Classification and biomarker identification using gene network modules and support vector machines

<p>Abstract</p> <p>Background</p> <p>Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.</p> <p>We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE)</p> <p>Results</p> <p>Initially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA) is applied to the remaining genes to form <it>n </it>clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained.</p> <p>Conclusion</p> <p>More than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays.</p> <p>The Matlab version of SVM-RNE can be downloaded from <url>http://web.macam.ac.il/~myousef</url></p

Combination Calculi for Uncertainty Reasoning: Representing Uncertainty Using Distributions

There are many different methods for incorporating notions of uncertainty in evidential reasoning. A common component to these methods is the use of additional values, other than conditional probabilities, to assert current degrees of belief and certainties in propositions. Beginning with the viewpoint that these values can be associated with statistics of multiple opinions in an evidential reasoning system, we categorize the choices that are available in updating and tracking these multiple opinions. In this way, we develop a matrix of different uncertainty calculi, some of which are standard, and others are new. The main contribution is to formalize a framework under which different methods for reasoning with uncertainty can be evaluated. As examples, we see that both the &quot;Kalman filtering&quot; approach and the &quot;Dempster-Shafer&quot; approach to reasoning with uncertainty can be interpreted within this framework of representing uncertainty by the statistics of multiple opinions

COLLABORATIVE FILTERING BASED ON CONTENT ADDRESSING

Abstract: Collaborative Filtering (CF) is one of the most popular recommendation techniques. It is based on the assumption that users with similar tastes prefer similar items. One of the major drawbacks of the CF is its limited scalability, as the complexity of the CF grows linearly both with the number of available users and items. This work proposes a new fast variant of the CF employed over multi-dimensional contentaddressable space. Our approach heuristically decreases the computational effort required by the CF algorithm by limiting the search process only to potentially similar users. Experimental results demonstrate that our approach is capable of generate recommendations with high levels of accuracy, while significantly improving performance in comparison with the traditional implementation of the CF.

Evaluation of User Model Effectiveness by Simulation

Abstract. Accurate initialization of a User Model (UM) is important for every system that provides personalized services. However, there are systems where this initialization is critical yet no user data is available from prior interactions. For example, one in which both the repeat usage by a user is rare, and the total interaction on a single use is relatively limited. Evaluating the effectiveness of a User Model and a particular instance of such a model is never an easy task, particularly through user studies. Moreover, evaluation typically focuses on the usability of an entire system rather than the performance of a specific UM instantiation. In this paper we propose evaluating the quality of UMs via simulation and comparison to a &amp;quot;gold standard&amp;quot;. This standard is an approximation of the user&apos;s ideal model. We will demonstrate this through a case study of a museum&apos;s visitor guide system implemented in the Hecht Museum at the University of Haifa, Israe