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PubMed2006

Unsupervised classification of ventricular extrasystoles using bounded clustering algorithms and morphology matching.

Ventricular extrasystoles (VE) are ectopic heartbeats involving irregularities in the heart rhythm. VEs arise in response to impulses generated in some part of the heart different from the sinoatrial node. These are caused by the premature discharge of a ventricular ectopic focus. VEs after myocardial infarction are associated with increased mortality. Screening of VEs is typically a manual and time consuming task that involves analysis of the heartbeat morphology, QRS duration, and variations of the RR intervals using long-term electrocardiograms. We describe a novel algorithm to perform automatic classification of VEs and report the results of our validation study. The proposed algorithm makes use of bounded clustering algorithms, morphology matching, and RR interval length to perform automatic VE classification without prior knowledge of the number of classes and heartbeat features. Additionally, the proposed algorithm does not need a training set.

Algorithms↗
PubMed2005

Classification of a large microarray data set: algorithm comparison and analysis of drug signatures.

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as "rewards" for the class-of-interest) while others have a negative contribution (act as "penalties") to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class.

Algorithms↗
PubMed2006

Classification of adaptive memetic algorithms: a comparative study.

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

Algorithms↗
PubMed2000

New support vector algorithms

We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter nu lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of nu, and report experimental results.

Journal Article↗
PubMed2003

A classification-based machine learning approach for the analysis of genome-wide expression data.

Three important areas of data analysis for global gene expression analysis are class discovery, class prediction, and finding dysregulated genes (biomarkers). The clinical application of microarray data will require marker genes whose expression patterns are sufficiently well understood to allow accurate predictions on disease subclass membership. Commonly used methods of analysis include hierarchical clustering algorithms, t-, F-, and Z-tests, and machine learning approaches. We describe an approach called the maximum difference subset (MDSS) algorithm that combines classification algorithms, classical statistics, and elements of machine learning and provides a coherent framework. By integrating prediction accuracy, the MDSS algorithm learns the critical threshold of statistical significance (the alpha or P-value), eliminating the arbitrariness of setting a threshold of statistical significance and minimizing the effect of the normality assumptions. To reduce the false positive rate and to increase external validity of the predictive gene set, a jackknife step is used. This step identifies and removes genes in the initial MDSS with low combined predictive utility. The overall MDSS provides a prediction that is less dependent on an arbitrary study design (sample inclusion or exclusion) and should thus have high external validity. We demonstrate that this approach, unlike other published methods, identifies biomarkers capable of predicting the outcome of anthracycline-cytarabine chemotherapy in cases of acute myeloid leukemia. By incorporating two criteria-statistical significance and predictive utility-the approach learns the significance level relevant for a given data set. The MDSS approach can be used with any test and classifier operator pair.

Acute Disease↗
PubMed2006

Identification of serum proteins discriminating colorectal cancer patients and healthy controls using surface-enhanced laser desorption ionisation-time of flight mass spectrometry.

AIM: To detect the new serum biomarkers for colorectal cancer (CRC) by serum protein profiling with surface-enhanced laser desorption ionisation--time of flight mass spectrometry (SELDI-TOF MS). METHODS: Two independent serum sample sets were analysed separately with the ProteinChip technology (set A: 40 CRC+49 healthy controls; set B: 37 CRC+31 healthy controls), using chips with a weak cation exchange moiety and buffer pH 5. Discriminative power of differentially expressed proteins was assessed with a classification tree algorithm. Sensitivities and specificities of the generated classification trees were obtained by blindly applying data from set A to the generated trees from set B and vice versa. CRC serum protein profiles were also compared with those from breast, ovarian, prostate, and non-small cell lung cancer. RESULTS: Mass-to-charge ratios (m/z) 3.1x10(3), 3.3x10(3), 4.5x10(3), 6.6x10(3) and 28x10(3) were used as classifiers in the best-performing classification trees. Tree sensitivities and specificities were between 65% and 90%. Most of these discriminative m/z values were also different in the other tumour types investigated. M/z 3.3x10(3), main classifier in most trees, was a doubly charged form of the 6.6x10(3)-Da protein. The latter was identified as apolipoprotein C-I. M/z 3.1x10(3) was identified as an N-terminal fragment of albumin, and m/z 28x10(3) as apolipoprotein A-I. CONCLUSION: SELDI-TOF MS followed by classification tree pattern analysis is a suitable technique for finding new serum markers for CRC. Biomarkers can be identified and reproducibly detected in independent sample sets with high sensitivities and specificities. Although not specific for CRC, these biomarkers have a potential role in disease and treatment monitoring.

Algorithms↗
PubMed2002

A comparison of material classification techniques for ultrasound inverse imaging.

The conjugate gradient method with edge preserving regularization (CGEP) is applied to the ultrasound inverse scattering problem for the early detection of breast tumors. To accelerate image reconstruction, several different pattern classification schemes are introduced into the CGEP algorithm. These classification techniques are compared for a full-sized, two-dimensional breast model. One of these techniques uses two parameters, the sound speed and attenuation, simultaneously to perform classification based on a Bayesian classifier and is called bivariate material classification (BMC). The other two techniques, presented in earlier work, are univariate material classification (UMC) and neural network (NN) classification. BMC is an extension of UMC, the latter using attenuation alone to perform classification, and NN classification uses a neural network. Both noiseless and noisy cases are considered. For the noiseless case, numerical simulations show that the CGEP-BMC method requires 40% fewer iterations than the CGEP method, and the CGEP-NN method requires 55% fewer. The CGEP-BMC and CGEP-NN methods yield more accurate reconstructions than the CGEP method. A quantitative comparison of the CGEP-BMC, CGEP-NN, and GN-UMC methods shows that the CGEP-BMC and CGEP-NN methods are more robust to noise than the GN-UMC method, while all three are similar in computational complexity.

Breast Neoplasms↗
PubMed2006

Novel round-robin tabu search algorithm for prostate cancer classification and diagnosis using multispectral imagery.

Quantitative cell imagery in cancer pathology has progressed greatly in the last 25 years. The application areas are mainly those in which the diagnosis is still critically reliant upon the analysis of biopsy samples, which remains the only conclusive method for making an accurate diagnosis of the disease. Biopsies are usually analyzed by a trained pathologist who, by analyzing the biopsies under a microscope, assesses the normality or malignancy of the samples submitted. Different grades of malignancy correspond to different structural patterns as well as to apparent textures. In the case of prostate cancer, four major groups have to be recognized: stroma, benign prostatic hyperplasia, prostatic intraepithelial neoplasia, and prostatic carcinoma. Recently, multispectral imagery has been used to solve this multiclass problem. Unlike conventional RGB color space, multispectral images allow the acquisition of a large number of spectral bands within the visible spectrum, resulting in a large feature vector size. For such a high dimensionality, pattern recognition techniques suffer from the well-known "curse-of-dimensionality" problem. This paper proposes a novel round-robin tabu search (RR-TS) algorithm to address the curse-of-dimensionality for this multiclass problem. The experiments have been carried out on a number of prostate cancer textured multispectral images, and the results obtained have been assessed and compared with previously reported works. The system achieved 98%-100% classification accuracy when testing on two datasets. It outperformed principal component/linear discriminant classifier (PCA-LDA), tabu search/nearest neighbor classifier (TS-1NN), and bagging/boosting with decision tree (C4.5) classifier.

Artificial Intelligence↗
PubMed2001

DIAN: a novel algorithm for genome ontological classification.

Faced with the determination of many completely sequenced genomes, computational biology is now faced with the challenge of interpreting the significance of these data sets. A multiplicity of data-related problems impedes this goal: Biological annotations associated with raw data are often not normalized, and the data themselves are often poorly interrelated and their interpretation unclear. All of these problems make interpretation of genomic databases increasingly difficult. With the current explosion of sequences now available from the human genome as well as from model organisms, the importance of sorting this vast amount of conceptually unstructured source data into a limited universe of genes, proteins, functions, structures, and pathways has become a bottleneck for the field. To address this problem, we have developed a method of interrelating data sources by applying a novel method of associating biological objects to ontologies. We have developed an intelligent knowledge-based algorithm, to support biological knowledge mapping, and, in particular, to facilitate the interpretation of genomic data. In this respect, the method makes it possible to inventory genomes by collapsing multiple types of annotations and normalizing them to various ontologies. By relying on a conceptual view of the genome, researchers can now easily navigate the human genome in a biologically intuitive, scientifically accurate manner.

Algorithms↗
PubMed1998

Automated acquisition of rules from clinical databases and its evaluation.

This paper presents an approach to induction of rules from databases using rough set model. The system was evaluated on three clinical databases, and induced results were compared with other conventional rule induction methods and medical experts' rules. The results show that the introduced results outperforms other methods, but that the description length of induced rules is a little short, compared with that of experts' rules, which suggests that experts' rules are combination of different kinds of reasoning, rather than simple classification.

Algorithms↗
PubMed2006

Distance based algorithms for small biomolecule classification and structural similarity search.

MOTIVATION: Structural similarity search among small molecules is a standard tool used in molecular classification and in-silico drug discovery. The effectiveness of this general approach depends on how well the following problems are addressed. The notion of similarity should be chosen for providing the highest level of discrimination of compounds wrt the bioactivity of interest. The data structure for performing search should be very efficient as the molecular databases of interest include several millions of compounds. RESULTS: In this paper we focus on the k-nearest-neighbor search method, which, until recently was not considered for small molecule classification. The few recent applications of k-nn to compound classification focus on selecting the most relevant set of chemical descriptors which are then compared under standard Minkowski distance L(p). Here we show how to computationally design the optimal weighted Minkowski distance wL(p) for maximizing the discrimination between active and inactive compounds wrt bioactivities of interest. We then show how to construct pruning based k-nn search data structures for any wL(p) distance that minimizes similarity search time. The accuracy achieved by our classifier is better than the alternative LDA and MLR approaches and is comparable to the ANN methods. In terms of running time, our classifier is considerably faster than the ANN approach especially when large data sets are used. Furthermore, our classifier quantifies the level of bioactivity rather than returning a binary decision and thus is more informative than the ANN approach.

Algorithms↗
PubMed2001

Application of classification tree and neural network algorithms to the identification of serological liver marker profiles for the diagnosis of hepatocellular carcinoma.

OBJECTIVE: Although many attempts have been made to identify tumour-specific alpha-fetoprotein (AFP) glycoforms or other serological markers for the diagnosis of hepatocellular carcinoma (HCC), none of the available markers has, so far, shown satisfactory sensitivity and specificity. Here we aimed to apply classification tree and neural network algorithms to interpret the levels of multiple serological liver markers to improve overall specificity and sensitivity, particularly with a view to discriminating between liver cirrhosis with and without HCC. METHODS: We developed classification trees and neural networks that identified serological liver marker profiles comprising AFP, alpha1-antitrypsin (A1AT), alpha2-macroglobulin (A2MG), thyroxine-binding globulin (TBG), transferrin and albumin as well as sex and age, which might permit the diagnosis of HCC. Data were collected from 65 HCC patients, 51 patients with liver cirrhosis alone (LC) and 51 normal healthy subjects. RESULTS: The generated classification trees and neural networks showed similar diagnostic values in differentiating HCC from LC. The classification trees identified AFP, A1AT and albumin as the most important classification parameters, whereas the neural networks identified A2MG, AFP, A1AT and albumin as the predominant factors. The classification logic of the classification trees indicated that more HCC cases could be identified among cases with slightly elevated AFP levels by using the serum levels of A1AT and albumin. The neural networks were also useful for the identification of the HCC cases when the AFP levels were below 500 ng/ml (p < 0.005). The neural networks could identify HCC cases with AFP levels within the normal range, but the classification trees could not. By combining the conventional AFP test and the neural networks, the overall diagnostic sensitivity for HCC was significantly increased from 60.0 to 73.8% (p < 0.05) while maintaining a high specificity (88.2%). The sensitivities for tumors of different sizes were similar. CONCLUSION: The neural network algorithm appeared to be more powerful than the classification tree algorithm in the identification of the distinctive serological liver marker profiles for the diagnosis of the HCC subgroup without significant elevation in serum AFP levels. By incorporating serological levels of other liver markers and including data from a large number of patients and control subjects, it should prove possible to develop a versatile neural network for early diagnosis of HCC.

Adult↗
PubMed1996

High-energy ballistic and avulsive facial injuries: classification, patterns, and an algorithm for primary reconstruction.

A 17-year experience from 1977 to 1993 with gunshot, shotgun, and high-energy avulsive facial injuries emphasizes the superiority and safety of "ballistic wound" surgical management: (1) immediate stabilization in anatomic position of existing bone, (2) primary closure of existing soft tissue, (3) periodic "second look" serial debridement procedures, and (4) definitive early reconstruction of soft-tissue and bony defects. The series contains 250 gunshot wounds, 53 close-range shotgun wounds, and 15 high-energy avulsive facial injuries. Four general patterns of involvement are noted for both gunshot and shotgun wounds and three for avulsive facial injuries. The treatment algorithm begins with identifying zones of injury and loss for both soft and hard tissue. Gunshot wounds are best classified by the location of the exit wound; shotgun and avulsive facial wounds are classified according to the zone of soft-tissue and bone loss. Treatment, prognosis, and complications vary according to four patterns of gunshot wounds and four patterns of shotgun wounds. Avulsive wounds have not been recommended previously for ballistic wound surgical management. The appropriate management of high-energy avulsive and ballistic facial injuries is best approached by an aggressive treatment program emphasizing initial primary repair of existing tissue, serial conservative debridement, and early definitive reconstruction.

Adult↗
PubMed2004

An efficient and effective region-based image retrieval framework.

An image retrieval framework that integrates efficient region-based representation in terms of storage and complexity and effective on-line learning capability is proposed. The framework consists of methods for region-based image representation and comparison, indexing using modified inverted files, relevance feedback, and learning region weighting. By exploiting a vector quantization method, both compact and sparse (vector) region-based image representations are achieved. Using the compact representation, an indexing scheme similar to the inverted file technology and an image similarity measure based on Earth Mover's Distance are presented. Moreover, the vector representation facilitates a weighted query point movement algorithm and the compact representation enables a classification-based algorithm for relevance feedback. Based on users' feedback information, a region weighting strategy is also introduced to optimally weight the regions and enable the system to self-improve. Experimental results on a database of 10,000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework.

Abstracting and Indexing↗
PubMed2002

The Mozena Classification System and treatment algorithm for ingrown hallux nails.

Infected ingrown toenails raise the question of how much nail should be removed and what amount of nail fold reduction should occur. Often, the ungual labia folds are found to be hypertrophic, forcing the nail to push into the flesh and start a foreign body reaction. A simplified approach to this problem is proposed on the basis of the measurement of 100 normal nail folds and 25 infected nail folds. The results of this study show that the treatment goal should be to achieve an ungual labia fold of less than 3 mm, concluding that there is a correlation between the depth of the ungual labia fold and the severity of the infected ingrown toenail.

Algorithms↗
PubMedDate not supplied

Computer analysis of atypical urothelial cells. I. Classification by supervised learning algorithms.

Computer discrimination of atypical (ATY) urothelial cells from the urinary sediment by means of supervised learning algorithms discoled that these cells form a distinct, although ill-defined, family of cells which differs from normal (NEG) and malignant (POS) cell groups. The clinical significance of this observation must await long-term clinical follow-up. The possibility of issuing computer displays on individual patients with possible diagnostic and prognostic implications is discussed.

Aged↗