Patrick Hoffman

DNA visual and analytic data mining

Describes data exploration techniques designed to classify DNA sequences. Several visualization and data mining techniques were used to validate and attempt to discover new methods for distinguishing coding DNA sequences (exons) from non-coding DNA sequences (introns). The goal of the data mining was to see whether some other, possibly non-linear combination of the fundamental position-dependent DNA nucleotide frequency values could be a better predictor than the AMI (average mutual information). We tried many different classification techniques including rule-based classifiers and neural networks. We also used visualization of both the original data and the results of the data mining to help verify patterns and to understand the distinction between the different types of data and classifications. In particular, the visualization helped us develop refinements to neural network classifiers, which have accuracies as high as any known method. Finally, we discuss the interactions between visualization and data mining and suggest an integrated approach.

Dimensional anchors: a graphic primitive for multidimensional multivariate information visualizations

We introduce a graphic primitive, called a dimensional anchor (DA), which facilitates the creation of new visualizations and provides insight into the analysis of information visualizations. The DA represents an attempt to provide a unified framework or model for a variety of visualizations, including Parallel Coordinates, scatter plot matrices, Radviz, Survey Plots and Circle Segments A dimensional anchor is constructed by assigning values to parameters associated with various geometric graphic elements that encode the basics of the above visualizations. We define a visualization vector space in which all of the above visualizations and many new ones are represented by vectors. These encodings make it possible to perform a Grand Tour traveling from Parallel Coordinates to Survey Plot, and visiting many other visualizations in between

Applications of Machine Learning and High‐Dimensional Visualization in Cancer Detection, Diagnosis, and Management

Abstract: Recent technical advances in combinatorial chemistry, genomics, and proteomics have made available large databases of biological and chemical information that have the potential to dramatically improve our understanding of cancer biology at the molecular level. Such an understanding of cancer biology could have a substantial impact on how we detect, diagnose, and manage cancer cases in the clinical setting. One of the biggest challenges facing clinical oncologists is how to extract clinically useful knowledge from the overwhelming amount of raw molecular data that are currently available. In this paper, we discuss how the exploratory data analysis techniques of machine learning and high‐dimensional visualization can be applied to extract clinically useful knowledge from a heterogeneous assortment of molecular data. After an introductory overview of machine learning and visualization techniques, we describe two proprietary algorithms (PURS and RadViz™) that we have found to be useful in the exploratory analysis of large biological data sets. We next illustrate, by way of three examples, the applicability of these techniques to cancer detection, diagnosis, and management using three very different types of molecular data. We first discuss the use of our exploratory analysis techniques on proteomic mass spectroscopy data for the detection of ovarian cancer. Next, we discuss the diagnostic use of these techniques on gene expression data to differentiate between squamous and adenocarcinoma of the lung. Finally, we illustrate the use of such techniques in selecting from a database of chemical compounds those most effective in managing patients with melanoma versus leukemia.

Benchmark development for the evaluation of visualization for data mining

New sets of powerful data visualization tools have appeared in the marketplace and in the research community. This, combined with readily available computer memory, speed, and graphics capabilities, makes it possible to explore larger and larger data sets. However, it is difficult to judge the effectiveness of these tools for supporting large scale information exploration and knowledge discovery. In this paper, we describe a set of issues critical to benchmarking and evaluation in this domain. We then propose an approach to constructing an evaluation environment and report on initial results from a prototype environment in which we tested five visualization approaches against nine existing data sets.