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PubMed1989

[Discriminant analysis of clinical markers before renal biopsy in patients with IgA nephropathy].

Discriminant analysis of clinical markers before renal biopsy in patients with IgA nephropathy is described. Sixty eight patients with IgA nephropathy (IgA nephropathy group) and 66 patients with other chronic glomerulonephritis (non-IgA nephropathy group) were examined. The discriminant analysis was applied to separate those two groups by using twenty clinical parameters as well as binding capacity of serum IgA to the glomeruli of renal specimens. Binding of serum IgA of patients to the glomeruli obtained from patients with IgA nephropathy was performed using avidin-biotin immunofluorescence. Among twenty clinical markers, the levels of serum IgA and creatinine, and degree of microhematuria in IgA nephropathy group were significantly higher than those in non-IgA nephropathy group Furthermore, the positive incidence of serum IgA binding of IgA nephropathy group was significantly higher than that of serum IgA binding of non-IgA nephropathy group. The correct classification rate were 79.10% using five clinical markers including serum IgA, microhematuria, serum C4, quantitation of proteinuria and degree of proteinuria. It is indicated that the levels of serum IgA and the binding of serum IgA to the glomeruli were considered to be major markers for clinical diagnosis of patients with IgA nephropathy It was concluded that the discriminant analysis before renal biopsy was useful for diagnosis of IgA nephropathy.

Adolescent↗
PubMed1987

[Discriminant analysis of hematological and serum biochemical values in cynomolgus monkey (Macaca fascicularis) bred and reared under the indoor individually-caged conditions].

The data on hematological and serum biochemical properties of laboratory-bred cynomolgus monkeys (Macaca fascicularis) at different ages were analyzed by discriminant analysis. All the animals had been bred and reared under uniform environmental conditions at Tsukuba Primate Center for Medical Science, N.I.H., Japan. The items used were as follows: red blood cell count (RBC), hematocrit value (Ht), hemoglobin concentration (Hb), mean corpuscular volume (MCV), white blood cell count (WBC), glutamic oxaloacetic transaminase activity (GOT), glutamic pyruvic transaminase activity (GPT), total protein concentration (TP), albumin concentration (ALB), albumin-globulin ratio (A/G), blood urea nitrogen (BUN), glucose concentration (GLU), total cholesterol concentration (TCHO), free cholesterol concentration (FCHO), triglyceride concentration (TG) and alkaline phosphatase activity (ALP). In total, 1086 animals in 10 age groups were examined. Data analyses were done with respect to the difference of sex. Discrimination was possible by Mahalanobis' generalized distance between centroids of groups. In canonical discriminant analysis (discriminant analysis with reduction of dimensionality), age was highly correlated to the value of the first canonical variate. From the approximate relative value of the eigenvector of the first canonical variate, the most discriminant variables are WBC, TP, ALB, A/G, TCHO, FCHO, TG, and ALP. It can be concluded that periodic measurement of these 8 parameters is necessary and sufficient to monitor the physiological conditions of growing monkeys.

Age Factors↗
PubMed1986

Prediction of protein structural class by discriminant analysis.

Protein structural class--alpha, beta, mixed (alpha/beta or alpha + beta), irregular--can be predicted from the amino acid sequence by discriminant analysis. Discrimination is based on distributions, in the classes, of vectors of attributes characterizing the sequences. In this paper, two sets of attributes and two methods of estimating their distributions are compared using more than 100 proteins from the Protein Data Bank. The best results were obtained when canonical variates of the frequencies of occurrence of 20 amino acids and non-parametric estimates of their distributions were used. Three variates are sufficient to allocate proteins to one of four classes with 83% reliability (estimated by cross-validation) and four variates allowed allocation to one of five classes with 78% reliability.

Amino Acid Sequence↗
PubMed2003

Use of correspondence discriminant analysis to predict the subcellular location of bacterial proteins.

Correspondence discriminant analysis (CDA) is a multivariate statistical method derived from discriminant analysis which can be used on contingency tables. We have used CDA to separate Gram negative bacteria proteins according to their subcellular location. The high resolution of the discrimination obtained makes this method a good tool to predict subcellular location when this information is not known. The main advantage of this technique is its simplicity. Indeed, by computing two linear formulae on amino acid composition, it is possible to classify a protein into one of the three classes of subcellular location we have defined. The CDA itself can be computed with the ADE-4 software package that can be downloaded, as well as the data set used in this study, from the Pôle Bio-Informatique Lyonnais (PBIL) server at http://pbil.univ-lyon1.fr.

Bacterial Proteins↗
PubMed2003

Predictive validity of discriminant analysis for genetic data.

We examined the predictive validity of the results using discriminant analysis to distinguish statistically among two or more populations with a large sample of random amplified polymorphic DNA (RAPD) loci, but a small sample of genotypes from each population. We compared and contrasted results from randomized data with results from real data of three studies by 100 randomized shuffling of genotypes into various populations. We generally obtained substantial differences between results from randomized data compared to those from the real data in several characteristics of discriminant analysis. We showed that a high level of correctly classified percentage is also obtainable in the analysis of randomized data, mainly with a low number of populations. However, the correctly classified percentage obtained from the real data was generally significantly higher than the percentage obtained from the randomized data. We suggested that the high level of real differences in allele frequencies of the RAPD polymorphic loci clearly distinguished the various populations and that the populations differ significantly in their RAPD contents in accordance with ecological heterogeneity. We obtained either no or a low level of difference between the correct classification rate obtained by the leaving-one-out procedure and that obtained from the original data, attributed to a low number of loci selected by the stepwise method. The results strengthen and support our conclusion and lead us to focus on the discriminant analysis by selecting only low numbers of discriminating variables.

Discriminant Analysis↗
PubMed1999

Photic driving response in primary headache: diagnostic value tested by discriminant analysis and artificial neural network classifiers.

The aim of this study was to discriminate migraine patients (MWoA) from tension-type headache (TTH) patients and normals in order to confirm that the photic driving response in the medium frequency range is a marker of migraine and to test the hypothesis that MWoA and TTH are separate disorders based on electrophysiological pattern. We recruited 120 MWoA patients, 64 TTH patients, and 51 healthy controls without any history of headache or of migraine inheritance, according to International Headache Society (IHS) criteria. The classification method was discriminant analysis using both linear discriminant analysis with a stepwise selection of predictors and an artificial neural network classifier (NNs). The mean amplitude of the first harmonic elicited by flash stimulation in the 15-27 Hz range was significantly increased over Fp1, C3, C4, P4, O2, and O1 electrodes in MWoA and TTH patients in comparison with normal subjects. Using both classification methods, only the control subjects were correctly distinguished. When only the patient groups were matched, no significant difference was detectable. The increased brain response to visual stimulation detected in both migraine and TTH suggests a common neuronal dysfunction in the two headache subtypes.

Adolescent↗
PubMed2006

Shaving diffusion tensor images in discriminant analysis: a study into schizophrenia.

A technique called 'shaving' is introduced to automatically extract the combination of relevant image regions in a comparative study. No hypothesis is needed, as in conventional pre-defined or expert selected region of interest (ROI)-analysis. In contrast to traditional voxel based analysis (VBA), correlations within the data can be modeled using principal component analysis (PCA) and linear discriminant analysis (LDA). A study into schizophrenia using diffusion tensor imaging (DTI) serves as an application. Conventional VBA found a decreased fractional anisotropy (FA) in a part of the genu of the corpus callosum and an increased FA in larger parts of white matter. The proposed method reproduced the decrease in FA in the corpus callosum and found an increase in the posterior limb of the internal capsule and uncinate fasciculus. A correlation between the decrease in the corpus callosum and the increase in the uncinate fasciculus was demonstrated.

Adolescent↗
PubMed2006

Principal component and linear discriminant analysis of T1 histograms of white and grey matter in multiple sclerosis.

Twenty-three relapsing remitting multiple sclerosis (RRMS) patients and 14 controls were imaged to produce normal-appearing white and grey matter T1 histograms. These were used to assess whether histogram measures from principal component analysis (PCA) and linear discriminant analysis (LDA) out-perform traditional histogram metrics in classification of T1 histograms into control and RRMS subject groups and in correlation with the expanded disability status score (EDSS). The histograms were classified into one of two groups using a leave-one-out analysis. In addition, the patients were scanned serially, and the calculated parameters correlated with the EDSS. The classification results showed that the more complex techniques were at least as good at classifying the subjects as histogram mean, peak height and peak location, with PCA/LDA having success rates of 76% for white matter and 68%/65% for grey matter. No significant correlations were found with EDSS for any histogram parameter. These results indicate that there is much information contained within the grey matter as well as the white matter histograms. Although in these histograms PCA and LDA did not add greatly to the discriminatory power of traditional histogram parameters, they provide marginally better performance, while relying only on data-driven feature selection.

Brain↗
PubMed1994

Sequential Optimum Selection: a categorical alternative to discriminant analysis for conducting research in assessment.

Sequential Optimum Selection, SOS, a nonparametric computer-assisted alternative to discriminant analysis, was introduced and explicated. SOS is an iterative procedure that develops ongoing decision rules for discriminating among groups, successively expunging frequency distributions or classes until all cases are accounted for. In three illustrative comparisons using clinical assessment data, SOS proved competitive with discriminant analysis in terms of over-all hit rates for parsimonious extraction of meaningful variables. Further, in one demonstration using parametric data, SOS held up better on cross-validation. While discriminant analysis will, in most cases, yield better discrimination than SOS, the latter does not make parametric assumptions and should therefore be considered a viable alternative as an exploratory procedure.

Diagnosis, Computer-Assisted↗
PubMed1993

Discriminant analysis for classification of murine melanomas and human cervical epithelial cells.

Computer analysis of cell images offers many advantages over routine visual examination. It leads to quantitative and accurate detection of subvisual information and provides reproducible measures so that objective decisions in cancer diagnosis become possible. Such diagnostic decisions usually follow partly from a classification process. In this paper two multivariate discriminant analysis methods--namely, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)--are presented. LDA and QDA were used to classify cytologic data based on some morphodensitometric measurements. The cytologic data constituted two samples, one representing B16 cell lines and the other including three types of normal human cervical epithelial cells. LDA and QDA were assessed both individually and in comparison to each other, mainly on the basis of the rate of correct classification and robustness. The measurements extracted from the cytologic data employed were shown to be stable and consistent. The statistical results obtained from experiments on cervical cells look particularly promising and encouraging for future work. It has also been shown in this study that the classification techniques employed are valid and that LDA performed almost as well as QDA.

Animals↗
PubMed1995

Application of multivariable optimal discriminant analysis in general internal medicine.

OBJECTIVE: To illustrate the use of multivariable optimal discriminant analysis (MultiODA). DESIGN: Data from four previously published studies were reanalyzed using MultiODA. The original analysis was Fisher's linear discriminant analysis (FLDA) for two studies and logistic regression analysis (LRA) for two studies. MEASUREMENTS AND MAIN RESULTS: In Study 1, FLDA achieved an overall percentage accuracy in classification (PAC) for the training sample of 69.9%, compared with 73.5% for MultiODA. In Study 2, the LRA model required three attributes to achieve a 76.1% overall PAC for the training sample and a 79.4% overall PAC for the hold-out sample. Using only two attributes, the MultiODA model achieved similar values. In Study 3, the FLDA model achieved an overall PAC of 82.5%, compared with 87.5% for the MultiODA model. In Study 4, MultiODA identified a two-attribute model that achieved a 93.3% overall training PAC, when an LRA model could not be developed. CONCLUSIONS: MultiODA identified: a superior training model (Study 1); a more parsimonious model that achieved superior overall training and identical hold-out PAC (Study 2); a model that achieved a higher hold-out PAC (Study 3); and a two-attribute model that achieved a relatively high PAC when a multivariable LRA model could not be obtained (Study 4). These findings suggest that MultiODA has the potential to improve the accuracy of predictions made in general internal medicine research.

Acquired Immunodeficiency Syndrome↗
PubMed1994

The prediction of human exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames.

Discriminant analysis is applied to the problem of recognition 5'-, internal and 3'-exons in human DNA sequences. Specific recognition functions were developed for revealing exons of particular types. The method based on a splice site prediction algorithm that uses the linear Fisher discriminant to combine the information about significant triplet frequencies of various functional parts of splice site regions and preferences of oligonucleotides in protein coding and intron regions (Solovyev, Lawrence, 1994). The accuracy of our splice site recognition function is about 97%. A discriminant function for 5'-exon prediction includes hexanucleotide composition of upstream region, triplet composition around the ATG codon, ORF coding potential, donor splice site potential and composition of downstream intron region. For internal exon prediction, we combine in a discriminant function the characteristics describing the 5'-intron region, donor splice site, coding region, acceptor splice site and 3'-intron region for each open reading frame flanked by GT and AG base pairs. The accuracy of precise internal exon recognition on a test set of 451 exon and 246693 pseudoexon sequences is 77% with a specificity of 79% and a level of pseudoexon ORF prediction of 99.96%. The recognition quality computed at the level of individual nucleotides is 89% for exon sequences and 98% for intron sequences. A discriminant function for 3'-exon prediction includes octanucleotide composition of upstream intron region, triplet composition around the stop codon, ORF coding potential, acceptor splice site potential and hexanucleotide composition of downstream region.(ABSTRACT TRUNCATED AT 250 WORDS)

Computer Simulation↗
PubMed2000

Discriminant analysis and its application in DNA sequence motif recognition.

Identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. Discriminant analysis is widely used for solving such problems. This paper will review two basic parametric methods: LDA (linear discriminant analysis) and QDA (quadratic discriminant analysis). Their usage in recognition of splice sites and exons in the human genome will be demonstrated.

Algorithms↗
PubMed2003

Screening of malignant pleural effusion by discriminant analysis.

OBJECTIVE: To assess the value of discriminant analysis as a method of optimizing the discriminant power of routine parameters in differentiating between malignant and non-malignant pleural effusions. METHODS: Retrospective review of the medical records of 245 patients with exudative pleural effusion. RESULTS: The most powerful predictor of the malignant etiology of pleural effusion was a function that consisted of seven variables: age (years); effusion volume (coded as up to one third = 1, up to two thirds = 2, massive = 3); sedimentation rate (mm/h); monocyte count in the peripheral blood (cells/mm3); bloodstained exudate (coded as yes = 1, no = 2); and glucose (mg/dL) and iron (microg/dL) concentration in pleural fluid. This function showed a sensitivity of 77%, specificity of 85%, positive predictive value (PPV) of 76%, negative predictive value (NPV) of 86%, and was able to give an 82% rate of correct classification. In patients aged 50 years or younger, the NPV ranged between 91 and 98%, whereas in those older than 60 years, the PPV was 89%. CONCLUSION: The calculated discriminant function is a simple, rapid, and inexpensive method for screening patients with pleural effusion for malignant etiology.

Adult↗
PubMed1978

Discriminant analysis and structure-activity relationships. 1. Naphthoquinones.

Discriminant analysis has been used to study the data for naphthoquinones as antitumor agents in three different animal tumor systems. In each case the most significant variables for classifying the compounds into two groups according to their antitumor activities were determined by a stepwise procedure. The usefulness of discriminant analysis in the design of drugs is discussed.

Animals↗
PubMed2004

Selecting compounds for focused screening using linear discriminant analysis and artificial neural networks.

Linear discriminant analysis and a committee of neural networks have been applied to recognise compounds that act at biological targets belonging to a specific gene family, protein kinases. The MDDR database was used to provide compounds targeted against this family and sets of randomly selected molecules. BCUT parameters were employed as input descriptors that encode structural properties and information relevant to ligand-receptor interactions. The technique was applied to purchasing compounds from external suppliers. These compounds achieved hit rates on a par with those achieved using known actives for related targets when tested for the ability to inhibit kinases at a single concentration. This approach is intended as one of a series of filters in the selection of screening candidates, compound purchases and the application of synthetic priorities to combinatorial libraries.

Databases, Factual↗
PubMed2006

Face recognition using kernel scatter-difference-based discriminant analysis.

There are two fundamental problems with the Fisher linear discriminant analysis for face recognition. One is the singularity problem of the within-class scatter matrix due to small training sample size. The other is that it cannot efficiently describe complex nonlinear variations of face images because of its linear property. In this letter, a kernel scatter-difference-based discriminant analysis is proposed to overcome these two problems. We first use the nonlinear kernel trick to map the input data into an implicit feature space F. Then a scatter-difference-based discriminant rule is defined to analyze the data in F. The proposed method can not only produce nonlinear discriminant features but also avoid the singularity problem of the within-class scatter matrix. Extensive experiments show encouraging recognition performance of the new algorithm.

Discriminant Analysis↗
PubMed2006

Study of discriminant analysis applied to motor imagery bipolar data.

We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA.

Brain↗