Biomedical subjects

Xiaobo Zhou

Publications and source records attributed to Xiaobo Zhou.

At least 19 recordsLinked to original sources

PubMed2006

A novel tracing algorithm for high throughput imaging Screening of neuron-based assays.

High throughput neuron image processing is an important method for drug screening and quantitative neurobiological studies. The method usually includes detection of neurite structures, feature extraction, quantification, and statistical analysis. In this paper, we present a new algorithm for fast and automatic extraction of neurite structures in microscopy neuron images. The algorithm is based on novel methods for soma segmentation, seed point detection, recursive center-line detection, and 2D curve smoothing. The algorithm is fully automatic without any human interaction, and robust enough for usage on images with poor quality, such as those with low contrast or low signal-to-noise ratio. It is able to completely and accurately extract neurite segments in neuron images with highly complicated neurite structures. Robustness comes from the use of 2D smoothening techniques and the idea of center-line extraction by estimating the surrounding edges. Efficiency is achieved by processing only pixels that are close enough to the line structures, and by carefully chosen stopping conditions. These make the proposed approach suitable for demanding image processing tasks in high throughput screening of neuron-based assays. Detailed results on experimental validation of the proposed method and on its comparative performance with other proposed schemes are included.

Algorithms↗

PubMed2006

Protein structure similarity from Principle Component Correlation analysis.

BACKGROUND: Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. RESULTS: We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC) analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. CONCLUSION: The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum eigenvalues can be highly effective in clustering structurally or topologically similar proteins. We believe that the PCC analysis of interaction matrix is highly flexible in adopting various structural parameters for protein structure comparison.

Algorithms↗

PubMed2006

Automated neurite labeling and analysis in fluorescence microscopy images.

BACKGROUND: To investigate the intricate nervous processes involved in many biological activities by computerized image analysis, accurate and reproducible labeling and measurement of neurites are prerequisite. We have developed an automated neurite analysis method to assist this task. METHODS: Our approach can be considered as automated with certain user interaction in setting initial parameters. Single and connected centerlines along neurites are extracted. The computerized method can also generate branching and end points. Owing to its multi-scale flexibility, both thick and thin neurites are simultaneously detected. RESULTS: We employ the relative neurite length difference (defined as the difference between the lengths obtained by automated and manual analysis divided by the total length of the latter) and neurite centerline deviation (defined as the area of the regions enclosed by different paths between automated and manual analysis divided by the total length of the former) to evaluate the performance of our algorithm, which is of great interest in neurite analysis. The average of the relative length difference is about 0.02, while the average of the centerline deviation is about 2.8 pixels. The probabilities of the distributions being the same from the Kolmogorov-Smirnov (KS) test of the automatic and manual results are 99.79%. The KS test also shows no significant bias between different observers based on the proposed new validation scheme. CONCLUSIONS: With the accurate and automated extraction of neurite centerlines and measurement of neurite lengths, the proposed method, which greatly reduces human labor and improves efficiency, can serve as a candidate tool for large-scale neurite analysis beyond the capability of manual tracing methods.

Algorithms↗

PubMed2006

Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy.

Quantitative measurement of cell cycle progression in individual cells over time is important in understanding drug treatment effects on cancer cells. Recent advances in time-lapse fluorescence microscopy imaging have provided an important tool to study the cell cycle process under different conditions of perturbation. However, existing computational imaging methods are rather limited in analyzing and tracking such time-lapse datasets, and manual analysis is unreasonably time-consuming and subject to observer variances. This paper presents an automated system that integrates a series of advanced analysis methods to fill this gap. The cellular image analysis methods can be used to segment, classify, and track individual cells in a living cell population over a few days. Experimental results show that the proposed method is efficient and effective in cell tracking and phase identification.

Artificial Intelligence↗

PubMed2006

Mutual information-based feature selection in studying perturbation of dendritic structure caused by TSC2 inactivation.

In this study, the effect of protein Tuberous sclerosis 2 (TSC2) on the dendritic spine density and length was demonstrated by using TSC2-RNAinactivation. In addition, the role of rapamycin, an antagonist of the molecular target of rapamycin, in the morphological changes of spine caused by TSC2 silencing was investigated. The features were extracted from highresolution three-dimensional image stacks collected by two-photon laser scanning microscopy of green fluorescing pyramidal cells expressing TSC2-RNA interference (RNAi), or TSC2-RNAi and rapamycin treatment in rat hippocampal slice cultures. We proposed to apply the lognormal distribution method for feature extraction. The extracted features of three cases under investigation, namely, (1) green-fluorescent protein GFP vs TSC2-RNAi, (2) GFP vs TSC2-RNAi and rapamycin, and (3) TSC2-RNAi vs TSC2-RNAi and rapamycin, were analyzed by mutual information-based feature selection and evaluated by three classifiers, K-nearest neighbor, Perceptron, and two-layer neural networks. The results showed that both the spine density and length have significant morphological changes after TSC2-RNAi treatment. However, rapamycin treatment could reverse the effect of TSC2-RNAi on spine length but not on spine density. These results are consistent with the results reported in the scientific literature. Finally, we explored the application of pattern recognition method in a small sample with richer feature properties, namely bootstrap mutual information estimation and a mutual information- based feature selection method.

Algorithms↗

PubMed2006

Bayesian variable selection for gene expression modeling with regulatory motif binding sites in neuroinflammatory events.

Multiple transcription factors (TFs) coordinately control transcriptional regulation of genes in eukaryotes. Although numerous computational methods focus on the identification of individual TF-binding sites (TFBSs), very few consider the interdependence among these sites. In this article, we studied the relationship between TFBSs and microarray gene expression levels using both family-wise and memberspecific motifs, under various combination of regression models with Bayesian variable selection, as well as motif scoring and sharing conditions, in order to account for the coordination complexity of transcription regulation. We proposed a three-step approach to model the relationship. In the first step, we preprocessed microarray data and used p-values and expression ratios to preselect upregulated and downregulated genes. The second step aimed to identify and score individual TFBSs within DNA sequence of each gene. A method based on the degree of similarity and the number of TFBSs was employed to calculate the score of each TFBS in each gene sequence. In the last step, linear regression and probit regression were used to build a predictive model of gene expression outcomes using these TFBSs as predictors. Given a certain number of predictors to be used, a full search of all possible predictor sets is usually combinatorially prohibitive. Therefore, this article considered the Bayesian variable selection for prediction using either of the regression models. The Bayesian variable selection has been applied in the context of gene selection, missing value estimation, and regulatory motif identification. In our modeling, the regressor was approximated as a linear combination of the TFBSs and a Gibbs sampler was employed to find the strongest TFBSs. We applied these regression models with the Bayesian variable selection on spinal cord injury gene expression data set. These TFs demonstrated intricate regulatory roles either as a family or as individual members in neuroinflammatory events. Our analysis can be applied to create plausible hypotheses for combinatorial regulation by TFBSs and avoiding false-positive candidates in the modeling process at the same time. Such a systematic approach provides the possibility to dissect transcription regulation, from a more comprehensive perspective, through which phenotypical events at cellular and tissue levels are moved forward by molecular events at gene transcription and translation levels.

Amino Acid Motifs↗

PubMed2005

Boosting alternating decision trees modeling of disease trait information.

We applied the alternating decision trees (ADTrees) method to the last 3 replicates from the Aipotu, Danacca, Karangar, and NYC populations in the Problem 2 simulated Genetic Analysis Workshop dataset. Using information from the 12 binary phenotypes and sex as input and Kofendrerd Personality Disorder disease status as the outcome of ADTrees-based classifiers, we obtained a new quantitative trait based on average prediction scores, which was then used for genome-wide quantitative trait linkage (QTL) analysis. ADTrees are machine learning methods that combine boosting and decision trees algorithms to generate smaller and easier-to-interpret classification rules. In this application, we compared four modeling strategies from the combinations of two boosting iterations (log or exponential loss functions) coupled with two choices of tree generation types (a full alternating decision tree or a classic boosting decision tree). These four different strategies were applied to the founders in each population to construct four classifiers, which were then applied to each study participant. To compute average prediction score for each subject with a specific trait profile, such a process was repeated with 10 runs of 10-fold cross validation, and standardized prediction scores obtained from the 10 runs were averaged and used in subsequent expectation-maximization Haseman-Elston QTL analyses (implemented in GENEHUNTER) with the approximate 900 SNPs in Hardy-Weinberg equilibrium provided for each population. Our QTL analyses on the basis of four models (a full alternating decision tree and a classic boosting decision tree paired with either log or exponential loss function) detected evidence for linkage (Z >or= 1.96, p < 0.01) on chromosomes 1, 3, 5, and 9. Moreover, using average iteration and abundance scores for the 12 phenotypes and sex as their relevancy measurements, we found all relevant phenotypes for all four populations except phenotype b for the Karangar population, with suggested subgroup structure consistent with latent traits used in the model. In conclusion, our findings suggest that the ADTrees method may offer a more accurate representation of the disease status that allows for better detection of linkage evidence.

Algorithms↗

PubMed2005

Overexpression of EB1 in human esophageal squamous cell carcinoma (ESCC) may promote cellular growth by activating beta-catenin/TCF pathway.

Esophageal squamous cell carcinoma (ESCC) has a multifactorial etiology involving environmental and/or genetic factors. End-binding protein 1 (EB1), which was cloned as an interacting partner of the adenomatous polyposis coli (APC) tumor suppressor protein, was previously found overexpressed in ESCC. However, the precise role of EB1 in the development of this malignancy has not yet been elucidated. In this study, we analysed freshly resected ESCC specimens and demonstrated that EB1 was overexpressed in approximately 63% of tumor samples compared to matched normal tissue. We report that overexpression of EB1 in the ESCC line EC9706 significantly promotes cell growth, whereas suppression of EB1 protein level by RNA interference significantly inhibited growth of esophageal tumor cells. In addition, EB1 overexpression induced nuclear accumulation of beta-catenin and promoted the transcriptional activity of beta-catenin/T-cell factor (TCF). These effects were partially or completely abolished by coexpression of APC or DeltaN TCF4, respectively. Also, we found that EB1 affected the interaction between beta-catenin and APC. Furthermore, EB1 overexpression was correlated with cytoplasmic/nuclear accumulation of beta-catenin in primary human ESCC. Taken together, these results support the novel hypothesis that EB1 overexpression may play a role in the development of ESCC by affecting APC function and activating the beta-catenin/TCF pathway.

Base Sequence↗

PubMed2005

Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection.

We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy.

Journal Article↗

PubMed2005

A computerized cellular imaging system for high content analysis in Monastrol suppressor screens.

In this paper, we describe a new bioimage informatics system developed for high content screening (HCS) applications with the goal to extract and analyze phenotypic features of hundreds of thousands of mitotic cells simultaneously. The system introduces the algorithm of multi-phenotypic mitotic analysis (MMA) and integrates that with algorithms of correlation analysis and compound clustering used in gene microarray studies. The HCS-MMA system combines different phenotypic information of cellular images obtained from three-channel acquisitions to distinguish and label individual cells at various phases of mitosis. The proposed system can also be used to extract and count the number of cells in each phase in cell-based assay experiments and archive the extracted data into a structured database for more sophisticated statistical and data analysis. To recognize different mitotic phases, binary patterns are set up based on a known biological mitotic spindle model to characterize cellular morphology of actin, microtubules, and DNA. To illustrate its utility, the HCS-MMA system has been applied to screen the quantitative response of 320 different drug compounds in suppressing Monastrol. The results are validated and evaluated by comparing the performance of HCS-MMA with visual analysis, as well as clustering of the drug compounds under evaluation.

Algorithms↗

PubMed2005

Overexpression of human pituitary tumor transforming gene (hPTTG), is regulated by beta-catenin /TCF pathway in human esophageal squamous cell carcinoma.

Overexpression of human pituitary tumor transforming gene (PTTG) is wildly detected in many tumors, including esophageal cancer. Besides overexpression of PTTG in esophageal squamous cell carcinoma (ESCC) tissues and cells, we detected accumulation of cytoplasmic beta-catenin in ESCC. In our study, a putative TCF4-binding element (TBE) was identified in PTTG promoter region. The activity of PTTG promoter containing the TBE was activated by S37Abeta-catenin and inhibited by dominant-negative TCF. Furthermore, the activation by S37Abeta-catenin was mostly abrogated among PTTG promoter region without the TBE or with a mutant one. By using biotin-streptavidin pull-down assay, we also found that the TBE among PTTG promoter bound to TCF-4 protein. Moreover, levels of PTTG mRNA and protein were increased by S37Abeta-catenin. Finally, it is noticeable that we detected a correlation between beta-catenin localization and PTTG expression in 69 primary ESCC (p<0.01). In brief, our study shows that overexpression of PTTG in ESCC is likely due to the activation of beta-catenin/WNT signaling. As a target gene of beta-catenin/TCF pathway, PTTG may play an important role in tumorigenesis of human ESCC.

Base Sequence↗

PubMed2005

Towards automated cellular image segmentation for RNAi genome-wide screening.

The Rho family of small GTPases is essential for morphological changes during normal cell development and migration, as well as during disease states such as cancer. Our goal is to identify novel effectors of Rho proteins using a cell-based assay for Rho activity to perform genome-wide functional screens using double stranded RNA (dsRNAs) interference. We aim to discover genes could cause the cell phenotype changed dramatically. Biologists currently attempt to perform the genome-wide RNAi screening to identify various image phenotypes. RNAi genome-wide screening, however, could easily generate more than a million of images per study, manual analysis is thus prohibitive. Image analysis becomes a bottleneck in realizing high content imaging screens. We propose a two-step segmentation approach to solve this problem. First, we determine the center of a cell using the information in the DNA-channel by segmenting the DNA nuclei and the dissimilarity function is employed to attenuate the over-segmentation problem, then we estimate a rough boundary for each cell using a polygon. Second, we apply fuzzy c-means based multi-threshold segmentation and sharpening technology; for isolation of touching spots, marker-controlled watershed is employed to remove touching cells. Furthermore, Voronoi diagrams are employed to correct the segmentation errors caused by overlapping cells. Image features are extracted for each cell. K-nearest neighbor classifier (KNN) is employed to perform cell phenotype classification. Experimental results indicate that the proposed approach can be used to identify cell phenotypes of RNAi genome-wide screens.

Algorithms↗

PubMed2005

Accumulation of cytoplasmic beta-catenin correlates with reduced expression of E-cadherin, but not with phosphorylated Akt in esophageal squamous cell carcinoma: immunohistochemical study.

Accumulation of beta-catenin in cytoplasm occurs frequently during the pathogenesis of esophageal squamous cell carcinoma (ESCC). The mechanism leading to this alteration, however, is largely unknown. In the present study, immunohistochemistry was performed for beta-catenin, E-cadherin and Ser473 phosphorylated Akt (P-Akt) in 44 tissue samples of ESCC and corresponding normal esophageal epithelium. Exon 3 of the beta-catenin gene was analyzed by using single-strand conformation polymorphism and direct sequencing. In addition to the reduced expression of E-cadherin and membranous beta-catenin observed in 65.9% and 68% of ESCC tested, respectively, cytoplasmic accumulation of beta-catenin was also detected in 68% (30/44) cases. However, only two cases were found to have the same beta-catenin gene mutation. The data showed that cytoplasmic accumulation of beta-catenin was significantly associated with reduced expression of E-cadherin (P < 0.05) and that of membranous beta-catenin (P < 0.05). Furthermore, cytoplasmic beta-catenin was correlated significantly with lymph node metastasis (P < 0.05). In contrast, although strong staining of P-Akt occurred in 14 of 44 cases (32%), there was no significant correlation between the positive staining of P-Akt and cytoplasmic beta-catenin. Taken together these results suggest that the lost membranous beta-catenin might translocate to cytoplasm depending on reduced expression of E-cadherin, while Akt seems unlikely to play a role in this process.

Adult↗

PubMed2004

A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks.

MOTIVATION: We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes. RESULTS: We propose a fully Bayesian approach to constructing probabilistic gene regulatory networks (PGRNs) that emphasizes network topology. The method computes the possible parent sets of each gene, the corresponding predictors and the associated probabilities based on a nonlinear perceptron model, using a reversible jump Markov chain Monte Carlo (MCMC) technique, and an MCMC method is employed to search the network configurations to find those with the highest Bayesian scores to construct the PGRN. The Bayesian method has been used to construct a PGRN based on the observed behavior of a set of genes whose expression patterns vary across a set of melanoma samples exhibiting two very different phenotypes with respect to cell motility and invasiveness. Key biological features have been faithfully reflected in the model. Its steady-state distribution contains attractors that are either identical or very similar to the states observed in the data, and many of the attractors are singletons, which mimics the biological propensity to stably occupy a given state. Most interestingly, the connectivity rules for the most optimal generated networks constituting the PGRN are remarkably similar, as would be expected for a network operating on a distributed basis, with strong interactions between the components.

Algorithms↗

PubMed2004

Cancer classification and prediction using logistic regression with Bayesian gene selection.

In microarray-based cancer classification and prediction, gene selection is an important research problem owing to the large number of genes and the small number of experimental conditions. In this paper, we propose a Bayesian approach to gene selection and classification using the logistic regression model. The basic idea of our approach is in conjunction with a logistic regression model to relate the gene expression with the class labels. We use Gibbs sampling and Markov chain Monte Carlo (MCMC) methods to discover important genes. To implement Gibbs Sampler and MCMC search, we derive a posterior distribution of selected genes given the observed data. After the important genes are identified, the same logistic regression model is then used for cancer classification and prediction. Issues for efficient implementation for the proposed method are discussed. The proposed method is evaluated against several large microarray data sets, including hereditary breast cancer, small round blue-cell tumors, and acute leukemia. The results show that the method can effectively identify important genes consistent with the known biological findings while the accuracy of the classification is also high. Finally, the robustness and sensitivity properties of the proposed method are also investigated.

Algorithms↗

PubMed2004

Gene clustering based on clusterwide mutual information.

Cluster analysis of gene-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and constructing gene regulatory networks. The motivation for considering mutual information is its capacity to measure a general dependence among gene random variables. We propose a novel clustering strategy based on minimizing mutual information among gene clusters. Simulated annealing is employed to solve the optimization problem. Bootstrap techniques are employed to get more accurate estimates of mutual information when the data sample size is small. Moreover, we propose to combine the mutual information criterion and traditional distance criteria such as the Euclidean distance and the fuzzy membership metric in designing the clustering algorithm. The performances of the new clustering methods are compared with those of some existing methods, using both synthesized data and experimental data. It is seen that the clustering algorithm based on a combined metric of mutual information and fuzzy membership achieves the best performance. The supplemental material is available at www.gspsnap.tamu.edu/gspweb/zxb/glioma_zxb.

Algorithms↗

PubMed2003

Missing-value estimation using linear and non-linear regression with Bayesian gene selection.

MOTIVATION: Data from microarray experiments are usually in the form of large matrices of expression levels of genes under different experimental conditions. Owing to various reasons, there are frequently missing values. Estimating these missing values is important because they affect downstream analysis, such as clustering, classification and network design. Several methods of missing-value estimation are in use. The problem has two parts: (1) selection of genes for estimation and (2) design of an estimation rule. RESULTS: We propose Bayesian variable selection to obtain genes to be used for estimation, and employ both linear and nonlinear regression for the estimation rule itself. Fast implementation issues for these methods are discussed, including the use of QR decomposition for parameter estimation. The proposed methods are tested on data sets arising from hereditary breast cancer and small round blue-cell tumors. The results compare very favorably with currently used methods based on the normalized root-mean-square error. AVAILABILITY: The appendix is available from http://gspsnap.tamu.edu/gspweb/zxb/missing_zxb/ (user: gspweb; passwd: gsplab).

Algorithms↗

PubMed2003

Binarization of microarray data on the basis of a mixture model.

Although gathered as continuous data, expression measurements from gene microarrays may be quantized before downstream analysis and modeling. This is especially true for modeling gene prediction and genetic regulatory networks. Coarse quantization results in lower computational requirements, lower data requirements for model inference, and easier conceptualization. This paper proposes a mixture model for binarization. For each gene, the model, composed of a sum of two distributions, is fit to expression data for that gene, and data points are binarized according to the model. The mixture model is based on the assumption of multiplicative up-regulation. The proposed method is compared with mean and median binarization by comparing classification performance based on the binary data from the different methods. Classification is performed for simulated data generated from a microarray model studied previously and for cancer data arising from two studies involving hereditary breast cancer and small, round blue-cell tumors of childhood.

Breast Neoplasms↗