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PubMed1991

[Rough sets theory in structure-activity relationship analysis of quaternary pyridinium compounds].

Relationship between chemical structure and antimicrobial activity of 53 quaternary pyridinium compounds is analysed using the theory of rough sets. The compounds are described by 8 attributes concerning structure and are divided into 5 classes of activity. The description builds up an information system. Using the rough sets approach a smallest set of attributes significant for a high quality of classification has been found. A decision algorithm has been derived from the information system showing important relations between structure and activity. It may be helpful in supporting decisions concerning synthesis of new antimicrobial compounds.

Algorithms↗
PubMed1997

[Quality assurance of inpatient care--exemplified by surgery].

Internal quality assurance is natural for hospital physicians. External quality control is undertaken in cooperation with medical societies and sponsors-partly on voluntary basis. Despite considerable efforts in optimizing treatment procedures by classification of injuries and tumor stages, algorithms and tightly scheduled practical medical training, further control mechanisms are demanded; the economic drive is evident. In parallel to the introduction of quality control for services with a case-based flat rate, an interstructural result quality is desired. Hospital certification procedures are no longer utopian. The physician's duty for continuing medical education, which is set down in the professional code, has to be proven. The development of guidelines by scientific societies may help but is in need of a corrective. The medical chambers may play a particular role in this connection. Even if mechanisms of quality assurance differ, it is in the patient's interest that there must be no difference between ambulant and hospital care.

Cost Control↗
PubMed1997

Incremental learning of probabilistic rules from clinical databases based on rough set theory.

Several rule induction methods have been introduced in order to discover meaningful knowledge from databases, including medical domain. However, most of the approaches induce rules from all the data in databases and cannot induce incrementally when new samples are derived. In this paper, a new approach to knowledge acquisition, which induce probabilistic rules incrementally by using rough set technique, is introduced and was evaluated on two clinical databases. The results show that this method induces the same rules as those induced by ordinary non-incremental learning methods, which extract rules from all the datasets, but that the former method requires more computational resources than the latter approach.

Algorithms↗
PubMed2006

Combined optimization of spatial and temporal filters for improving brain-computer interfacing.

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.

Algorithms↗
PubMed1996

Classification of quantitative EEG data by an artificial neural network: a preliminary study.

Previous studies from different laboratories have suggested that qEEG could be useful for distinguishing dementia from normality. Our aims were: (1) to study the ability of qEEG to distinguish dementia among different pathological conditions in ambulatory settings; (2) to compare the ability of classical statistical analysis and of neural networks in classifying qEEG data. We were able to obtain a multiple discriminant function using a training set of patients, which classified correctly more than 91% of the qEEGs from an independent group of patients, with less than 5% of false positives. Kohonen's neural network was trained with the same set of patients. This unsupervised learning artificial neural network performed the classification of the independent sample with an accuracy comparable to that of the multiple discriminant function. Our results suggest that the use of unsupervised learning algorithms could be an interesting alternative in the classification of data obtained from psychiatric patients where definition of their clinical profile is not always a simple task.

Adult↗
PubMed2002

Maximum common subgraph isomorphism algorithms for the matching of chemical structures.

The maximum common subgraph (MCS) problem has become increasingly important in those aspects of chemoinformatics that involve the matching of 2D or 3D chemical structures. This paper provides a classification and a review of the many MCS algorithms, both exact and approximate, that have been described in the literature, and makes recommendations regarding their applicability to typical chemoinformatics tasks.

Algorithms↗
PubMed2000

Computer-based registration for digital subtraction in dental radiology.

OBJECTIVES: (1) To review computerized a posteriori techniques for geometry and contrast registration prior to digital subtraction in dental radiography; (2) to define a uniform notation for their methodological and technical classification and based on this key code; (3) to derive criteria for successful application of computer-based a posteriori registration for routine clinical subtraction. METHODS: All techniques are classified with respect to the (1) dimension of geometry registration; (2) origin; (3) abstraction level, and (4) linkage of features used for registration of geometry; (5) elasticity; (6) domain, and (7) parameter determination of the geometrical transform used; (8) interaction of geometrical registration; as well as (9) origin of features, (10) model of transform, and (11) interaction of procedure for contrast correction. RESULTS: With respect to clinical practicability, superior registration techniques are based on the low level abstraction of intrinsic features for both geometry and contrast registration. By approximately linking the features, a global projective transform should be generated for geometry registration by automatic methods, while automatic contrast correction should be non-parametric. This challenge is met only by one out of 36 published algorithms. Hence, although numerous computer-based techniques have been published, only a few of them are applied more than once in practice. CONCLUSION: The key code proposed in this paper is useful for technical classification of a posteriori registration methods in dental radiography and allows their objective comparison. Further investigations will focus on standardization of practicable procedures to evaluate the robustness of competing methods.

Algorithms↗
PubMed1991

[Semi-automatic TNM classification of malignant tumors with the ESTER system exemplified by the larynx].

Classification of tumours according to the TNM scheme has been accepted worldwide. However, vague baseline assessments and borderline cases render a comparison of the outcome on the basis of TNM classification impossible. Therefore we integrated the TNM rules as a new algorithm into an existing expert system for determining therapy. Thus, every tumour documented with the ESTHER system is automatically classified according to current TNM rules. The program is designed to cope with future changes of the TNM system: raw data are used for classification so that only the algorithms need to be modified.

Expert Systems↗
PubMed1998

Analysis of outcome after thermotherapy using different classifications of bladder outlet obstruction.

The urodynamic profiles of 97 patients with benign prostatic hyperplasia undergoing low-energy transurethral microwave thermotherapy (TUMT) for lower urinary tract symptoms were analysed using the Abrams/Griffiths nomogram, the urethral resistance algorithm, the linPURR, Schäfer nomogram, and the CHESS classification. A significant clinical response was seen for the whole group, as shown by changes in symptom score, free flow rate, and residual urine. The best symptomatic response was identified in patients in whom obstruction was present, whatever the classification used. Only the two-dimensional CHESS classification was found to predict a group of patients with a better response in both symptoms and objective variables. Obviously, a better response from TUMT can only be predicted by a classification system that identifies the independent variables of footpoint and slope of the PURR. The CHESS classification was the only one of those studied that satisfactorily identified these two parameters and could be used as a system of case selection for this minimally invasive treatment.

Aged↗
PubMed2006

Performance evaluation of fingerprint verification systems.

This paper is concerned with the performance evaluation of fingerprint verification systems. After an initial classification of biometric testing initiatives, we explore both the theoretical and practical issues related to performance evaluation by presenting the outcome of the recent Fingerprint Verification Competition (FVC2004). FVC2004 was organized by the authors of this work for the purpose of assessing the state-of-the-art in this challenging pattern recognition application and making available a new common benchmark for an unambiguous comparison of fingerprint-based biometric systems. FVC2004 is an independent, strongly supervised evaluation performed at the evaluators' site on evaluators' hardware. This allowed the test to be completely controlled and the computation times of different algorithms to be fairly compared. The experience and feedback received from previous, similar competitions (FVC2000 and FVC2002) allowed us to improve the organization and methodology of FVC2004 and to capture the attention of a significantly higher number of academic and commercial organizations (67 algorithms were submitted for FVC2004). A new, "Light" competition category was included to estimate the loss of matching performance caused by imposing computational constraints. This paper discusses data collection and testing protocols, and includes a detailed analysis of the results. We introduce a simple but effective method for comparing algorithms at the score level, allowing us to isolate difficult cases (images) and to study error correlations and algorithm "fusion." The huge amount of information obtained, including a structured classification of the submitted algorithms on the basis of their features, makes it possible to better understand how current fingerprint recognition systems work and to delineate useful research directions for the future.

Algorithms↗
PubMed2003

Simultaneous gene clustering and subset selection for sample classification via MDL.

MOTIVATION: The microarray technology allows for the simultaneous monitoring of thousands of genes for each sample. The high-dimensional gene expression data can be used to study similarities of gene expression profiles across different samples to form a gene clustering. The clusters may be indicative of genetic pathways. Parallel to gene clustering is the important application of sample classification based on all or selected gene expressions. The gene clustering and sample classification are often undertaken separately, or in a directional manner (one as an aid for the other). However, such separation of these two tasks may occlude informative structure in the data. Here we present an algorithm for the simultaneous clustering of genes and subset selection of gene clusters for sample classification. We develop a new model selection criterion based on Rissanen's MDL (minimum description length) principle. For the first time, an MDL code length is given for both explanatory variables (genes) and response variables (sample class labels). The final output of the proposed algorithm is a sparse and interpretable classification rule based on cluster centroids or the closest genes to the centroids. RESULTS: Our algorithm for simultaneous gene clustering and subset selection for classification is applied to three publicly available data sets. For all three data sets, we obtain sparse and interpretable classification models based on centroids of clusters. At the same time, these models give competitive test error rates as the best reported methods. Compared with classification models based on single gene selections, our rules are stable in the sense that the number of clusters has a small variability and the centroids of the clusters are well correlated (or consistent) across different cross validation samples. We also discuss models where the centroids of clusters are replaced with the genes closest to the centroids. These models show comparable test error rates to models based on single gene selection, but are more sparse as well as more stable. Moreover, we comment on how the inclusion of a classification criterion affects the gene clustering, bringing out class informative structure in the data. AVAILABILITY: The methods presented in this paper have been implemented in the R language. The source code is available from the first author.

Algorithms↗
PubMed2002

Automatic recognition of alertness and drowsiness from EEG by an artificial neural network.

We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum EEG as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. The experimental data were collected from 17 subjects. Two experts in EEG interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization (LVQ) neural network. We showed that the LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of LVQ were validated using the data recorded in 12 healthy volunteer subjects, yet whose EEG recordings have not been used for the training of the ANN. The statistics were used as a measure of potential applicability of the LVQ: the t-distribution showed that matching between the human assessment and the network output was 94.37+/-1.95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training.

Adult↗
PubMed1985

Pattern recognition analysis of a set of mutagenic aliphatic N-nitrosamines.

A set of 21 mutagenic aliphatic N-nitrosamines were subjected to a pattern recognition analysis using ADAPT software. Four descriptors based on molecular connectivity, geometry and sigma charge on nitrogen were capable of achieving a 100% classification using the linear learning machine or iterative least squares algorithms. Three descriptors were capable of a 90.5% and two descriptors of a 85.7% overall correct classification. Three of the four descriptors were each capable of classifying 15 of the 16 active chemicals while it required three of the four descriptors to classify correctly two of the five inactive chemicals. These results are in concert with previous observations that molecular connectivity, geometry, and sigma charge on nitrogen are powerful descriptors for separating active from inactive mutagenic and carcinogenic N-nitrosamines.

Mutagens↗
PubMed2003

Genetic algorithms applied to multi-class prediction for the analysis of gene expression data.

MOTIVATION: An important challenge in the use of large-scale gene expression data for biological classification occurs when the expression dataset being analyzed involves multiple classes. Key issues that need to be addressed under such circumstances are the efficient selection of good predictive gene groups from datasets that are inherently 'noisy', and the development of new methodologies that can enhance the successful classification of these complex datasets. METHODS: We have applied genetic algorithms (GAs) to the problem of multi-class prediction. A GA-based gene selection scheme is described that automatically determines the members of a predictive gene group, as well as the optimal group size, that maximizes classification success using a maximum likelihood (MLHD) classification method. RESULTS: The GA/MLHD-based approach achieves higher classification accuracies than other published predictive methods on the same multi-class test dataset. It also permits substantial feature reduction in classifier genesets without compromising predictive accuracy. We propose that GA-based algorithms may represent a powerful new tool in the analysis and exploration of complex multi-class gene expression data. AVAILABILITY: Supplementary information, data sets and source codes are available at http://www.omniarray.com/bioinformatics/GA.

Algorithms↗
PubMed2005

An algorithm to construct genetically similar subsets of families with the use of self-reported ethnicity information.

We present a simple algorithm that uses self-reported ethnicity information, pedigree structure, and affection status to group families into genetically more homogeneous subsets. This algorithm should prove useful to researchers who wish to perform genetic analyses on more-homogeneous subsets when they suspect that ignoring heterogeneity could lead to false-positive results or loss of power. We applied our algorithm to the self-reported ethnicity information of 159 families from the Veterans Affairs Cooperative Study of schizophrenia. We compared these estimates of population membership with those obtained using the program structure in an analysis of 378 microsatellite markers. We found excellent concordance between family classifications determined using self-reported ethnicity information and our algorithm and those determined using genetic marker data and structure; 158 of the 159 families had concordant classifications. In addition, the degree of admixture estimated using our algorithm and self-reported ethnicity information correlated well with that predicted using the genotype information.

Algorithms↗
PubMed2004

Latent variable discovery in classification models.

The naive Bayes model makes the often unrealistic assumption that the feature variables are mutually independent given the class variable. We interpret a violation of this assumption as an indication of the presence of latent variables, and we show how latent variables can be detected. Latent variable discovery is interesting, especially for medical applications, because it can lead to a better understanding of application domains. It can also improve classification accuracy and boost user confidence in classification models.

Algorithms↗
PubMed2005

Efficient training algorithms for a class of shunting inhibitory convolutional neural networks.

This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). Experimental results show that the new hybrid method can perform as well as the Levenberg-Marquardt (LM) algorithm, but at a much lower computational cost and less memory storage. For comparison sake, the visual pattern recognition task of face/nonface discrimination is chosen as a classification problem to evaluate the performance of the training algorithms. Sixteen training algorithms are implemented for the three different variants of the proposed CoNN architecture: binary-, Toeplitz- and fully connected architectures. All implemented algorithms can train the three network architectures successfully, but their convergence speed vary markedly. In particular, the combination of LS with the new hybrid method and LS with the LM method achieve the best convergence rates in terms of number of training epochs. In addition, the classification accuracies of all three architectures are assessed using ten-fold cross validation. The results show that the binary- and Toeplitz-connected architectures outperform slightly the fully connected architecture: the lowest error rates across all training algorithms are 1.95% for Toeplitz-connected, 2.10% for the binary-connected, and 2.20% for the fully connected network. In general, the modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods, the three variants of LM algorithm, and the new hybrid/LS method perform consistently well, achieving error rates of less than 3% averaged across all three architectures.

Algorithms↗