The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Alexander Tropsha - One of the best experts on this subject based on the ideXlab platform.
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modeling liver related adverse effects of drugs using kNearest Neighbor quantitative structure activity relationship Method
Chemical Research in Toxicology, 2010Co-Authors: Amie D Rodgers, Hao Zhu, Denis Fourches, Ivan Rusyn, Alexander TropshaAbstract:Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals in both development and postmarketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the quantitative structure−activity relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity, and balanced (40/60) active/inactive ratios were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k Nearest Neighbor Method...
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quantitative structure activity relationship analysis of pyridinone hiv 1 reverse transcriptase inhibitors using the k Nearest Neighbor Method and qsar based database mining
Journal of Computer-aided Molecular Design, 2005Co-Authors: Jose L Medinafranco, Alexander Golbraikh, Scott Oloff, Rafael Castillo, Alexander TropshaAbstract:We have developed quantitative structure–activity relationship (QSAR) models for 44 non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs) of the pyridinone derivative type. The k Nearest Neighbor (kNN) variable selection approach was used. This Method utilizes multiple descriptors such as molecular connectivity indices, which are derived from two-dimensional molecular topology. The modeling process entailed extensive validation including the randomization of the target property (Y-randomization) test and the division of the dataset into multiple training and test sets to establish the external predictive power of the training set models. QSAR models with high internal and external accuracy were generated, with leave-one-out cross-validated R2 (q2) values ranging between 0.5 and 0.8 for the training sets and R2 values exceeding 0.6 for the test sets. The best models with the highest internal and external predictive power were used to search the National Cancer Institute database. Derivatives of the pyrazolo[3,4-d]pyrimidine and phenothiazine type were identified as promising novel NNRTIs leads. Several candidates were docked into the binding pocket of nevirapine with the AutoDock (version 3.0) software. Docking results suggested that these types of compounds could be binding in the NNRTI binding site in a similar mode to a known non-nucleoside inhibitor nevirapine.
Nikola Kasabov - One of the best experts on this subject based on the ideXlab platform.
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a graph based semi supervised k Nearest Neighbor Method for nonlinear manifold distributed data classification
Information Sciences, 2016Co-Authors: Yaqian Zhang, Lin Zhu, Jie Yang, Nikola KasabovAbstract:k Nearest Neighbors (kNN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based kNN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data. To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by constructing an R-level Nearest-Neighbor strengthened tree over the graph, and then compute a TRW matrix for similarity measurement purposes. After this, the Nearest Neighbors are identified according to the TRW matrix and the class label of a query point is determined by the sum of all the TRW weights of its Nearest Neighbors. To deal with online situations, we also propose a new algorithm to handle sequential samples based a local Neighborhood reconstruction. Comparison experiments are conducted on both synthetic data sets and real-world data sets to demonstrate the validity of the proposed new kNN algorithm and its improvements to other version of kNN algorithms. Given the widespread appearance of manifold structures in real-world problems and the popularity of the traditional kNN algorithm, the proposed manifold version kNN shows promising potential for classifying manifold-distributed data.
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ontology based framework for personalized diagnosis and prognosis of cancer based on gene expression data
International Conference on Neural Information Processing, 2008Co-Authors: Yingjie Hu, Nikola KasabovAbstract:This paper presents an ontology-based framework for personalized cancer decision support system based on gene expression data. This framework integrates the ontology and personalized cancer predictions using a variety of machine learning models. A case study is proposed for demonstrating the personalized cancer diagnosis and prognosis on two benchmark cancer gene data. Different Methods based on global, local and personalized modeling, including Multi Linear Regression (MLR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Evolving Classifier Function (ECF), weighted distance weighted variables K-Nearest Neighbor Method (WWKNN) and a transductive neuro-fuzzy inference system with weighted data normalization (TWNFI) are investigated. The development platform is general that can use multimodal information for personalized prediction and new knowledge creation within an evolving ontology framework.
Bosuk Yang - One of the best experts on this subject based on the ideXlab platform.
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multi step ahead direct prediction for the machine condition prognosis using regression trees and neuro fuzzy systems
Expert Systems With Applications, 2009Co-Authors: Van Tung Tran, Bosuk YangAbstract:This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false Nearest Neighbor Method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines' operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.
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dempster shafer regression for multi step ahead time series prediction towards data driven machinery prognosis
Mechanical Systems and Signal Processing, 2009Co-Authors: Bosuk YangAbstract:Abstract Predicting a sequence of future values of a time series using the descriptors observed in the past can be regarded as the stand-stone of data-driven machinery prognosis. The purpose of this paper is to develop a novel data-driven machinery prognosis strategy for industry application. First, the collected time-series degradation features are reconstructed based on the theorem of Takens, among which the reconstruction parameters, delay time and embedding dimension are selected by the C–C Method and the false Nearest Neighbor Method, respectively. Next, the Dempster–Shafer regression technique is developed to perform the task of time-series prediction. Moreover, the strategy of iterated multi-step-ahead prediction is discussed to keep track with the rapid variation of time-series signals during the data monitoring process in an industrial plant. The proposed scheme is validated using condition monitoring data of a methane compressor to predict the degradation trend. Experimental results show that the proposed Methods have a low error rate; hence, it can be regarded as an effective tool for data-driven machinery prognosis applications.
Gennaro Esposito - One of the best experts on this subject based on the ideXlab platform.
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entropy of two molecule correlated translational rotational motions using the kth Nearest Neighbor Method
Journal of Chemical Theory and Computation, 2021Co-Authors: Federico Fogolari, Gennaro Esposito, Bruce TidorAbstract:The entropy associated with rotations, translations, and their coupled motions provides an important contribution to the free energy of many physicochemical processes such as association and solvat...
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pdb2entropy and pdb2trent conformational and translational rotational entropy from molecular ensembles
Journal of Chemical Information and Modeling, 2018Co-Authors: Federico Fogolari, Ornela Maloku, Cedrix Dongmo J Foumthuim, Alessandra Corazza, Gennaro EspositoAbstract:Entropy calculation is an important step in the postprocessing of molecular dynamics trajectories or predictive models. In recent years the Nearest Neighbor Method has emerged as a powerful Method to deal in a flexible way with the dimensionality of the problem. Here we provide two programs, PBD2ENTROPY and PDB2TRENT that compute the conformational and translational–rotational entropy, respectively, based on the Nearest Neighbor Method. PDB2ENTROPY takes in input two files containing the following: (1) conformational ensembles of the same molecule(s) in PDB format and (2) definitions of torsion angles (a default file is provided where additional user definitions can be easily implemented). PDB2TRENT takes in a file containing samples of the complexed molecules, a string specifying atoms providing the reference framework to superimpose samples, and a string specifying atoms used to compute rotation and translation of one molecule with respect to the other. The C programs and sample demonstration data are a...
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PDB2ENTROPY and PDB2TRENT: Conformational and Translational–Rotational Entropy from Molecular Ensembles
2018Co-Authors: Federico Fogolari, Ornela Maloku, Cedrix Dongmo J Foumthuim, Alessandra Corazza, Gennaro EspositoAbstract:Entropy calculation is an important step in the postprocessing of molecular dynamics trajectories or predictive models. In recent years the Nearest Neighbor Method has emerged as a powerful Method to deal in a flexible way with the dimensionality of the problem. Here we provide two programs, PBD2ENTROPY and PDB2TRENT that compute the conformational and translational–rotational entropy, respectively, based on the Nearest Neighbor Method. PDB2ENTROPY takes in input two files containing the following: (1) conformational ensembles of the same molecule(s) in PDB format and (2) definitions of torsion angles (a default file is provided where additional user definitions can be easily implemented). PDB2TRENT takes in a file containing samples of the complexed molecules, a string specifying atoms providing the reference framework to superimpose samples, and a string specifying atoms used to compute rotation and translation of one molecule with respect to the other. The C programs and sample demonstration data are available on the GitHub repository (URL: http://github.com/federico-fogolari/pdb2entropy and http://github.com/federico-fogolari/pdb2trent)
Jason E Ybarra - One of the best experts on this subject based on the ideXlab platform.
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the progression of star formation in the rosette molecular cloud
The Astrophysical Journal, 2013Co-Authors: Jason E Ybarra, Elizabeth A Lada, C Romanzuniga, Z Balog, Junfeng Wang, Eric D FeigelsonAbstract:Using Spitzer Space Telescope and Chandra X-Ray Observatory data, we identify young stellar objects (YSOs) in the Rosette Molecular Cloud (RMC). By being able to select cluster members and classify them into YSO types, we are able to track the progression of star formation locally within the cluster environments and globally within the cloud. We employ the Nearest Neighbor Method analysis to explore the density structure of the clusters and YSO ratio mapping to study age progressions in the cloud. We find a relationship between the YSO ratios and extinction that suggests star formation occurs preferentially in the densest parts of the cloud and that the column density of gas rapidly decreases as the region evolves. This suggests rapid removal of gas may account for the low star formation efficiencies observed in molecular clouds. We find that the overall age spread across the RMC is small. Our analysis suggests that star formation started throughout the complex around the same time. Age gradients in the cloud appear to be localized and any effect the H II region has on the star formation history is secondary to that of the primordial collapse of the cloud.
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the progression of star formation in the rosette molecular cloud
arXiv: Solar and Stellar Astrophysics, 2013Co-Authors: Jason E Ybarra, Elizabeth A Lada, C Romanzuniga, Z Balog, Junfeng Wang, Eric D FeigelsonAbstract:Using Spitzer Space Telescope and Chandra X-ray Observatory data, we identify YSOs in the Rosette Molecular Cloud (RMC). By being able to select cluster members and classify them into YSO types, we are able to track the progression of star formation locally within the cluster environments and globally within the cloud. We employ Nearest Neighbor Method (NNM) analysis to explore the density structure of the clusters and YSO ratio mapping to study age progressions in the cloud. We find a relationship between the YSO ratios and extinction which suggests star formation occurs preferentially in the densest parts of the cloud and that the column density of gas rapidly decreases as the region evolves. This suggests rapid removal of gas may account for the low star formation efficiencies observed in molecular clouds. We find that the overall age spread across the RMC is small. Our analysis suggests that star formation started throughout the complex around the same time. Age gradients in the cloud appear to be localized and any effect the HII region has on the star formation history is secondary to that of the primordial collapse of the cloud.
