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The application of machine learning methods for the identification of candidate genes responsible for phenotypes of interest, such as cancer, is a major challenge in the field of bioinformatics. These lists of genes are often called genomic signatures and their linkage to phenotype associations may form a significant step in discovering the causation between genotypes and phenotypes. Traditional methods that produce genomic signatures from DNA Microarray data tend to extract significantly different lists under relatively small variations of the training data. That instability hinders the validity of research findings and raises skepticism about the reliability of such methods. In this study, a complete framework for the extraction of stable and reliable lists of candidate genes is presented. The proposed methodology enforces stability of results at the validation step and as a result, it is independent of the feature selection and classification methods used. Furthermore, two different statistical tests are performed in order to assess the statistical significance of the observed results. Moreover, the consistency of the signatures extracted by independent executions of the proposed method is also evaluated. The results of this study highlight the importance of stability issues in genomic signatures, beyond their prediction capabilities. [ABSTRACT FROM PUBLISHER]

Breast cancer is a highly heterogeneous disease and very common among western women. The main cause of death is not the primary tumor but its metastases at distant sites, such as lymph nodes and other organs (preferentially lung, liver, and bones). The study of circulating tumor cells (CTCs) in peripheral blood resulting from tumor cell invasion and intravascular filtration highlights their crucial role concerning tumor aggressiveness and metastasis. Genomic research regarding CTCs monitoring for breast cancer is limited due to the lack of indicative genes for their detection and isolation. Instead of direct CTC detection, in our study, we focus on the identification of factors in peripheral blood that can indirectly reveal the presence of such cells. Using selected publicly available breast cancer and peripheral blood microarray datasets, we follow a two-step elimination procedure for the identification of several discriminant factors. Our procedure facilitates the identification of major genes involved in breast cancer pathology, which are also indicative of CTCs presence. [ABSTRACT FROM PUBLISHER]

An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray platforms and analysis techniques. One challenging task is to develop robust statistical models in order to integrate multi-platform findings. We compare some methodologies on the field with respect to estrogen receptor (ER) status, and focus on a unified-among-platforms scale implemented by Shen et al. in 2004, which is based on a Bayesian mixture model. Under this scale, we study the ER intensity similarities between four breast cancer datasets derived from various platforms. We evaluate our results with an independent dataset in terms of ER sample classification, given the derived gene ER signatures of the integrated data. We found that integrated multi-platform gene signatures and fold-change variability similarities between different platform measurements can assist the statistical analysis of independent microarray datasets in terms of ER classification. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Gene expression patterns that distinguish clinically significant disease subclasses may not only play a prominent role in diagnosis, but also lead to the therapeutic strategies tailoring the treatment to the particular biology of each disease. Nevertheless, gene expression signatures derived through statistical feature-extraction procedures on population datasets have received rightful criticism, since they share few genes in common, even when derived from the same dataset. We focus on knowledge complementarities conveyed by two or more gene-expression signatures by means of embedded biological processes and pathways, which alternatively form a meta-knowledge platform of analysis towards a more global, robust and powerful solution. Methods: The main contribution of this work is the introduction and study of an approach for integrating different gene signatures based on the underlying biological knowledge, in an attempt to derive a unified global solution. It is further recognized that one group''s signature does not perform well on another group''s data, due to incompatibilities of microarray technologies and the experimental design. We assess this cross-platform aspect, showing that a unified solution derived on the basis of both statistical and biological validation may also help in overcoming such inconsistencies. Results: Based on the proposed approach we derived a unified 69-gene signature, which outperforms significantly the performance of the initial signatures succeeding a 0.73 accuracy metric on 234 new patients with 81% sensitivity and 64% specificity. The same signature manages to reveal the two prognostic groups on an additional dataset of 286 new patients obtained through a different experimental protocol and microarray platform. Furthermore, it manages to derive two clusters in a dataset from a different platform, showing remarkable difference on both gene-expression and survival-prediction levels. [Copyright &y& Elsevier]

The concept of gene signature overlap has been addressed previously in a number of research papers. A common conclusion is the absence of significant overlap. In this paper, we verify the aforementioned fact, but we also assess the issue of similarities not on the gene level, but on the biology level hidden underneath a given signature. We proceed by taking into account the biological knowledge that exists among different signatures, and use it as a means of integrating them and refining their statistical significance on the datasets. In this form, by integrating biological knowledge with information stemming from data distributions, we derive a unified signature that is significantly improved over its predecessors in terms of performance and robustness. Our motive behind this approach is to assess the problem of evaluating different signatures not in a competitive but rather in a complementary manner, where one is treated as a pool of knowledge contributing to a global and unified solution. [ABSTRACT FROM AUTHOR]

Abstract: Objective: The problem of marker selection in DNA microarray analysis has been addressed so far by two basic types of approaches, the so-called filter and wrapper methods. Wrapper methods operate in a recursive fashion where feature (gene) weights are re-evaluated and dynamically changing from iteration to iteration, while in filter methods feature weights remain fixed. Our objective in this study is to show that the application of filter criteria in a recursive fashion, where weights are potentially adjusted from cycle to cycle, produces noticeable improvement on the generalization performance measured on independent test sets. Methods and materials: Toward this direction we explore the behavior of two well known and broadly accepted pattern recognition approaches namely the support vector machines (SVM) and a single linear neuron (LN), properly adapted to the problem of marker selection. Within this context we also show how the kernel ability of SVM could be employed in a practical manner to provide alternative ways to approach the problem of reliable marker selection. Results: We explore how the proposed approaches behave in two application domains (breast cancer and leukemia), achieving comparable or even better results than those reported in the related bibliography. An important advantage of these approaches is their ability to derive stable performance without deteriorating due to the complexity of the application domain. Validation is performed using internal leave one out (ILOO) and 10-fold cross validation as well as independent test set evaluation. Conclusions: Results show that the proposed methodologies achieve remarkable performance and indicate that applying filter criteria in a wrapper fashion (‘wrapper filtering criteria’) provides a useful tool for marker selection. The contribution of this study is threefold. First it provides a methodology to apply filter criteria in a wrapper way (which is a new approach), second it introduces a fundamental pattern recognition component namely the single neuron (which is a linear estimator) and explores its behavior on marker selection and third it demonstrates an approach to exploit the kernel ability of SVMs in a practical and effective manner. [Copyright &y& Elsevier]