Gregor Leban

Orange: from experimental machine learning to interactive data mining

Orange (www.ailab.si/orange) is a suite for machine learning and data mining. For researchers in machine learning, Orange offers scripting to easily prototype new algorithms and experimental procedures. For explorative data analysis, it provides a visual programming framework with emphasis on interactions and creative combinations of visual components.

Microarray data mining with visual programming

UNLABELLED Visual programming offers an intuitive means of combining known analysis and visualization methods into powerful applications. The system presented here enables users who are not programmers to manage microarray and genomic data flow and to customize their analyses by combining common data analysis tools to fit their needs. AVAILABILITY http://www.ailab.si/supp/bi-visprog SUPPLEMENTARY INFORMATION http://www.ailab.si/supp/bi-visprog.

VizRank: Data Visualization Guided by Machine Learning

Data visualization plays a crucial role in identifying interesting patterns in exploratory data analysis. Its use is, however, made difficult by the large number of possible data projections showing different attribute subsets that must be evaluated by the data analyst. In this paper, we introduce a method called VizRank, which is applied on classified data to automatically select the most useful data projections. VizRank can be used with any visualization method that maps attribute values to points in a two-dimensional visualization space. It assesses possible data projections and ranks them by their ability to visually discriminate between classes. The quality of class separation is estimated by computing the predictive accuracy of k-nearest neighbor classifier on the data set consisting of x and y positions of the projected data points and their class information. The paper introduces the method and presents experimental results which show that VizRanks ranking of projections highly agrees with subjective rankings by data analysts. The practical use of VizRank is also demonstrated by an application in the field of functional genomics.

VizRank: finding informative data projections in functional genomics by machine learning

UNLABELLED VizRank is a tool that finds interesting two-dimensional projections of class-labeled data. When applied to multi-dimensional functional genomics datasets, VizRank can systematically find relevant biological patterns. AVAILABILITY http://www.ailab.si/supp/bi-vizrank SUPPLEMENTARY INFORMATION http://www.ailab.si/supp/bi-vizrank.

FreeViz-An intelligent multivariate visualization approach to explorative analysis of biomedical data

Visualization can largely improve biomedical data analysis. It plays a crucial role in explorative data analysis and may support various data mining tasks. The paper presents FreeViz, an optimization method that finds linear projection and associated scatterplot that best separates instances of different class. In a single graph, the resulting FreeViz visualization can provide a global view of the classification problem being studied, reveal interesting relations between classes and features, uncover feature interactions, and provide information about intra-class similarities. The paper gives mathematical foundations of FreeViz, and presents its utility on various biomedical data sets.

Conquering the curse of dimensionality in gene expression cancer diagnosis: tough problem, simple models

In the paper we study the properties of cancer gene expression data sets from the perspective of classification and tumor diagnosis. Our findings and case studies are based on several recently published data sets. We find that these data sets typically include a subset of about 100 highly discriminating features of which predictive power can be further enhanced by exploring their interactions. This finding speaks against often used univariate feature selection methods, and may explain the superior performance of support vector machines recently reported in the related work. We argue that a much simpler technique that directly finds visualizations with clear separation of diagnostic classes may be used instead. Furthermore, it may perform better in inference of an understandable classifier that includes only a few relevant features.

Simple and effective visual models for gene expression cancer diagnostics

In the paper we show that diagnostic classes in cancer gene expression data sets, which most often include thousands of features (genes), may be effectively separated with simple two-dimensional plots such as scatterplot and radviz graph. The principal innovation proposed in the paper is a method called VizRank, which is able to score and identify the best among possibly millions of candidate projections for visualizations. Compared to recently much applied techniques in the field of cancer genomics that include neural networks, support vector machines and various ensemble-based approaches, VizRank is fast and finds visualization models that can be easily examined and interpreted by domain experts. Our experiments on a number of gene expression data sets show that VizRank was always able to find data visualizations with a small number of (two to seven) genes and excellent class separation. In addition to providing grounds for gene expression cancer diagnosis, VizRank and its visualizations also identify small sets of relevant genes, uncover interesting gene interactions and point to outliers and potential misclassifications in cancer data sets.