Search results for "Component analysis"
showing 10 items of 562 documents
Automated quality control protocol for MR spectra of brain tumors.
2008
Item does not contain fulltext eTUMOUR (http://www.etumour.net/) is acquiring a large database of brain tumor (1)H MR spectra to develop automated pattern recognition methods and decision support system (DSS) for tumor diagnosis. Development of accurate pattern-recognition algorithms requires spectra undistorted by artifacts, low signal-to-noise, or broad lines. eTUMOUR currently uses panels of expert spectroscopists to subjectively grade spectra as being acceptable or unacceptable. Automated quality control (QC) would be more satisfactory for several reasons: 1) to provide a reproducible objective classification of spectrum quality; 2) for use within the future DSS to prevent misdiagnosis …
Classification of Congeneric and QSAR of Homologous Antileukemic S–Alkylcysteine Ketones
2021
Based on a set of six vector properties, the partial correlation diagram is calculated for a set of 28 S-alkylcysteine diazomethyl- and chloromethyl-ketone derivatives. Those with the greatest antileukemic activity in the same class correspond to high partial correlations. A periodic classification is performed based on information entropy. The first four characteristics denote the group, and the last two indicate the period. Compounds in the same period and, especially, group present similar properties. The most active substances are situated at the bottom right. Nine classes are distinguished. The principal component analysis of the homologous compounds shows five subclasses included in t…
Combined use of PCA and QSAR/QSPR to predict the drugs mechanism of action. An application to the NCI ACAM Database
2009
During the years the National Cancer Institute (NCI) accumulated an enormous amount of information through the application of a complex protocol of drugs screening involving several tumor cell lines, grouped into panels according to the disease class. The Anti-cancer Agent Mechanism (ACAM) database is a set of 122 compounds with anti-cancer activity and a reasonably well known mechanism of action, for which are available drug screening data that measure their ability to inhibit growth of a panel of 60 human tumor lines, explicitly designed as a training set for neural network and multivariate analysis. The aim of this work is to adapt a methodology (previously developed for the analysis of …
Modeling the chiral resolution ability of highly sulfated β-cyclodextrin for basic compounds in electrokinetic chromatography
2013
Abstract Despite the fact that extensive research in the field of enantioseparations by capillary electrophoresis has been carried out, it is difficult to predict whether a concrete chiral selector would be useful for the separation of a racemic compound. Hence, several experimental effort is necessary to test the abilities of individual chiral selectors, usually by trial and error procedures. Thus, the enantioseparation of a new racemate becomes a time- and money-consuming task. In this work, the ability of highly sulfated β-cyclodextrin (HS-β-CD) as chiral selector in electrokinetic chromatography (EKC) is modeled for the first time, using exclusively directly-available structural data of…
Toward Pricing Financial Derivatives with an IBM Quantum Computer
2021
Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time evolution of interest rates. Several stochastic dynamics have been proposed in the literature to model either the instantaneous interest rate or the instantaneous forward rate. A successful approach to model the latter is the celebrated Heath-Jarrow-Morton framework, in which its dynamics is entirely specified by volatility factors. In its multifactor version, this model considers several noisy components to capture at best the dynamics of several time-maturing forward rates. However, as no general analytical solution is available, there is a trade-off between t…
Dynamic integration of classifiers in the space of principal components
2003
Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble to be successful, it should consist of accurate and diverse base classifiers. However, it is also important that the integration procedure in the ensemble should properly utilize the ensemble diversity. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique of dynamic integration, in which local accuracy estimates are calculated for each base classifier of an ensemble, in the neighborhood of a new instance to be pr…
Periodic Classification of Local Anaesthetics (Procaine Analogues)
2006
Algorithms for classification are proposed based on criteria (information entropyand its production). The feasibility of replacing a given anaesthetic by similar ones in thecomposition of a complex drug is studied. Some local anaesthetics currently in use areclassified using characteristic chemical properties of different portions of their molecules.Many classification algorithms are based on information entropy. When applying theseprocedures to sets of moderate size, an excessive number of results appear compatible withdata, and this number suffers a combinatorial explosion. However, after the equipartitionconjecture, one has a selection criterion between different variants resulting fromc…
Comparing Recurrent Neural Networks using Principal Component Analysis for Electrical Load Predictions
2021
Electrical demand forecasting is essential for power generation capacity planning and integrating environment-friendly energy sources. In addition, load predictions will help in developing demand-side management in coordination with renewable power generation. Meteorological conditions influence urban area load pattern; therefore, it is vital to include weather parameters for load predictions. Machine Learning algorithms can effectively be used for electrical load predictions considering impact of external parameters. This paper explores and compares the basic Recurrent Neural Networks (RNN); Simple Recurrent Neural Networks (Vanilla RNN), Gated Recurrent Units (GRU), and Long Short-Term Me…
Spanish Mediterranean diet and other dietary patterns and breast cancer risk: case–control EpiGEICAM study
2014
BACKGROUND: Although there are solid findings regarding the detrimental effect of alcohol consumption, the existing evidence on the effect of other dietary factors on breast cancer (BC) risk is inconclusive. This study aimed to evaluate the association between dietary patterns and risk of BC in Spanish women, stratifying by menopausal status and tumour subtype, and to compare the results with those of Alternate Healthy Index (AHEI) and Alternate Mediterranean Diet Score (aMED). METHODS: We recruited 1017 incident BC cases and 1017 matched healthy controls of similar age (±5 years) without a history of BC. The association between 'a priori' and 'a posteriori' developed dietary patterns and B…
Increased serum miR-193a-5p during non-alcoholic fatty liver disease progression: Diagnostic and mechanistic relevance
2022
Background & Aims Serum microRNA (miRNA) levels are known to change in non-alcoholic fatty liver disease (NAFLD) and may serve as useful biomarkers. This study aimed to profile miRNAs comprehensively at all NAFLD stages. Methods We profiled 2,083 serum miRNAs in a discovery cohort (183 cases with NAFLD representing the complete NAFLD spectrum and 10 population controls). miRNA libraries generated by HTG EdgeSeq were sequenced by Illumina NextSeq. Selected serum miRNAs were profiled in 372 additional cases with NAFLD and 15 population controls by quantitative reverse transcriptase PCR. Results Levels of 275 miRNAs differed between cases and population controls. Fewer differences were seen wi…