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We focus on the problem of prediction with confidence and describe a recently developed learning algorithm called transductive confidence machine for making qualified region predictions. Its main advantage, in comparison with other classifiers, is that it is well-calibrated, with number of prediction errors strictly controlled by a given predefined confidence level. We apply the transductive confidence machine to the problems of acute leukaemia and ovarian cancer prediction using microarray and proteomics pattern diagnostics, respectively. We demonstrate that the algorithm performs well, yielding well-calibrated and informative predictions whilst maintaining a high level of accuracy.

Microarray gene expression data usually have a large number of dimensions, e.g., over ten thousand genes, and a small number of samples, e.g., a few tens of patients. In this paper, we use the support vector machine (SVM) for cancer classification with microarray data. Dimensionality reduction methods, such as principal components analysis (PCA), class-separability measure, Fisher ratio, and t-test, are used for gene selection. A voting scheme is then employed to do multi-group classification by k(k - 1) binary SVMs. We are able to obtain the same classification accuracy but with much fewer features compared to other published results.

Damp obstruction refers to the stagnation of vital energy (qi) caused by dampness resulting in dysfunction of body and limbs movement, as well as impairment of spleen and stomach digestive function. Damp obstruction is the dampness-induced imbalance of five elements; thus it serves as an ideal model for genomic study using cDNA microarray. We have performed microarray analyses to major organs of damp-obstructed rats. Cluster analysis for the expression profiles of major organs indicated that spleen, stomach, and kidney respond to dampness differently from heart, liver, lung, and brain. Gene expression profile specific to each element or group of elements was also identified. Our results are consistent with the philosophy of Chinese medicine that the five elements, metal (lung), wood (liver), water (kidney), fire (heart), and earth (spleen and stomach) coordinate by subjugation or restriction to maintain a healthy, physiological state. This is the first time that a powerful genomic tool was applied to probe the ancient theory of Chinese medicine.

The aim of the present study is to probe candidate genes which were involved in the electroacupuncture (EA) analgesia and to understand the molecular basis of the individual difference of EA analgesia in rats. We compared hypothalamus transcriptional profiles of responders with those of non-responders after 1 Hz EA treatment at ST36 acupoint for 1 hour by using oligonucleotide microarray. Responders and non-responders were determined by tail flick latency (TFL). A real-time quantitative RT-PCR was applied to validate the differential expressed genes. Our study provided a global hypothalamus transcriptional profile of EA analgesia in rats. We found that 63 and 3 genes were up- and down-regulated in the responder group, respectively. Half of the differentially expressed genes were classified into 9 functional groups which were ion transport, sensory perception, synaptogenesis and synaptic transmission, signal transduction, inflammatory response, apoptosis, transcription, protein amino acid phosphorylation and G-protein signaling. Glutamatergic receptors, ghrelin precursor, melanocortin 4 receptor (MC4-R) and neuroligin 1 were found to be up-regulated in the responder group which may become new targets for nociceptive study and deserve further investigation for developing new acupuncture therapy and intervention of pain modulation.

Traditional Chinese medicine (TCM) has been used for thousands of years. Most Chinese herbal formulae consist of several herbal components and have been used to treat various diseases. However, the mechanisms of most formulae and the relationship between formulae and their components remain to be elucidated. Here we analyzed the putative mechanism of San-Huang-Xie-Xin-Tang (SHXXT) and defined the relationship between SHXXT and its herbal components by microarray technique. HepG2 cells were treated with SHXXT or its components and the gene expression profiles were analyzed by DNA microarray. Gene set enrichment analysis indicated that SHXXT and its components displayed a unique anti-proliferation pattern via p53 signaling, p53 activated, and DNA damage signaling pathways in HepG2 cells. Network analysis showed that most genes were regulated by one molecule, p53. In addition, hierarchical clustering analysis showed that Rhizoma Coptis shared a similar gene expression profile with SHXXT. These findings may explain why Rhizoma Coptis is the principle herb that exerts the major effect in the herbal formula, SHXXT. Moreover, this is the first report to reveal the relationship between formulae and their herbal components in TCM by microarray and bioinformatics tools.

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.

Hydrogen peroxide (H₂O₂) has been shown to promote neurodegeneration by inducing the activation of nuclear factor-κB (NF-κB). In this study, NF-κB activation was induced by H₂O₂ in human neuroblastoma SH-SY5Y cells. Whether paeonol, one of the phenolic phytochemicals isolated from the Chinese herb Paeonia suffruticosa Andrews (MC), would attenuate the H₂O₂-induced NF-κB activity was investigated. Western blot results showed that paeonol inhibited the phosphorylation of IκB and the translocation of NF-κB into the nucleus. The ability of paeonol to reduce DNA binding ability and suppress the H₂O₂-induced NF-κB activation was confirmed by an electrophoretic mobility shift assay and a luciferase reporter assay. Using a microarray combined with gene set analysis, we found that the suppression of NF-κB was associated with mature T cell up-regulated genes, the c-jun N-terminal kinase pathway, and two hypoxia-related gene sets, including the hypoxia up-regulated gene set and hypoxia inducible factor 1 targets. Moreover, using network analysis to investigate genes that were altered by H₂O₂ and reversely regulated by paeonol, we found that NF-κB was the primary center of the network and amyloid precursor protein (APP) was the secondary center. Western blotting showed that paeonol inhibited APP at the protein level. In conclusion, our work suggests that paeonol down-regulates H₂O₂-induced NF-κB activity, as well as NF-κB-associated APP expression. Furthermore, the gene expression profile accompanying the suppression of NF-κB by paeonol was identified. The new gene set that can be targeted by paeonol provided a potential use for this drug and a possible pharmacological mechanism for other phenolic compounds that protect against oxidative-related injury.

The commercial quality of Japanese Angelica radices — Angelica acutiloba Kitagawa (Yamato-toki) and A. acutiloba Kitagawa var. sugiyama Hikino (Hokkai-toki) — used in Kampo traditional herbal medicines, was studied by use of omics technologies. Complementary and alternative medical providers have observed in their clinical experience that differences in radix commercial quality reflect the differences in pharmacological responses; however, there has been little scientific examination of this phenomenon. The approach of omics, including metabolomics, transcriptomics, genomics, and informatics revealed a distinction between the radix-quality grades based on their metabolites, gene expression in human subjects, and plant genome sequences. Systems biology, constructing a network of omics data used to analyze this complex system, is expected to be a powerful tool for enhancing the study of radix quality and furthering a comprehensive understanding of all medicinal plants.

Lung cancer is the leading cause of cancer-related death worldwide. Non-small cell lung cancer (NSCLC) accounts for 80% of lung cancer cases and the reported overall 5-year survival rate is less than 5%. Natural medicines have attracted much attention due to their lower toxicity and fewer side effects. Trichosanthes kirilowii Maxim (TKM) fruits are commonly used in cancer treatment in combination with other Chinese medicinal herbs. However, little is known about their biological functions and mechanisms in NSCLC cells. In this study, we investigated the efficacy of TKM fruits in NSCLC cells using cell proliferation, invasion, migration, and anchorage independent assays and a Xenograft NSCLC tumor model, and explored the possible biological mechanism by flow cytometric analysis, cDNA microarray and real-time PCR. Results showed that TKM fruits significantly suppressed NSCLC cell proliferation, migration, invasion, tumorigenicity and tumor growth, and significantly extended the survival time of NSCLC-bearing mice. Flow cytometric analysis showed that TKM fruits significantly induced G2-M arrest, necrosis and apoptosis in NSCLC cells. cDNA microarray analysis revealed that TKM fruits regulated the differential expression of 544 genes, and the differential expression of selected genes was also confirmed. Gene ontology (GO) analysis showed that 18 of first 20 biological processes were involved in cell cycle and mitosis. These results indicate that TKM fruits have certain inhibitory effect on NSCLC cells through cell-cycle and mitosis arrest, and suggest that TKM fruits may be an important resource for developing new antitumor drugs, and a potent natural product for treating patients with NSCLC.

Microarray technology has been increasingly recognized as a powerful means for monitoring the expression levels of thousands of genes simultaneously. Microarray image processing is an essential aspect of microarray experiment, of which gridding is thought to be the most important step of spot recognition. Many times, microarray image gridding requires assisted intervention to achieve the acceptable accuracy. In this paper, an automatic microarray image gridding algorithm was presented by using image projection vectors together with power spectrum model. For obtaining grid position, the image projection vectors were utilized by adequately considering the grid parameters. On the other hand, as a preprocessing procedure of microarray gridding, detection of the grid rotation was involved in our study by using power spectrum analyses of the image projection vectors. Our approach has been evaluated by three different microarray datasets. Experimental comparisons with up-to-date approaches by using both synthetic and real image data are demonstrated. The gridding result was shown to be very accurate, and able to provide correct gridding dataset for the downstream microarray analyses. In summary, our study demonstrated the combination of image projection vectors with power spectrum model as a powerful strategy for microarray image gridding.

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.

Microarray technologies are capable of simultaneously measuring the signals for thousands of messenger RNAs and large numbers of proteins from single samples. Arrays are now widely used in basic biomedical research for mRNA expression profiling and are increasingly being used to explore patterns of gene expression in clinical research. Most research has focused on the interpretation of the meaning of the microarray data which are transformed into gene expression matrices where usually the rows represent genes, the columns represent various samples. Clustering samples can be done by analyzing and eliminating of irrelevant genes. However, majority methods are supervised (or assisted by domain knowledge), less attention has been paid on unsupervised approaches which are important when little domain knowledge is available. In this paper, we present a new framework for unsupervised analysis of gene expression data, which applies an interrelated two-way clustering approach on the gene expression matrices. The goal of clustering is to identify important genes and perform cluster discovery on samples. The advantage of this approach is that we can dynamically manipulate the relationship between the gene clusters and sample groups while conducting an iterative clustering through both of them. The performance of the proposed method with various gene expression data sets is also illustrated.

Classification of microarray data is one of the major research interests in the biomedical field. It allows physicians for early detection of cancer through analysis of the Deoxyribonucleic acid (DNA). Classification of these sensitive data is still challenging due to the small sample and more feature size. In this paper, the authors have used an ensemble model for classifying two types of leukemia as Acute lymphocytic leukemia (ALL) and Acute myelocytic leukemia (AML). The work is carried out with other types of genetic data such as Leukemia, lung tumor, liver cancer, and liver Cirrhosis. The biomedical data are imbalanced. The ensemble classifier is based on a stacked approach where deep neural network (DNN) classifiers are used as the base classifier. The structure of each DNN is chosen as the homogenous type for the same training process for all classifiers. Because of the adaptive nature and random weight initialization, it provides different results for each classifier. The outputs of the base classifiers are again fed to a gradient boosting ensemble model termed a meta classifier. The meta classifier provides the final classification output. For comparison purposes, two types of meta-classifiers such as support vector machine (SVM) and ensemble gradient boosting are used in the proposed work. The performance of the model is verified well and the results are provided in the result section. From the experimental result, it is observed that the classification accuracy is 96% with SVM and 98% with boosted meta classifier for leukemia data, whereas 99.04% for lung tumor and 99.03% for liver Cirrhosis.

Affymetrix Launches NimbleExpress.

More Convenient Microarray Labeling Available.

Ocimum Introduces siRNA Design Software.

Rapidly Growing Stem Cell Market in Taiwan.

Evotec Technologies Provides InsightTM Cell.

Agilent's New HD-CGH Custom Microarrays for Pinpointing Cancer-related Genetic Abnormalities.

World's Largest Parallel Protein Production Facility in Monash University.

Agilent Introduces Latest and Most Comprehensive Microarray.

Over the past years applications of fusion technique have been growing rapidly. However, very few applications of the technique to microarray data have been reported. In this paper, we propose a new fusion method based on pseudo standard deviation minimization (PSDM) for the feature selection of microarray. This new method provides a more accurate set of features. Therefore the classification can be performed and functional meaning from the features can also be revealed. The new method is actually obtained through a combination of two different feature selection methods (FSMs). It is shown that it can explore nonperfect correlation between gene expression profile and cancer classes or feature detection algorithms. To evaluate its effectiveness, it is tested on lymphoma and leukemia microarray expression datasets and then compared with the existing methods. Self-organizing map (SOM) is used for feature classification. It can be seen through the comparison that the classification accuracy of the new fusion method is at least 2% ~ 3% higher than others.

A variety of DNA, protein or cell microarray devices and systems have been developed and commercialized. In addition to the biomolecule related analysis, they are also being used for pharmacogenomic research, infectious and genetic disease and cancer diagnostics, and proteomic and cellular analysis.¹ Currently, microarray is fabricated on a planar surface; this limits the amount of biomolecules that can be bounded on the surface. In this work, a planar protein microarray chip with nonplanar spot surface was fabricated to enhance the chip performance. A nonplanar spot surface was created by first coating the silica nanoparticles with albumin and depositing them into the patterned microwells. The curve surfaces of the nanoparticles increase the surface area for immobilization of proteins, which helps to enhance the detection sensitivity of the chip. Using this technique, proteins are immobilized onto the nanoparticles before they are deposited onto the chip, and therefore the method of protein immobilization can be customized at each spot. Furthermore, a nonplanar surface promotes the retention of native protein structure better than planar surface.² The technique developed can be used to produce different types of microarrays, such as DNA, protein and antibody microarrays.

Microarray technologies facilitate the generation of vast amount of bio-signal or genomic signal data. The major challenge in processing these signals is the extraction of the global characteristics of the data due to their huge dimension and the complex relationship among various genes. Statistical methods are used in broad spectrum in this domain. But, various limitations like extensive preprocessing, noise sensitiveness, requirement of critical input parameters and prior knowledge about the microarray dataset emphasise the need for better exploratory techniques. Transform oriented signal processing techniques are successful in many data processing techniques like image and video processing. But, the use of wavelets in analyzing the microarray bio-signals is not sufficiently probed. The aim of this paper is to propose a wavelet power spectrum based technique for dimensionality reduction through gene selection and classification problem of gene microarray data. The proposed method was administered on such datasets and the results are encouraging. The present method is robust to noise since no preprocessing has been applied. Also, it does not require any critical input parameters or any prior knowledge about the data which is required in many existing methods.

We present a fast method that selects oligonucleotide probes (such as DNA 25-mers) for microarray experiments on a truly large scale. For example, reliable oligos for human genes can be found within four days, a speedup of one to two orders of magnitude compared to previous approaches. This speed is attained by using the longest common substring as a specificity measure for candidate oligos. We present a space- and time-efficient algorithm, based on a suffix array with additional information, to compute matching statistics (lengths of longest matches) between all candidate oligos and all remaining sequences. With the matching statistics available, we show how to incorporate constraints such as oligo length, melting temperature, and self-complementarity into the selection process at a postprocessing stage. As a result, we can now design custom oligos for any sequenced genome, just as the technology for on-site chip synthesis is becoming increasingly mature.