Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice

FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC OXYGENATION PATTERNS

Diabetic patients might present peripheral microcirculation impairment and might benefit from physical training. Thirty-nine diabetic patients underwent the monitoring of the tibialis anterior muscle oxygenation during a series of voluntary ankle flexo-extensions by near-infrared spectroscopy (NIRS). NIRS signals were acquired before and after training protocols. Sixteen control subjects were tested with the same protocol. Time-frequency distributions of the Cohen's class were used to process the NIRS signals relative to the concentration changes of oxygenated and reduced hemoglobin. A total of 24 variables were measured for each subject and the most discriminative were selected by using four feature selection algorithms: QuickReduct, Genetic Rough-Set Attribute Reduction, Ant Rough-Set Attribute Reduction, and traditional ANOVA. Artificial neural networks were used to validate the discriminative power of the selected features. Results showed that different algorithms extracted different sets of variables, but all the combinations were discriminative. The best classification accuracy was about 70%. The oxygenation variables were selected when comparing controls to diabetic patients or diabetic patients before and after training. This preliminary study showed the importance of feature selection techniques in NIRS assessment of diabetic peripheral vascular impairmen

Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy

In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. Its low complexity is based on the reuse of previous computations to calculate current feature relevance. The mu-TAFS algorithm --named as such to differentiate it from previous TAFS algorithms-- implements a simulated annealing technique specially designed for feature subset selection. The algorithm is applied to the maximization of gene subset relevance in several public-domain microarray data sets. The experimental results show a notoriously high classification performance and low size subsets formed by biologically meaningful genes.Postprint (published version

Knowledge-based variable selection for learning rules from proteomic data

<p>Abstract</p> <p>Background</p> <p>The incorporation of biological knowledge can enhance the analysis of biomedical data. We present a novel method that uses a proteomic knowledge base to enhance the performance of a rule-learning algorithm in identifying putative biomarkers of disease from high-dimensional proteomic mass spectral data. In particular, we use the Empirical Proteomics Ontology Knowledge Base (EPO-KB) that contains previously identified and validated proteomic biomarkers to select <it>m/z</it>s in a proteomic dataset prior to analysis to increase performance.</p> <p>Results</p> <p>We show that using EPO-KB as a pre-processing method, specifically selecting all biomarkers found only in the biofluid of the proteomic dataset, reduces the dimensionality by 95% and provides a statistically significantly greater increase in performance over no variable selection and random variable selection.</p> <p>Conclusion</p> <p>Knowledge-based variable selection even with a sparsely-populated resource such as the EPO-KB increases overall performance of rule-learning for disease classification from high-dimensional proteomic mass spectra.</p

Investigating data mining techniques for extracting information from Alzheimer\u27s disease data

Data mining techniques have been used widely in many areas such as business, science, engineering and more recently in clinical medicine. These techniques allow an enormous amount of high dimensional data to be analysed for extraction of interesting information as well as the construction of models for prediction. One of the foci in health related research is Alzheimer\u27s disease which is currently a non-curable disease where diagnosis can only be confirmed after death via an autopsy. Using multi-dimensional data and the applications of data mining techniques, researchers hope to find biomarkers that will diagnose Alzheimer\u27s disease as early as possible. The primary purpose of this research project is to investigate the application of data mining techniques for finding interesting biomarkers from a set of Alzheimer\u27s disease related data. The findings from this project will help to analyse the data more effectively and contribute to methods of providing earlier diagnosis of the disease

Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis

<p>Abstract</p> <p>Background</p> <p>Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public.</p> <p>Results</p> <p>In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM). Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile.</p> <p>Conclusion</p> <p>Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases. Moreover, with its open source architecture, interested researchers can further develop and extend this software based on the existing infrastructure.</p