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PubMed2005

Microarray analysis of endometrial carcinomas and mixed mullerian tumors reveals distinct gene expression profiles associated with different histologic types of uterine cancer.

Previous studies using cDNA microarray have indicated that distinct gene expression profiles characterize endometrioid and papillary serous carcinomas of the endometrium. Molecular studies have observed that mixed mullerian tumors, characterized by both carcinomatous and sarcomatous components, share features that are characteristic of endometrial carcinomas. The objective of this analysis was to more precisely define gene expression patterns that distinguish endometrioid and papillary serous histologies of endometrial carcinoma and mixed mullerian tumors of the uterus. One hundred nineteen pathologically confirmed uterine cancer samples were studied (66 endometrioid, 24 papillary serous, and 29 mixed mullerian tumors). Gene expressions were analyzed using the Affymetrix Human Genome Arrays U133A and U133B Genechip set. Unsupervised analysis revealed distinct global gene expression patterns of endometrioid, papillary serous, mixed mullerian tumors, and normal tissues as grossly separated clusters. Two-sample t tests comparing endometrioid and papillary serous, endometrioid and mixed mullerian tumor, and papillary serous and mixed mullerian tumor pairs identified 1,055, 5,212, and 1,208 differentially expressed genes at P < 0.001, respectively. These data revealed that distinct patterns of gene expression characterize various histologic types of uterine cancer. Gene expression profiles for select genes were confirmed using quantitative PCR. An understanding of the molecular heterogeneity of various histologic types of endometrial cancer has the potential to lead to better individualization of treatment in the future.

Cluster Analysis↗
PubMed2004

Daily iron supplementation is more efficacious than twice weekly iron supplementation for the treatment of childhood anemia in western Kenya.

A recent meta-analysis of 14 clinical trials indicated that daily compared with intermittent iron supplementation resulted in significantly greater hematological improvement in pregnant women. No such definitive beneficial effect was demonstrated in preschool children. We compared the efficacy of daily and twice weekly iron supplementation for 6 wk under supervised and unsupervised conditions in the treatment of mild and moderate anemia [hemoglobin (Hb) 50-109 g/L] in children aged 2-59 mo living in a malaria-endemic area of western Kenya. The study was a cluster-randomized trial using a factorial design; participants were aware of the treatment assigned. All children (n = 1049) were administered a single dose of sulfadoxine-pyrimethamine at enrollment followed by 6 wk of daily supervised iron supplementation [3-6 mg/(kg.d)], twice weekly supervised iron supplementation [6-12 mg/(kg.wk)], daily unsupervised iron supplementation, or twice weekly unsupervised iron supplementation. In the supervised groups, Hb concentrations at 6 and 12 wk (6 wk postsupplementation) were significantly higher in children given iron daily rather than twice weekly [mean (95% CI) difference at 6-wk: 4.2 g/L (2.1, 6.4); 12-wk: 4.4 g/L (1.8, 7.0)]. Among the unsupervised groups, Hb concentrations were not different at 6 wk [mean (95% CI) difference: 0.86 g/L (-1.4, 3.1)], but significantly higher at 12 wk for those assigned daily iron [mean (95% CI) difference: 3.4 g/L (0.79, 6.0), P = 0.02]. In this malarious area and after initial antimalarial treatment, 6 wk of daily iron supplementation results in better hematological responses than twice weekly iron supplementation in the treatment of anemia in preschool children, regardless of whether adherence can be ensured.

Anemia↗
PubMed2001

Unsupervised learning from complex data: the matrix incision tree algorithm.

Analysis of large-scale gene expression data requires novel methods for knowledge discovery and predictive model building as well as clustering. Organizing data into meaningful structures is one of the most fundamental modes of learning. DNA microarray data set can be viewed as a set of mutually associated genes in a high-dimensional space. This paper describes a novel method to organize a complex high-dimensional space into successive lower-dimensional spaces based on the geometric properties of the data structure in the absence of a priori knowledge. The matrix incision tree algorithm reveals the hierarchical structural organization of observed data by determining the successive hyperplanes that 'optimally' separate the data hyperspace. The algorithm was tested against published data sets yielding promising results.

Algorithms↗
PubMed2005

Super paramagnetic clustering of protein sequences.

BACKGROUND: Detection of sequence homologues represents a challenging task that is important for the discovery of protein families and the reliable application of automatic annotation methods. The presence of domains in protein families of diverse function, inhomogeneity and different sizes of protein families create considerable difficulties for the application of published clustering methods. RESULTS: Our work analyses the Super Paramagnetic Clustering (SPC) and its extension, global SPC (gSPC) algorithm. These algorithms cluster input data based on a method that is analogous to the treatment of an inhomogeneous ferromagnet in physics. For the SwissProt and SCOP databases we show that the gSPC improves the specificity and sensitivity of clustering over the original SPC and Markov Cluster algorithm (TRIBE-MCL) up to 30%. The three algorithms provided similar results for the MIPS FunCat 1.3 annotation of four bacterial genomes, Bacillus subtilis, Helicobacter pylori, Listeria innocua and Listeria monocytogenes. However, the gSPC covered about 12% more sequences compared to the other methods. The SPC algorithm was programmed in house using C++ and it is available at http://mips.gsf.de/proj/spc. The FunCat annotation is available at http://mips.gsf.de. CONCLUSION: The gSPC calculated to a higher accuracy or covered a larger number of sequences than the TRIBE-MCL algorithm. Thus it is a useful approach for automatic detection of protein families and unsupervised annotation of full genomes.

Algorithms↗
PubMed2000

Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation.

An algorithm for the segmentation of a single sequence of three-dimensional magnetic resonance (MR) images into cerebrospinal fluid, gray matter, and white matter classes is proposed. This new method is a possibilistic clustering algorithm using the fuzzy theory as frame and the wavelet coefficients of the voxels as features to be clustered. Fuzzy logic models the uncertainty and imprecision inherent in MR images of the brain, while the wavelet representation allows for both spatial and textural information. The procedure is fast, unsupervised, and totally independent of any statistical assumptions. The method is tested on a phantom image, then applied to normal and Alzheimer's brains, and finally compared with another classic brain tissue segmentation method, affording a relevant classification of voxels into the different tissue classes.

Adult↗
PubMed2005

Defective DNA mismatch repair determines a characteristic transcriptional profile in proximal colon cancers.

BACKGROUND & AIMS: Colon cancers with defective DNA mismatch repair (MMR) have peculiar molecular, pathologic, and clinical features, including high-level microsatellite instability, conspicuous lymphocytic infiltration, preferential location in the proximal colon, and better prognosis. Our aim was to characterize the transcriptional profile of this colon cancer subset. METHODS: An oligonucleotide microarray containing 12,625 probes was used to evaluate gene expression in 25 proximal colon cancers, 10 samples of normal colon mucosa, and 14 colon cancer cell lines. Transcriptional profiles of MMR-deficient cancers and cell lines were compared with those of their MMR-proficient counterparts. RESULTS: Unsupervised analysis of microarray data showed that MMR status exerts a predominant influence on the gene expression profile of proximal colon cancers. Hierarchical clustering divided the cancers into 2 groups corresponding almost perfectly with their MMR status. Supervised analysis identified numerous gene expression changes that represent a genetic signature of MMR-deficient colon cancers. Changes in genes involved in apoptosis and the immune response were consistent with the better prognosis of MMR-deficient cancers. In MMR-deficient cancers and cell lines, 4-1BBL, a crucial gene in the anti-tumor immune response, was, respectively, 2.4 and 6.0 times more expressed than in their MMR-proficient counterparts. This difference was confirmed by quantitative reverse-transcription polymerase chain reaction and flow cytometric assessment of 4-1BBL protein expression in colon cancer cell lines. Our analysis also showed novel possible gene targets of microsatellite instability. CONCLUSIONS: MMR inactivation produces distinct changes in the cellular messenger RNA pool, which is consistent with a unique tumorigenesis pathway.

Adaptor Proteins, Signal Transducing↗
PubMed2003

An information theoretic approach for analyzing temporal patterns of gene expression.

MOTIVATION: Arrays allow measurements of the expression levels of thousands of mRNAs to be made simultaneously. The resulting data sets are information rich but require extensive mining to enhance their usefulness. Information theoretic methods are capable of assessing similarities and dissimilarities between data distributions and may be suited to the analysis of gene expression experiments. The purpose of this study was to investigate information theoretic data mining approaches to discover temporal patterns of gene expression from array-derived gene expression data. RESULTS: The Kullback-Leibler divergence, an information-theoretic distance that measures the relative dissimilarity between two data distribution profiles, was used in conjunction with an unsupervised self-organizing map algorithm. Two published, array-derived gene expression data sets were analyzed. The patterns obtained with the KL clustering method were found to be superior to those obtained with the hierarchical clustering algorithm using the Pearson correlation distance measure. The biological significance of the results was also examined. AVAILABILITY: Software code is available by request from the authors. All programs were written in ANSI C and Matlab (Mathworks Inc., Natick, MA).

Algorithms↗
PubMed2006

Multivariate information bottleneck.

The information bottleneck (IB) method is an unsupervised model independent data organization technique. Given a joint distribution, p(X, Y), this method constructs a new variable, T, that extracts partitions, or clusters, over the values of X that are informative about Y. Algorithms that are motivated by the IB method have already been applied to text classification, gene expression, neural code, and spectral analysis. Here, we introduce a general principled framework for multivariate extensions of the IB method. This allows us to consider multiple systems of data partitions that are interrelated. Our approach utilizes Bayesian networks for specifying the systems of clusters and which information terms should be maintained. We show that this construction provides insights about bottleneck variations and enables us to characterize the solutions of these variations. We also present four different algorithmic approaches that allow us to construct solutions in practice and apply them to several real-world problems.

Algorithms↗
PubMed2005

Proteomic signatures for histological types of lung cancer.

We performed proteomic studies on lung cancer cells to elucidate the mechanisms that determine histological phenotype. Thirty lung cancer cell lines with three different histological backgrounds (squamous cell carcinoma, small cell lung carcinoma and adenocarcinoma) were subjected to two-dimensional difference gel electrophoresis (2-D DIGE) and grouped by multivariate analyses on the basis of their protein expression profiles. 2-D DIGE achieves more accurate quantification of protein expression by using highly sensitive fluorescence dyes to label the cysteine residues of proteins prior to two-dimensional polyacrylamide gel electrophoresis. We found that hierarchical clustering analysis and principal component analysis divided the cell lines according to their original histology. Spot ranking analysis using a support vector machine algorithm and unsupervised classification methods identified 32 protein spots essential for the classification. The proteins corresponding to the spots were identified by mass spectrometry. Next, lung cancer cells isolated from tumor tissue by laser microdissection were classified on the basis of the expression pattern of these 32 protein spots. Based on the expression profile of the 32 spots, the isolated cancer cells were categorized into three histological groups: the squamous cell carcinoma group, the adenocarcinoma group, and a group of carcinomas with other histological types. In conclusion, our results demonstrate the utility of quantitative proteomic analysis for molecular diagnosis and classification of lung cancer cells.

Adenocarcinoma↗
PubMed2004

Automated morphometric analysis of the cat retinal alpha/Y, beta/X and delta ganglion cells using wavelet statistical moment and clustering algorithms.

Computational morphological analysis comprises the development of measures (indicators) that describe different form attributes of a neuron and provides additional parameters for classification algorithms. Our work addressed the problem of small group sizes often encountered in neuromorphological and neurophysiological research, automated classification tasks (unsupervised learning) and introduced a new morphological measure: the wavelet statistical moment. We analysed cat alpha/Y, beta/X and delta Golgi-stained retinal ganglion cells using six different shape features (circularity, 2(nd) statistical moment and entropy of Gaussian blurred images, wavelet statistical moment, number of terminations and the fractal dimension). This allowed us to compare the sensitivity of the methods in uniquely describing morphological attributes of these cells.

Algorithms↗
PubMed2005

Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma.

We describe a novel strategy (random forest clustering) for tumor profiling based on tissue microarray data. Random forest clustering is attractive for tissue microarray and other immunohistochemistry data since it handles highly skewed tumor marker expressions well and weighs the contribution of each marker according to its relatedness with other tumor markers. This is the first tumor class discovery analysis of renal cell carcinoma patients based on protein expression profiles. The tissue array data contained at least three tumor samples from each of 366 renal cell carcinoma patients. The eight tumor markers explore tumor proliferation, cell cycle abnormalities, cell mobility, and the hypoxia pathway. Since the procedure is unsupervised, no clinicopathological data or traditional classifications are used a priori. To explore whether the tissue microarray data can be used to identify fundamental subtypes of renal cell carcinoma patients, we first carried out random forest clustering of all 366 patients. By analyzing the tumor markers simultaneously, the procedure automatically detected classes that correspond to clear- vs non-clear cell tumors (demonstration of proof-of-principle). The resulting molecular grouping provides better prediction of survival (logrank P=0.000090) than this classical pathological grouping (logrank P=0.023). We then sought to extend the class discovery by searching for finer subclasses of clear cell patients. The procedure automatically discovered: (a) two classes corresponding to low- and high-grade patients (demonstration of proof-of-principle); (b) a subgroup of long-surviving clear cell patients with a distinct molecular profile and (c) two novel tumor subclasses in low-grade clear cell patients that could not be explained by any clinicopathological variables (demonstration of discovery).

Biomarkers, Tumor↗
PubMed2005

Improving molecular cancer class discovery through sparse non-negative matrix factorization.

MOTIVATION: Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over other clustering techniques, such as hierarchical clustering or self-organizing maps. In this paper, we investigate the benefit of explicitly enforcing sparseness in the factorization process. RESULTS: We report an improved unsupervised method for cancer classification by the use of gene-expression profile via sparse non-negative matrix factorization. We demonstrate the improvement by direct comparison with classic non-negative matrix factorization on the three well-studied datasets. In addition, we illustrate how to identify a small subset of co-expressed genes that may be directly involved in cancer.

Algorithms↗
PubMedDate not supplied

The latent process decomposition of cDNA microarray data sets.

We present a new computational technique (a software implementation, data sets, and supplementary information are available at http://www.enm.bris.ac.uk/lpd/) which enables the probabilistic analysis of cDNA microarray data and we demonstrate its effectiveness in identifying features of biomedical importance. A hierarchical Bayesian model, called Latent Process Decomposition (LPD), is introduced in which each sample in the data set is represented as a combinatorial mixture over a finite set of latent processes, which are expected to correspond to biological processes. Parameters in the model are estimated using efficient variational methods. This type of probabilistic model is most appropriate for the interpretation of measurement data generated by cDNA microarray technology. For determining informative substructure in such data sets, the proposed model has several important advantages over the standard use of dendrograms. First, the ability to objectively assess the optimal number of sample clusters. Second, the ability to represent samples and gene expression levels using a common set of latent variables (dendrograms cluster samples and gene expression values separately which amounts to two distinct reduced space representations). Third, in constrast to standard cluster models, observations are not assigned to a single cluster and, thus, for example, gene expression levels are modeled via combinations of the latent processes identified by the algorithm. We show this new method compares favorably with alternative cluster analysis methods. To illustrate its potential, we apply the proposed technique to several microarray data sets for cancer. For these data sets it successfully decomposes the data into known subtypes and indicates possible further taxonomic subdivision in addition to highlighting, in a wholly unsupervised manner, the importance of certain genes which are known to be medically significant. To illustrate its wider applicability, we also illustrate its performance on a microarray data set for yeast.

Algorithms↗
PubMed2005

Unsupervised motion-compensation of multi-slice cardiac perfusion MRI.

This paper presents a novel method for registration of single and multi-slice cardiac perfusion MRI. Utilising off-line computer intensive analyses of variance and clustering in an annotated training set, the presented method is capable of providing registration without any manual interaction in less than a second per frame. Changes in image intensity during the bolus passage are modelled by a slice-coupled active appearance model, which is augmented with a cluster analysis of the training set. Landmark correspondences are optimised using the MDL framework due to Davies et al. Image search is verified and stabilised using perfusion specific prior models of pose and shape estimated from training data. Qualitative and quantitative validation of the method is carried out using 2000 clinical quality, short-axis, perfusion MR slice images, acquired from 10 freely breathing patients with acute myocardial infarction. Despite evident perfusion deficits and varying image quality in the limited training set, a leave-one-out cross-validation of the method showed a mean point to curve distance of 1.25+/-0.36 pixels for the left and right ventricle combined. We conclude that this learning-based method holds great promise for the automation of cardiac perfusion investigations, due to its accuracy, robustness and generalisation ability.

Artifacts↗
PubMed2006

Template-driven gene selection procedure.

The hierarchical clustering and statistical techniques usually used to analyse microarray data do not inherently represent the underlying biology. Herein, a hybrid approach involving characteristics of both supervised and unsupervised learning is presented. This approach is based on template matching in which the interaction of the variables of inherent malignancy and the ability to express the malignant phenotype are modelled. Immortalised normal urothelial cells and bladder cancer cells of different malignancy were grown in conventional two-dimensional tissue culture and in three dimensions on extracellular matrices (ECMs) that were either permissive or restrictive for expression of the malignant phenotype. The transcriptome represents the effects of two variables--inherent malignancy and the modulatory effect of ECM. By assigning values to each of the biological variables of inherent malignancy and the ability to express the malignant phenotype, a template was constructed, which encapsulated the interaction between them. Gene expression correlating both positively and negatively with the template was observed, but when iterative correlations were carried out, the different models for the template converged on the same actual template. A subset of 21 genes was identified, which correlated with two a priori models or an optimised model above the 95% confidence limits identified in a bootstrap resampling with 5000 permutations of the data set. The correlation coefficients of expression of several genes were > 0.8. Analysis of upstream transcriptional regulatory elements (TREs) confirmed that these genes were not a randomly selected set of genes. Several TREs were identified as significantly over-expressed in the sample of 20 genes for which TREs were identified, and the high correlations of several genes were consistent with transcriptional co-regulation. The authors suggest that the template method can be used to identify a unique set of genes for further investigation.

Algorithms↗
PubMed2004

SUSTAIN: a network model of category learning.

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

Humans↗
PubMed2003

Overexpression of the EGFR/FKBP12/HIF-2alpha pathway identified in childhood astrocytomas by angiogenesis gene profiling.

Intense angiogenesis proliferation, a histopathological hallmark distinguishing malignant from benign astrocytoma, is vital for tumor progression. Thus, identifying and targeting specific pathways that promote malignant astrocytoma-induced angiogenesis could have substantial therapeutic benefit. Expression profiling of 13 childhood astrocytomas to determine the expression pattern of 133 angiogenesis-related genes revealed that 44 (33%) genes were differentially expressed (17 were overexpressed, and 27 were underexpressed) between malignant high-grade astrocytomas (HGAs) and benign low-grade astrocytomas. Hierarchical clustering and principal components analysis using only the 133 angiogenesis-related genes distinguished HGA from low-grade astrocytoma in 100% of the samples analyzed, as did unsupervised analyses using the entire set of 9198 expressed genes represented on the array, indicating that the angiogenesis-related genes were reliable markers of pathological grade. A striking new finding was significant overexpression of hypoxia-inducible transcription factor (HIF)-2alpha as well as high-level expression of FK506-binding protein (FKBP) 12 by HGA. Furthermore, 9 of 21 (43%) genes overexpressed by HGA were HIF/FKBP-associated genes. This group included the epidermal growth factor receptor (EGFR), which promotes HIF synthesis, as well as insulin-like growth factor-binding protein 2 (IGFBP2), a target gene of HIF activity. Differential protein expression of HIF-2alpha was validated in an independent group of 16 astrocytomas (P = 0.02). We conclude that the EGFR/FKBP12/HIF-2alpha pathway is important in childhood HGA and represents a potential new therapeutic target.

Astrocytoma↗
PubMed2005

Dynamic Trees for unsupervised segmentation and matching of image regions.

We present a probabilistic framework--namely, multiscale generative models known as Dynamic Trees (DT)--for unsupervised image segmentation and subsequent matching of segmented regions in a given set of images. Beyond these novel applications of DTs, we propose important additions for this modeling paradigm. First, we introduce a novel DT architecture, where multilayered observable data are incorporated at all scales of the model. Second, we derive a novel probabilistic inference algorithm for DTs--Structured Variational Approximation (SVA)--which explicitly accounts for the statistical dependence of node positions and model structure in the approximate posterior distribution, thereby relaxing poorly justified independence assumptions in previous work. Finally, we propose a similarity measure for matching dynamic-tree models, representing segmented image regions, across images. Our results for several data sets show that DTs are capable of capturing important component-subcomponent relationships among objects and their parts, and that DTs perform well in segmenting images into plausible pixel clusters. We demonstrate the significantly improved properties of the SVA algorithm--both in terms of substantially faster convergence rates and larger approximate posteriors for the inferred models--when compared with competing inference algorithms. Furthermore, results on unsupervised object recognition demonstrate the viability of the proposed similarity measure for matching dynamic-structure statistical models.

Algorithms↗