The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Humberto González-díaz - One of the best experts on this subject based on the ideXlab platform.
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Multi-target Spectral Moment: QSAR for antiviral drugs vs. different viral species.
Analytica chimica acta, 2009Co-Authors: Francisco J. Prado-prado, Fernanda Borges, Eugenio Uriarte, Lazaro G. Perez-montoto, Humberto González-díazAbstract:The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target Spectral Moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a Spectral Moment analysis.
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Multi-target Spectral Moment: QSAR for antifungal drugs vs. different fungi species.
European journal of medicinal chemistry, 2009Co-Authors: Francisco J. Prado-prado, Fernanda Borges, Lazaro G. Perez-montoto, Humberto González-díazAbstract:Abstract The most important limitation of antifungal QSAR models is that they predict the biological activity of drugs against only one fungal species. This is determined due the fact that most of the up-to-date reported molecular descriptors encode only information about the molecular structure. Consequently, predicting the probability with which a drug is active against different fungal species with a single unifying model is a goal of major importance. Herein, we use the Markov Chain theory to calculate new multi-target Spectral Moments to fit a QSAR model that predicts the antifungal activity of more than 280 drugs against 90 fungi species. Linear discriminant analysis (LDA) was used to classify drugs into two classes as active or non-active against the different tested fungal species whose data we processed. The model correctly classifies 12 434 out of 12 566 non-active compounds (98.95%) and 421 out of 468 active compounds (89.96%). Overall training predictability was 98.63%. Validation of the model was carried out by means of external predicting series, the model classifying, thus, 6216 out of 6277 non-active compounds and 215 out of 239 active compounds. Overall training predictability was 98.7%. The present is the first attempt to calculate, within a unifying framework, the probabilities of antifungal action of drugs against many different species based on Spectral Moment’s analysis.
Francisco J. Prado-prado - One of the best experts on this subject based on the ideXlab platform.
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Multi-target Spectral Moment: QSAR for antiviral drugs vs. different viral species.
Analytica chimica acta, 2009Co-Authors: Francisco J. Prado-prado, Fernanda Borges, Eugenio Uriarte, Lazaro G. Perez-montoto, Humberto González-díazAbstract:The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target Spectral Moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a Spectral Moment analysis.
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Multi-target Spectral Moment: QSAR for antifungal drugs vs. different fungi species.
European journal of medicinal chemistry, 2009Co-Authors: Francisco J. Prado-prado, Fernanda Borges, Lazaro G. Perez-montoto, Humberto González-díazAbstract:Abstract The most important limitation of antifungal QSAR models is that they predict the biological activity of drugs against only one fungal species. This is determined due the fact that most of the up-to-date reported molecular descriptors encode only information about the molecular structure. Consequently, predicting the probability with which a drug is active against different fungal species with a single unifying model is a goal of major importance. Herein, we use the Markov Chain theory to calculate new multi-target Spectral Moments to fit a QSAR model that predicts the antifungal activity of more than 280 drugs against 90 fungi species. Linear discriminant analysis (LDA) was used to classify drugs into two classes as active or non-active against the different tested fungal species whose data we processed. The model correctly classifies 12 434 out of 12 566 non-active compounds (98.95%) and 421 out of 468 active compounds (89.96%). Overall training predictability was 98.63%. Validation of the model was carried out by means of external predicting series, the model classifying, thus, 6216 out of 6277 non-active compounds and 215 out of 239 active compounds. Overall training predictability was 98.7%. The present is the first attempt to calculate, within a unifying framework, the probabilities of antifungal action of drugs against many different species based on Spectral Moment’s analysis.
Georgios B Giannakis - One of the best experts on this subject based on the ideXlab platform.
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a Spectral Moment approach to velocity estimation in mobile communications
Wireless Communications and Networking Conference, 2000Co-Authors: Cihan Tepedelenlioglu, Georgios B GiannakisAbstract:Estimation of the maximum Doppler spread, or equivalently the vehicle velocity is useful in improving handoff algorithms, and necessary for the optimal tuning of parameters for systems that adapt to changing channel conditions. We provide a novel velocity estimator based on the Spectral Moments of the in-phase and the quadrature-phase components or the squared envelope of the received signal. We characterize the joint effects of the Ricean K-factor, the directivity and the angle of non-isotropic scattering, and the effects of additive white noise on our estimator and other covariance-based velocity estimators analytically. Simulations illustrate our approach and compare with existing techniques.
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WCNC - A Spectral Moment approach to velocity estimation in mobile communications
2000 IEEE Wireless Communications and Networking Conference. Conference Record (Cat. No.00TH8540), 1Co-Authors: Cihan Tepedelenlioglu, Georgios B GiannakisAbstract:Estimation of the maximum Doppler spread, or equivalently the vehicle velocity is useful in improving handoff algorithms, and necessary for the optimal tuning of parameters for systems that adapt to changing channel conditions. We provide a novel velocity estimator based on the Spectral Moments of the in-phase and the quadrature-phase components or the squared envelope of the received signal. We characterize the joint effects of the Ricean K-factor, the directivity and the angle of non-isotropic scattering, and the effects of additive white noise on our estimator and other covariance-based velocity estimators analytically. Simulations illustrate our approach and compare with existing techniques.
Volodymyr Turkowski - One of the best experts on this subject based on the ideXlab platform.
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Spectral Moment sum rules for the retarded Green's function and self-energy of the inhomogeneous Bose-Hubbard model in equilibrium and nonequilibrium
Physical Review A, 2013Co-Authors: James Freericks, Volodymyr Turkowski, H. R. Krishnamurthy, Michael KnapAbstract:We derive exact expressions for the zeroth and the first three Spectral Moment sum rules for the retarded Green's function and for the zeroth and the first Spectral Moment sum rules for the retarded self-energy of the inhomogeneous Bose-Hubbard model in nonequilibrium, when the local on-site repulsion and the chemical potential are time-dependent, and in the presence of an external time-dependent electromagnetic field. We also evaluate these expressions for the homogeneous case in equilibrium, where all time dependence and external fields vanish. Unlike similar sum rules for the Fermi-Hubbard model, in the Bose-Hubbard model case, the sum rules often depend on expectation values that cannot be determined simply from parameters in the Hamiltonian like the interaction strength and chemical potential but require knowledge of equal-time many-body expectation values from some other source. We show how one can approximately evaluate these expectation values for the Mott-insulating phase in a systematic strong-coupling expansion in powers of the hopping divided by the interaction. We compare the exact Moment relations to the calculated Moments of Spectral functions determined from a variety of different numerical approximations and use them to benchmark their accuracy. DOI: 10.1103/PhysRevA.87.013628
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inhomogeneous Spectral Moment sum rules for the retarded green function and self energy of strongly correlated electrons or ultracold fermionic atoms in optical lattices
Physical Review B, 2009Co-Authors: James Freericks, Volodymyr TurkowskiAbstract:Spectral Moment sum rules are presented for the inhomogeneous many-body problem described by the fermionic Falicov-Kimball or Hubbard models. These local sum rules allow for arbitrary hoppings, site energies, and interactions. They can be employed to quantify the accuracy of numerical solutions to the inhomogeneous many-body problem such as strongly correlated multilayered devices, ultracold atoms in an optical lattice with a trap potential, strongly correlated systems that are disordered, or systems with nontrivial spatial ordering such as a charge-density wave or a spin-density wave. We also show how the Spectral Moment sum rules determine the asymptotic behavior of the Green function, self-energy, and dynamical mean field when applied to the dynamical mean-field theory solution of the many-body problem. In particular, we illustrate in detail how one can dramatically reduce the number of Matsubara frequencies needed to solve the Falicov-Kimball model while still retaining high precision, and we sketch how one can incorporate these results into Hirsch-Fye quantum Monte Carlo solvers for the Hubbard (or more complicated) models. Since the solution of inhomogeneous problems is significantly more time consuming than periodic systems, efficient use of these sum rules can provide a dramatic speed up in the computational time required to solvemore » the many-body problem. We also discuss how these sum rules behave in nonequilibrium situations as well, where the Hamiltonian has explicit time dependence due to a driving field or due to the time-dependent change in a parameter such as the interaction strength or the origin of the trap potential.« less
Humberto Gonzalezdiaz - One of the best experts on this subject based on the ideXlab platform.
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multi target Spectral Moment qsar versus ann for antiparasitic drugs against different parasite species
Bioorganic & Medicinal Chemistry, 2010Co-Authors: Francisco J Pradoprado, Xerardo Garciamera, Humberto GonzalezdiazAbstract:There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target Spectral Moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using Spectral Moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on Spectral Moment analysis.
