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Query: UMLS:C0220723 (PCA)
 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

3D QSAR studies were performed on a library of 120 GAPDH inhibitors, including a series of coumarins, flavonoids, and nucleosides. The VolSurf method was successfully used to calculate surface descriptors for protein-ligand affinity and binding site of the enzyme. PCA/PLS analyses have permitted the evaluation of the structural features crucial for potency, selectivity, and favorable pharmacokinetic properties, and are important for the design of new ligands.
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PMID:Structure-activity relationships of novel inhibitors of glyceraldehyde-3-phosphate dehydrogenase. 1508 Oct 8

Multivariate statistical methods including pattern recognition (Principal Component Analysis--PCA) and modeling (Multiple Linear Regression--MLR, Partial Least Squares--PLS, as well as Principal Component Regression--PCR) methods were carried out to evaluate the state of ambient air in Miskolc (second largest city in Hungary). Samples were taken from near the ground at a place with an extremely heavy traffic. Although PCA is not able to determine the significance of variables, it can uncover their similarities and classify the cases. PCA revealed that it is worth to separate day and night data because different factors influence the ozone concentrations during all day. Ozone concentration was modeled by MLR and PCR with the same efficiency if the conditions of meteorological parameters were not changed (i.e. morning and afternoon). Without night data, PCR and PLS suggest that the main process is not a photochemical but a chemical one.
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PMID:Prediction of ozone concentration in ambient air using multivariate methods. 1548 79

Since sewage discharges can significantly contribute to the contaminant loadings in coastal areas, it is important to identify sources, pathways and environmental sinks. Sterol and fatty alcohol biomarkers were quantified in source materials, suspended sediments and settling matter from the Ria Formosa Lagoon. Simple ratios between key biomarkers including 5beta-coprostanol, cholesterol and epi-coprostanol were able to identify the sewage sources and effected deposition sites. Multivariate methods (PCA) were used to identify co-varying sites. PLS analysis using the sewage discharge as the signature indicated approximately 25% of the variance in the sites could be predicted by the sewage signature. A new source of sewage derived organic matter was found with a high sewage predictable signature. The suspended sediments had relatively low sewage signatures as the material was diluted with other organic matter from in situ production. From a management viewpoint, PLS provides a useful tool in identifying the pathways and accumulation sites for such contaminants.
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PMID:Identifying the source, transport path and sinks of sewage derived organic matter. 1584 May 29

Classical Volsurf approach was applied to a set of 70 carbapenem compounds acting as antibiotics. Antibacterial activity of Staphylococcus aureus SG 511 and Escherichia coli 078 representing Gram positive and Gram negative bacteria, respectively, was used for the analysis. The score plots obtained from principal component analysis showed clustering of compounds according to the activity and their loading plots explained the Volsurf descriptors responsible for the separation or peculiar behaviour of these compounds. Partial Least Square analysis yielded a seven component model for S. aureus with a cross-validated r2 (q2) value of 0.684 and conventional r2 value of 0.883 and for E. coli it is a six component model with cross-validated r2 (q2) value of 0.514 and conventional r2 value of 0.756. Both the PCA and PLS models were validated by an external test set of 15 compounds. All the compounds of the test set were fairly predicted with residual values less than one log unit. Comparatively activity data of S. aureus (Gram positive) was better explained than E. coli (Gram negative) by these models.
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PMID:Volsurf analysis of carbapenem antibiotics. 1584 46

The concept, characteristics and history of metabonomics are introduced. The techniques used in data acquisition and data analysis in metabonomics including their advantages and disadvantages are summarized. In data acquisition platform, NMR, GC/MS, LC/MS (/MS) are the prevalent techniques although at present, none of them is a perfect technique that could meet with the requirement of the metabonomics for measuring all metabolites. While in data analysis, the PCA, PLS and ANN are the major techniques. The researchers could select them according to the research destination. Recent advances and applications of metabonomics in disease diagnosis, drug toxicity evaluation, plant metabolomics and microbial metabolomics are reviewed. In addition, by giving the situation on the establishment of the related corporations, the conferences about metabonomics and proclamation of NIH roadmap the current boom of the metabonomics is reflected. It can be expected that with the development of the function genomics, metabonomics will play a major role in the discovery of the phynotype of the genome and searching for the disease diagnostic biomarkers, and it will also bring much benefit to the drug discovery, clinical diagnosis and nutrition science.
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PMID:[Metabonomics and its applications]. 1585 20

The amount of ethanol in beverages has been quantified by density and infrared spectroscopy methods. The density method allows only the amount of ethanol to be quantified, while the infrared spectroscopy method allows the quantification of ethanol and, if any, the presence of contaminants such as methanol. The amount of ethanol quantified agrees to bottle declaration particularly for the infrared spectroscopy method. The infrared spectroscopy method coupled with a mathematical treatment (PCA and PLS-2) thus distinguishes itself as a fast and reliable technique for determining the amount of ethanol in beverages.
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PMID:Quantification of alcohol in beverages by density and infrared spectroscopy methods. 1600 32

With the rising use of principal component analysis/partial least squares (PCA/PLS) in the process analytical technology (PAT) initiative of the pharmaceutical industry, it seems appropriate to view that approach from a statistical process control (SPC) perspective. The purpose of this study was to demonstrate the effect of process instability (ie, state of statistical out-of-control) on use of PCA/PLS. The demonstrated differences in results should encourage PCA/PLS users to incorporate SPC as an active part of their process analytical control (PAC) toolkit to check for stability prior to drawing conclusions based on PCA/PLS analysis.
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PMID:Effect of multivariate process instability on principal component analysis: a case study. 1614 32

We describe the optimal high-level postprocessing of single-voxel (1)H magnetic resonance spectra and assess the benefits and limitations of automated methods as diagnostic aids in the detection of recurrent brain tumor. In a previous clinical study, 90 long-echo-time single-voxel spectra were obtained from 52 patients and classified during follow-up (30/28/32 normal/non-progressive tumor/tumor). Based on these data, a large number of evaluation strategies, including both standard resonance line quantification and algorithms from pattern recognition and machine learning, were compared in a quantitative evaluation. Results from linear and non-linear feature extraction, including ICA, PCA and wavelet transformations, and also the data from resonance line quantification were combined systematically with different classifiers such as LDA, chemometric methods (PLS, PCR), support vector machines and ensemble methods. Classification accuracy was assessed using a leave-one-out cross-validation scheme and the area under the curve (AUC) of the receiver operator characteristic (ROC). A regularized linear regression on spectra with binned channels reached 91% classification accuracy compared with 83% from quantification. Interpreting the loadings of these regressions, we find that lipid and lactate signals are too unreliable to be used in a simple machine rule. Choline and NAA are the main source of relevant information. Overall, we find that fully automated pattern recognition algorithms perform as well as, or slightly better than, a manually controlled and optimized resonance line quantification.
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PMID:Optimal classification of long echo time in vivo magnetic resonance spectra in the detection of recurrent brain tumors. 1664 60

The assignment of significantly changed NMR signals, which were identified with the help of multivariate models, to individual metabolites in biofluids is a manual and tedious task requiring knowledge in chemometrics and NMR spectroscopy. Metabolite projection analysis, introduced in this work, allows automatic linking of multivariate models with metabolites by skipping the level of manual NMR signal identification. The method depends on the projection of sets of metabolite NMR spectra from a database into PCA or PLS models of NMR spectra of biofluid samples. Metabolites that are significantly changed can be identified graphically in metabolite projection plots or numerically as projected virtual concentration. The method is demonstrated together with a newly introduced algorithm for refined nonequidistant binning using a metabonomics study with amiodarone as administered drug. Amiodarone can induce phospholipidosis in the lung and liver, which is accompanied by associated organ toxicity in these organs. It is shown how metabolite projection analysis allows easy and fast tentative assignment of all structures of metabolites whose concentrations in the urine samples significantly changed upon dosage. These metabolites had also been identified previously by manually interpreting the multivariate models and spectra. Among these metabolites, phenylacetylglycine was also identified as being significantly increased. This metabolite has recently been proposed as urinary biomarker for phospholipidosis.
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PMID:Metabolite projection analysis for fast identification of metabolites in metabonomics. Application in an amiodarone study. 1673 7

Three extensions of the basic PCA and PLS methodologies are described. These extensions are hierarchical, non-linear and batch-based in nature. The objectives of these methods are to assist in problem understanding and problem solving in very complex (QSAR) problem formulations. The method extensions are illustrated using two example QSAR data sets containing many X- and Y-variables.
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PMID:Megavariate analysis of environmental QSAR data. Part II--investigating very complex problem formulations using hierarchical, non-linear and batch-wise extensions of PCA and PLS. 1680 62


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