Narrow Results

Based on the theory of constitution of Traditional Chinese Medicine (TCM), the human population can be classified into nine constitutions including a balanced constitution and eight unbalanced constitutions (Yang-deficient, Yin-deficient, Qi-deficient, Phlegm-wetness, Wetness-heat, Stagnant blood, Depressed, and Inherited special constitutions). Generally, unbalanced constitutions are more susceptible to certain diseases than balanced constitutions. However, whether such constitution classification has modern genetic and biochemical basis is poorly understood. Here we examined gene expression profiles in peripheral white blood cells from eight individuals with Yang-deficient constitutions and six individuals with balanced constitutions using Affymetrix U133 plus 2.0 expression array. Based on a q < 0.05 and fold-change ≥ 2 cutoff, we have identified that 785 genes are up-regulated and 954 genes are down-regulated in Yang-deficient constitution compared to a balanced constitution. Importantly, we found that the expression of thyroid hormone receptor beta (TRβ) and several key nuclear receptor coactivators including steroid receptor coactivator 1 (SRC1), steroid receptor coactivator 3 (SRC3), cAMP-response element-binding protein (CREB) binding protein (CBP) and Mediator is significantly decreased. Such decreased expression of TR transcription complex may lead to impaired thermogenesis, providing a molecular explanation of the main symptom associated with Yang-deficient constitution, cold intolerance. Future studies are needed to validate these gene expression changes in additional populations and address the underlying mechanisms for differential gene expression.

Previous studies on tumor classification based on feature extraction from gene expression profiles (GEP) were proven to be effective, but some of such methods lack biomedical meaning to some extent. To deal with this problem, we proposed a novel feature extraction method whose experimental results are of biomedical interpretability and helpful for gaining insight into the structure analysis of gene expression dataset. This method first applied rank sum test to roughly select a set of informative genes and then adopted factor analysis to extract latent factors for tumor classification. Experiments on three pairs of cross-platform tumor datasets indicated that the proposed method can obviously improve the performance of cross-platform classification and only several latent factors, which can represent a large number of informative genes, would obtain very high predictive accuracy on test set. The results also suggested that the classification model trained on one dataset can successfully predict another tumor dataset with the same tumor subtype obtained on different experimental platforms.

Machine learning is widely applied to gene expression profiles based molecular tumor classification, but sample imbalance problem is often overlooked. This paper proposed a subclass-weighted neighborhood classifier to address the imbalanced sample set problem and a novel neighborhood rough set model to select informative genes for classification performance improvement. Experiments on three publicly available tumor datasets demonstrated that the proposed method is obviously effective on imbalanced dataset with obscure boundary between two subtypes and informative gene selection and it can achieve higher cross-validation accuracy with much fewer tumor-related genes.

An important issue in the design of gene selection algorithm for microarray data analysis is the formation of suitable criterion function for measuring the relevance between different gene expressions. Mutual information (MI) is a widely used criterion function but it calculates the relevance on the entire samples only once which cannot exactly identify the informative genes. This paper proposes a novel idea of computing MI in stages. The proposed multistage mutual information (MSMI) computes MI, initially using all the samples and based on the classification performance produced by artificial neural network (ANN), MI is repeatedly calculated using only the unclassified samples until there is no improvement in the classification accuracy. The performance of the proposed approach is evaluated using ten gene expression data sets. Simulation result shows that the proposed approach helps to improve the discriminate power of the genes with regard to the target disease of a microarray sample. Statistical analysis of the test result shows that the proposed method selects highly informative genes and produces comparable classification accuracy than the other approaches reported in the literature.

We present a method for She classification of cancer based on gene expression profiles using single genes. We select the genes with high class-discrimination capability according to their depended degree by the classes. We then build classifiers based on the decision rules induced by single genes selected. We test our single-gene classification method on three publicly available cancerous gene expression datasets. In a majority of cases, we gain relatively accurate classification outcomes by just utilizing one gene. Some genes highly correlated with the pathogenesis of cancer are identified. Our feature selection and classification approaches are both based on rough sets, a machine learning method. In comparison with other methods, our method is simple, effective and robust. We conclude that, if gene selection is implemented reasonably, accurate molecular classification of cancer can be achieved with very simple predictive models based on gene expression profiles.

Microarray experimentation has been widely used for screening and high throughput discovery of mechanisms underlying biological systems. However, there are many factors that need to be taken into account for an effective microarray experimental design. Such factors are the abundance of starting material (RNA), number of replicates or experimental cost. The present work proposes rational strategies for microarray experimental design, with the scope to minimize signal variance and improve precision, with the minimum amount of technical replicates needed, thus alleviating the burden of biological material collection and the cost of the experiment. In this sense, alternative microarray design strategies are tested (direct comparisons, all paired comparisons, indirect comparisons through the implementation of various loop strategies), targeting to minimize the wet lab experimental effort needed, for the more precise interpretation of the biological question investigated. In this work we propose a methodological approach for the computational implementation of comparisons between different microarray experimental samples which are not co-hybridized together. Moreover, this approach is tested in-silico, on a showcase of a simple "loop" experimental design, including 3 biological triplicates. The proposed methodological approach introduces a robust method for calculating and comparing gene expression profiles between sets of microarray experiments by using the minimum number of experiments on one hand and getting robust results on the other. Further extension of this work could form a rational operational computational framework for the design of microarray or more general high-throughput biological experiments.