoritmy MeSH
• financování organizované MeSH
• lidé MeSH
• měření krevního tlaku metody   přístrojové vybavení   využití   MeSH
• metoda Monte Carlo MeSH
• monitory krevního tlaku využití   MeSH
• pletysmografie využití   MeSH
• počítačové zpracování signálu MeSH
• rychlost toku krve fyziologie   MeSH
• stanovení krevního objemu metody   využití   MeSH
• statistika jako téma metody   MeSH
• zaváděcí katétry využití   MeSH
• Check Tag
• lidé MeSH

Maximum entropy estimation is of broad interest for inferring properties of systems across many disciplines. Using a recently introduced technique for estimating the maximum entropy of a set of random discrete variables when conditioning on bivariate mutual informations and univariate entropies, we show how this can be used to estimate the direct network connectivity between interacting units from observed activity. As a generic example, we consider phase oscillators and show that our approach is typically superior to simply using the mutual information. In addition, we propose a nonparametric formulation of connected informations, used to test the explanatory power of a network description in general. We give an illustrative example showing how this agrees with the existing parametric formulation, and demonstrate its applicability and advantages for resting-state human brain networks, for which we also discuss its direct effective connectivity. Finally, we generalize to continuous random variables and vastly expand the types of information-theoretic quantities one can condition on. This allows us to establish significant advantages of this approach over existing ones. Not only does our method perform favorably in the undersampled regime, where existing methods fail, but it also can be dramatically less computationally expensive as the cardinality of the variables increases.

• MeSH
• entropie MeSH
• informační systémy * MeSH
• lidé MeSH
• nervová síť MeSH
• teoretické modely MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• práce podpořená grantem MeSH

Reconstructing the structural connectivity between interacting units from observed activity is a challenge across many different disciplines. The fundamental first step is to establish whether or to what extent the interactions between the units can be considered pairwise and, thus, can be modeled as an interaction network with simple links corresponding to pairwise interactions. In principle, this can be determined by comparing the maximum entropy given the bivariate probability distributions to the true joint entropy. In many practical cases, this is not an option since the bivariate distributions needed may not be reliably estimated or the optimization is too computationally expensive. Here we present an approach that allows one to use mutual informations as a proxy for the bivariate probability distributions. This has the advantage of being less computationally expensive and easier to estimate. We achieve this by introducing a novel entropy maximization scheme that is based on conditioning on entropies and mutual informations. This renders our approach typically superior to other methods based on linear approximations. The advantages of the proposed method are documented using oscillator networks and a resting-state human brain network as generic relevant examples.

• MeSH
• entropie MeSH
• informační systémy * MeSH
• korelace dat MeSH
• neuronové sítě MeSH
• teoretické modely MeSH
• Publikační typ
• práce podpořená grantem MeSH

Feature selection is a significant part of many machine learning applications dealing with small-sample and high-dimensional data. Choosing the most important features is an essential step for knowledge discovery in many areas of biomedical informatics. The increased popularity of feature selection methods and their frequent utilisation raise challenging new questions about the interpretability and stability of feature selection techniques. In this study, we compared the behaviour of ten state-of-the-art filter methods for feature selection in terms of their stability, similarity, and influence on prediction performance. All of the experiments were conducted on eight two-class datasets from biomedical areas. While entropy-based feature selection appears to be the most stable, the feature selection techniques yielding the highest prediction performance are minimum redundance maximum relevance method and feature selection based on Bhattacharyya distance. In general, univariate feature selection techniques perform similarly to or even better than more complex multivariate feature selection techniques with high-dimensional datasets. However, with more complex and smaller datasets multivariate methods slightly outperform univariate techniques.

• MeSH
• algoritmy MeSH
• databáze faktografické MeSH
• lidé MeSH
• multivariační analýza MeSH
• Parkinsonova nemoc diagnóza   MeSH
• sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů metody   MeSH
• software MeSH
• statistické modely MeSH
• výpočetní biologie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• srovnávací studie MeSH

• MeSH
• matematické výpočty počítačové MeSH
• matematika MeSH
• numerická analýza pomocí počítače * MeSH
• Publikační typ
• monografie MeSH

• Konspekt
• Počítačová věda. Výpočetní technika. Informační technologie
• NLK Obory
• přírodní vědy
• přírodní vědy

PURPOSE: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O(6)-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. METHODS: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. RESULTS: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78-0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. CONCLUSIONS: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

• MeSH
• DNA modifikační methylasy genetika   MeSH
• enzymy opravy DNA genetika   MeSH
• glioblastom diagnostické zobrazování   genetika   chirurgie   MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• metylace DNA * MeSH
• mozek diagnostické zobrazování   chirurgie   MeSH
• nádorové biomarkery genetika   MeSH
• nádorové supresorové proteiny genetika   MeSH
• nádory mozku diagnostické zobrazování   genetika   chirurgie   MeSH
• promotorové oblasti (genetika) MeSH
• retrospektivní studie MeSH
• ROC křivka MeSH
• support vector machine MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The cat flea (Ctenocephalides felis) is the most common parasite of domestic cats and dogs worldwide. Due to the morphological ambiguity of C. felis and a lack of - particularly largescale - phylogenetic data, we do not know whether global C. felis populations are morphologically and genetically conserved, or whether human-mediated migration of domestic cats and dogs has resulted in homogenous global populations. To determine the ancestral origin of the species and to understand the level of global pervasion of the cat flea and related taxa, our study aimed to document the distribution and phylogenetic relationships of Ctenocephalides fleas found on cats and dogs worldwide. We investigated the potential drivers behind the establishment of regional cat flea populations using a global collection of fleas from cats and dogs across six continents. We morphologically and molecularly evaluated six out of the 14 known taxa comprising genus Ctenocephalides, including the four original C. felis subspecies (Ctenocephalides felis felis, Ctenocephalides felis strongylus, Ctenocephalides felis orientis and Ctenocephalides felis damarensis), the cosmopolitan species Ctenocephalides canis and the African species Ctenocephalides connatus. We confirm the ubiquity of the cat flea, representing 85% of all fleas collected (4357/5123). Using a multigene approach combining two mitochondrial (cox1 and cox2) and two nuclear (Histone H3 and EF-1α) gene markers, as well as a cox1 survey of 516 fleas across 56 countries, we demonstrate out-of-Africa origins for the genus Ctenocephalides and high levels of genetic diversity within C. felis. We define four bioclimatically limited C. felis clusters (Temperate, Tropical I, Tropical II and African) using maximum entropy modelling. This study defines the global distribution, African origin and phylogenetic relationships of global Ctenocephalides fleas, whilst resolving the taxonomy of the C. felis subspecies and related taxa. We show that humans have inadvertently precipitated the expansion of C. felis throughout the world, promoting diverse population structure and bioclimatic plasticity. By demonstrating the link between the global cat flea communities and their affinity for specific bioclimatic niches, we reveal the drivers behind the establishment and success of the cat flea as a global parasite.

• MeSH
• Ctenocephalides klasifikace   genetika   růst a vývoj   MeSH
• fylogeneze MeSH
• genetické markery MeSH
• infestace blechami parazitologie   veterinární   MeSH
• kočky MeSH
• lidé MeSH
• nemoci koček parazitologie   MeSH
• nemoci psů parazitologie   MeSH
• psi MeSH
• zvířata MeSH
• Check Tag
• kočky MeSH
• lidé MeSH
• mužské pohlaví MeSH
• psi MeSH
• ženské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Afrika MeSH