Isabel A. Nepomuceno-Chamorro

Inferring gene regression networks with model trees

BackgroundNovel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities.ResultsWe propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REG NET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REG NET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods.ConclusionsREG NET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REG NET.

Discovering gene association networks by multi-objective evolutionary quantitative association rules

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge.

Prognostic transcriptional association networks

Motivation: The application of information encoded in molecular networks for prognostic purposes is a crucial objective of systems biomedicine. This approach has not been widely investigated in the cardiovascular research area. Within this area, the prediction of clinical outcomes after suffering a heart attack would represent a significant step forward. We developed a new quantitative prediction-based method for this prognostic problem based on the discovery of clinically relevant transcriptional association networks. This method integrates regression trees and clinical class-specific networks, and can be applied to other clinical domains. Results: Before analyzing our cardiovascular disease dataset, we tested the usefulness of our approach on a benchmark dataset with control and disease patients. We also compared it to several algorithms to infer transcriptional association networks and classification models. Comparative results provided evidence of the prediction power of our approach. Next, we discovered new models for predicting good and bad outcomes after myocardial infarction. Using blood-derived gene expression data, our models reported areas under the receiver operating characteristic curve above 0.70. Our model could also outperform different techniques based on co-expressed gene modules. We also predicted processes that may represent novel therapeutic targets for heart disease, such as the synthesis of leucine and isoleucine. Availability: The SATuRNo software is freely available at http://www.lsi.us.es/isanepo/toolsSaturno/. Contact: inepomuceno@us.es Supplementary information: Supplementary data are available at Bioinformatics online.

Integrating biological knowledge based on functional annotations for biclustering of gene expression data

Gene expression data analysis is based on the assumption that co-expressed genes imply co-regulated genes. This assumption is being reformulated because the co-expression of a group of genes may be the result of an independent activation with respect to the same experimental condition and not due to the same regulatory regime. For this reason, traditional techniques are recently being improved with the use of prior biological knowledge from open-access repositories together with gene expression data. Biclustering is an unsupervised machine learning technique that searches patterns in gene expression data matrices. A scatter search-based biclustering algorithm that integrates biological information is proposed in this paper. In addition to the gene expression data matrix, the input of the algorithm is only a direct annotation file that relates each gene to a set of terms from a biological repository where genes are annotated. Two different biological measures, FracGO and SimNTO, are proposed to integrate this information by means of its addition to-be-optimized fitness function in the scatter search scheme. The measure FracGO is based on the biological enrichment and SimNTO is based on the overlapping among GO annotations of pairs of genes. Experimental results evaluate the proposed algorithm for two datasets and show the algorithm performs better when biological knowledge is integrated. Moreover, the analysis and comparison between the two different biological measures is presented and it is concluded that the differences depend on both the data source and how the annotation file has been built in the case GO is used. It is also shown that the proposed algorithm obtains a greater number of enriched biclusters than other classical biclustering algorithms typically used as benchmark and an analysis of the overlapping among biclusters reveals that the biclusters obtained present a low overlapping. The proposed methodology is a general-purpose algorithm which allows the integration of biological information from several sources and can be extended to other biclustering algorithms based on the optimization of a merit function.

CarGene: Characterisation of sets of genes based on metabolic pathways analysis

The great amount of biological information provides scientists with an incomparable framework for testing the results of new algorithms. Several tools have been developed for analysing gene-enrichment and most of them are Gene Ontology-based tools. We developed a Kyoto Encyclopedia of Genes and Genomes (Kegg)-based tool that provides a friendly graphical environment for analysing gene-enrichment. The tool integrates two statistical corrections and simultaneously analysing the information about many groups of genes in both visual and textual manner. We tested the usefulness of our approach on a previous analysis (Huttenshower et al.). Furthermore, our tool is freely available (http://www.upo.es/eps/bigs/cargene.html).

Inferring gene-gene associations from Quantitative Association Rules

The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of genes to be analyzed in relation to the low number of experiments or samples available. Microarray experiments are generating datasets that can help in reconstructing gene networks. One of the most important problems in network reconstruction is finding, for each gene in the network, which genes can affect it and how. Association Rules are an approach of unsupervised learning to relate attributes to each other. In this work we use Quantitative Association Rules in order to define interrelations between genes. These rules work with intervals on the attributes, without discretizing the data before and they are generated by a multi-objective evolutionary algorithm. In most cases the extracted rules confirm the existing knowledge about cell-cycle gene expression, while hitherto unknown relationships can be treated as new hypotheses.

Machine learning techniques to discover genes with potential prognosis role in Alzheimer’s disease using different biological sources

Abstract Alzheimer’s disease is a complex progressive neurodegenerative brain disorder, being its prevalence expected to rise over the next decades. Unconventional strategies for elucidating the genetic mechanisms are necessary due to its polygenic nature. In this work, the input information sources are five: a public DNA microarray that measures expression levels of control and patient samples, repositories of known genes associated to Alzheimer’s disease, additional data, Gene Ontology and finally, a literature review or expert knowledge to validate the results. As methodology to identify genes highly related to this disease, we present the integration of three machine learning techniques: particularly, we have used decision trees, quantitative association rules and hierarchical cluster to analyze Alzheimer’s disease gene expression profiles to identify genes highly linked to this neurodegenerative disease, through changes in their expression levels between control and patient samples. We propose an ensemble of decision trees and quantitative association rules to find the most suitable configurations of the multi-objective evolutionary algorithm GarNet, in order to overcome the complex parametrization intrinsic to this type of algorithms. To fulfill this goal, GarNet has been executed using multiple configuration settings and the well-known C4.5 has been used to find the minimum accuracy to be satisfied. Then, GarNet is rerun to identify dependencies between genes and their expression levels, so we are able to distinguish between healthy individuals and Alzheimer’s patients using the configurations that overcome the minimum threshold of accuracy defined by C4.5 algorithm. Finally, a hierarchical cluster analysis has been used to validate the obtained gene-Alzheimer’s Disease associations provided by GarNet. The results have shown that the obtained rules were able to successfully characterize the underlying information, grouping relevant genes for Alzheimer Disease. The genes reported by our approach provided two well defined groups that perfectly divided the samples between healthy and Alzheimer’s Disease patients. To prove the relevance of the obtained results, a statistical test and gene expression fold-change were used. Furthermore, this relevance has been summarized in a volcano plot, showing two clearly separated and significant groups of genes that are up or down-regulated in Alzheimer’s Disease patients. A biological knowledge integration phase was performed based on the information fusion of systematic literature review, enrichment Gene Ontology terms for the described genes found in the hippocampus of patients. Finally, a validation phase with additional data and a permutation test is carried out, being the results consistent with previous studies.

Biclustering of Gene Expression Data Based on SimUI Semantic Similarity Measure

Biclustering is an unsupervised machine learning technique that simultaneously clusters genes and conditions in gene expression data. Gene Ontology (GO) is usually used in this context to validate the biological relevance of the results. However, although the integration of biological information from different sources is one of the research directions in Bioinformatics, GO is not used in biclustering as an input data. A scatter search-based algorithm that integrates GO information during the biclustering search process is presented in this paper. SimUI is a GO semantic similarity measure that defines a distance between two genes. The algorithm optimizes a fitness function that uses SimUI to integrate the biological information stored in GO. Experimental results analyze the effect of integration of the biological information through this measure. A SimUI fitness function configuration is experimentally studied in a scatter search-based biclustering algorithm.

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

BackgroundBiclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each pair of genes which establishes their functional similarity. A scatter search-based algorithm that optimizes a merit function that integrates GO information is studied in this paper. This merit function uses a term that addresses the information through a GO measure.ResultsThe effect of two possible different gene pairwise GO measures on the performance of the algorithm is analyzed. Firstly, three well known yeast datasets with approximately one thousand of genes are studied. Secondly, a group of human datasets related to clinical data of cancer is also explored by the algorithm. Most of these data are high-dimensional datasets composed of a huge number of genes. The resultant biclusters reveal groups of genes linked by a same functionality when the search procedure is driven by one of the proposed GO measures. Furthermore, a qualitative biological study of a group of biclusters show their relevance from a cancer disease perspective.ConclusionsIt can be concluded that the integration of biological information improves the performance of the biclustering process. The two different GO measures studied show an improvement in the results obtained for the yeast dataset. However, if datasets are composed of a huge number of genes, only one of them really improves the algorithm performance. This second case constitutes a clear option to explore interesting datasets from a clinical point of view.

Data Mining Techniques Applied to Hydrogen Lactose Breath Test

In this work, we present the results of applying data mining techniques to hydrogen breath test data. Disposal of H2 gas is of utmost relevance to maintain efficient microbial fermentation processes. Objectives Analyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production. Methods Hydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients. Results Six different patterns have been extracted upon analysis of the hydrogen breath test data. We have also shown the relevance of each of the samples taken throughout the test. Conclusions Analysis of the hydrogen breath test data sets using data mining techniques has identified new patterns of hydrogen generation upon lactose absorption. We can see the potential of application of data mining techniques to clinical data sets. These results offer promising data for future research on the relations between gut microbiota produced hydrogen and its link to clinical symptoms.