Distance measures for image segmentation evaluation

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.

Classification Using Generalized Partial Least Squares

The advances in computational biology have made simultaneous monitoring of thousands of features possible. The high throughput technologies not only bring about a much richer information context in which to study various aspects of gene functions but they also present challenge of analyzing data with large number of covariates and few samples. As an integral part of machine learning, classification of samples into two or more categories is almost always of interest to scientists. In this paper, we address the question of classification in this setting by extending partial least squares (PLS), a popular dimension reduction tool in chemometrics, in the context of generalized linear regression based on a previous approach, Iteratively ReWeighted Partial Least Squares, i.e. IRWPLS (Marx, 1996). We compare our results with two-stage PLS (Nguyen and Rocke, 2002A; Nguyen and Rocke, 2002B) and other classifiers. We show that by phrasing the problem in a generalized linear model setting and by applying bias correction to the likelihood to avoid (quasi)separation, we often get lower classification error rates.

Active caspase 3 and DNA fragmentation as markers for apoptotic cell death in primary and metastatic liver tumours

AIMS: The induction of tumour cell death by apoptosis is a major goal of cancer therapy and the in situ detection of apoptosis in tumour tissue has become an important diagnostic parameter. Different apoptosis detection methods assess distinct biochemical processes in the dying cell. Thus, their direct comparison is mandatory to evaluate their diagnostic value. The aim of this study was to compare the immunohistochemical detection of active caspase 3 and single-stranded DNA in primary and metastatic liver tumours as markers of apoptotic cell death. METHODS: We studied detection of active caspase 3 and single-stranded DNA in 20 primary hepatocellular carcinomas (HCC) and 20 liver metastases from colorectal carcinomas (CRC) using immunohistochemistry on paraffin sections. RESULTS: Our results reveal that both methods are suitable and sensitive techniques for the in situ detection of apoptosis, however, they also demonstrate that immunohistochemistry for active caspase 3 and single-stranded DNA have differential sensitivities in HCC and CRC. CONCLUSION: The sensitivity of apoptosis detection using immunohistochemistry for active caspase 3 and single-stranded DNA may be tumour cell type dependent.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

DISSECTION OF STRESS RESPONSE NETWORKS REGULATING MULTIPLE STRESSES IN RICE

Important food crops like rice are constantly exposed to various stresses that can have devastating effect on their survival and productivity. Being sessile, these highly evolved organisms have developed elaborate molecular machineries to sense a mixture of stress signals and elicit a precise response to minimize the damage. However, recent discoveries revealed that the interplay of these stress regulatory and signaling molecules is highly complex and remains largely unknown. In this work, we conducted large scale analysis of differential gene expression using advanced computational methods to dissect regulation of stress response which is at the heart of all molecular changes leading to the observed phenotypic susceptibility. One of the most important stress conditions in terms of loss of productivity is drought. We performed genomic and proteomic analysis of epigenetic and miRNA mechanisms in regulation of drought responsive genes in rice and found subsets of genes with striking properties. Overexpressed genesets included higher number of epigenetic marks, miRNA targets and transcription factors which regulate drought tolerance. On the other hand, underexpressed genesets were poor in above features but were rich in number of metabolic genes with multiple co-expression partners contributing majorly towards drought resistance. Identification and characterization of the patterns exhibited by differentially expressed genes hold key to uncover the synergistic and antagonistic components of the cross talk between stress response mechanisms. We performed meta-analysis on drought and bacterial stresses in rice and Arabidopsis, and identified hundreds of shared genes. We found high level of conservation of gene expression between these stresses. Weighted co-expression network analysis detected two tight clusters of genes made up of master transcription factors and signaling genes showing strikingly opposite expression status. To comprehensively identify the shared stress responsive genes between multiple abiotic and biotic stresses in rice, we performed meta-analyses of microarray studies from seven different abiotic and six biotic stresses separately and found more than thirteen hundred shared stress responsive genes. Various machine learning techniques utilizing these genes classified the stresses into two major classes' namely abiotic and biotic stresses and multiple classes of individual stresses with high accuracy and identified the top genes showing distinct patterns of expression. Functional enrichment and co-expression network analysis revealed the different roles of plant hormones, transcription factors in conserved and non-conserved genesets in regulation of stress response.

Ethanol-induced cytochrome P4502E1 causes carcinogenic etheno-DNA lesions in alcoholic liver disease

Oxidative stress is thought to play a major role in the pathogenesis of hepatocellular cancer (HCC), a frequent complication of alcoholic liver disease (ALD). However, the underlying mechanisms are poorly understood. In hepatocytes of ALD patients, we recently detected by immunohistochemistry significantly increased levels of carcinogenic etheno-DNA adducts that are formed by the reaction of the major lipid peroxidation product, 4-hydroxynonenal (4-HNE) with nucleobases. In the current study, we show that protein-bound 4-HNE and etheno-DNA adducts both strongly correlate with cytochrome P450 2E1 (CYP2E1) expression in patients with ALD (r = 0.9, P < 0.01). Increased levels of etheno-DNA adducts were also detected in the liver of alcohol-fed lean (Fa/?) and obese (fa/fa) Zucker rats. The number of nuclei in hepatocytes stained positively for etheno-DNA adducts correlated significantly with CYP2E1 expression (r = 0.6, P = 0.03). To further assess the role of CYP2E1 in the formation of etheno-DNA adducts, HepG2 cells stably transfected with human CYP2E1 were exposed to ethanol with or without chlormethiazole (CMZ), a specific CYP2E1 inhibitor. Ethanol increased etheno-DNA adducts in the nuclei of CYP2E1-transfected HepG2 cells in a concentration-dependent and time-dependent manner, but not in vector mock-transfected control cells. CMZ blocked the generation of etheno-DNA adducts by 70%-90% (P < 0.01). CONCLUSION: Our data support the assumption that ethanol-mediated induction of hepatic CYP2E1 leading inter alia to highly miscoding lipid peroxidation-derived DNA lesions may play a central role in hepatocarcinogenesis in patients with ALD.

Human urinary metabolomic profile of PPARalpha induced fatty acid beta-oxidation

Activation of the peroxisome proliferator-activated receptor alpha (PPARalpha) is associated with increased fatty acid catabolism and is commonly targeted for the treatment of hyperlipidemia. To identify latent, endogenous biomarkers of PPARalpha activation and hence increased fatty acid beta-oxidation, healthy human volunteers were given fenofibrate orally for 2 weeks and their urine was profiled by UPLC-QTOFMS. Biomarkers identified by the machine learning algorithm random forests included significant depletion by day 14 of both pantothenic acid (>5-fold) and acetylcarnitine (>20-fold), observations that are consistent with known targets of PPARalpha including pantothenate kinase and genes encoding proteins involved in the transport and synthesis of acylcarnitines. It was also concluded that serum cholesterol (-12.7%), triglycerides (-25.6%), uric acid (-34.7%), together with urinary propylcarnitine (>10-fold), isobutyrylcarnitine (>2.5-fold), (S)-(+)-2-methylbutyrylcarnitine (5-fold), and isovalerylcarnitine (>5-fold) were all reduced by day 14. Specificity of these biomarkers as indicators of PPARalpha activation was demonstrated using the Ppara-null mouse. Urinary pantothenic acid and acylcarnitines may prove useful indicators of PPARalpha-induced fatty acid beta-oxidation in humans. This study illustrates the utility of a pharmacometabolomic approach to understand drug effects on lipid metabolism in both human populations and in inbred mouse models.

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.

NEW TARGET GENES FOR TUMOR SUPPRESSORS p53 AND p73 IN REGENERATING LIVER

The p53-family of proteins regulates expression of target genes during tissue development and differentiation. Within the p53-family, p53 and p73 have hepatic-specific functions in development and tumor suppression. Despite a growing list of p53/p73 target genes, very few of these have been studied in vivo, and the knowledge regarding functions of p53 and p73 in normal tissues remains limited. p53+/-p73+/- mice develop hepatocellular carcinoma (HCC), whereas overexpression of p53 in human HCC leads to tumor regression. However, the mechanism of p53/p73 function in liver remains poorly characterized. Here, the model of mouse liver regeneration is used to identify new target genes for p53/p73 in normal quiescent vs. proliferating cells. In response to surgical removal of ~2/3 of liver mass (partial hepatectomy, PH), the remaining hepatocytes exit G0 of cell cycle and undergo proliferation to reestablish liver mass. The hypothesis tested in this work is that p53/p73 functions in cell cycle arrest, apoptosis and senescence are repressed during liver regeneration, and reactivated at the end of the regenerative response. Chromatin immunoprecipitation (ChIP), with a p73-antibody, was used to probe arrayed genomic sequences (ChIP-chip) and uncover 158 potential targets of p73-regulation in normal liver. Global microarray analysis of mRNA levels, at T=0-48h following PH, revealed sets of genes that change expression during regeneration. Eighteen p73-bound genes changed expression after PH. Four of these genes, Foxo3, Jak1, Pea15, and Tuba1 have p53 response elements (p53REs), identified in silico within the upstream regulatory region. Forkhead transcription factor Foxo3 is the most responsive gene among transcription factors with altered expression during regenerative, cellular proliferation. p53 and p73 bind a Foxo3 p53RE and maintain active expression in quiescent liver. During liver regeneration, binding of p53 and p73, recruitment of acetyltransferase p300, and an active chromatin structure of Foxo3 are disrupted, alongside loss of Foxo3 expression. These parameters of Foxo3 regulation are reestablished at completion of liver growth and regeneration, supporting a temporary suspension of p53 and p73 regulatory functions in normal cells during tissue regeneration.

Using citation data to improve retrieval from MEDLINE.

OBJECTIVE: To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. The most effective strategies were simple citation count and PageRank, which on average identified over six important articles in the first 100 results compared to 0.85 for the best noncitation-based algorithm (p < 0.001). The authors saw similar differences between citation-based and noncitation-based algorithms at 10, 20, 50, 200, 500, and 1,000 results (p < 0.001). Citation lag affects performance of PageRank more than simple citation count. However, in spite of citation lag, citation-based algorithms remain more effective than noncitation-based algorithms. CONCLUSION Algorithms that have proved successful on the World Wide Web can be applied to biomedical information retrieval. Citation-based algorithms can help identify important articles within large sets of relevant results. Further studies are needed to determine whether citation-based algorithms can effectively meet actual user information needs.

Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.