A graph theoretic approach to testing associations between disparate sources of functional genomic data

The last few years have seen the advent of high-throughput technologies to analyze various properties of the transcriptome and proteome of several organisms. The congruency of these different data sources, or lack thereof, can shed light on the mechanisms that govern cellular function. A central challenge for bioinformatics research is to develop a unified framework for combining the multiple sources of functional genomics information and testing associations between them, thus obtaining a robust and integrated view of the underlying biology. We present a graph theoretic approach to test the significance of the association between multiple disparate sources of functional genomics data by proposing two statistical tests, namely edge permutation and node label permutation tests. We demonstrate the use of the proposed tests by finding significant association between a Gene Ontology-derived "predictome" and data obtained from mRNA expression and phenotypic experiments for Saccharomyces cerevisiae. Moreover, we employ the graph theoretic framework to recast a surprising discrepancy presented in Giaever et al. (2002) between gene expression and knockout phenotype, using expression data from a different set of experiments.

Confidence Bands for Convex Median Curves using Sign-Tests

Suppose that one observes pairs (x1,Y1), (x2,Y2), ..., (xn,Yn), where x1 < x2 < ... < xn are fixed numbers while Y1, Y2, ..., Yn are independent random variables with unknown distributions. The only assumption is that Median(Yi) = f(xi) for some unknown convex or concave function f. We present a confidence band for this regression function f using suitable multiscale sign tests. While the exact computation of this band seems to require O(n4) steps, good approximations can be obtained in O(n2) steps. In addition the confidence band is shown to have desirable asymptotic properties as the sample size n tends to infinity.

Ultrasound measurements of the distance between acupuncture needle tip at P6 and the median nerve

BACKGROUND: Pericard 6 (P6) is one of the most frequently used acupuncture points, especially in preventing nausea and vomiting. At this point, the median nerve is located very superficially. OBJECTIVES: To investigate the distance between the needle tip and the median nerve during acupuncture at P6, we conducted a prospective observational ultrasound (US) imaging study. We tested the hypothesis that de qi (a sensation that is typical of acupuncture needling) is evoked when the needle comes into contact with the epineural tissue and thereby prevents nerve penetration. SETTINGS/LOCATION: The outpatient pain clinic of the Medical University of Vienna, Austria. SUBJECTS: Fifty (50) patients receiving acupuncture treatment including P6 bilaterally. INTERVENTIONS: Patients were examined at both forearms using US (a 10-MHz linear transducer) after insertion of the needle at P6. OUTCOME MEASURES: The distance between the needle tip and the median nerve, the number of nerve contacts and nerve penetrations, as well as the number of successfully elicited de qi sensations were recorded. RESULTS: Complete data could be obtained from 97 cases. The mean distance from the needle tip to the nerve was 1.8 mm (standard deviation 2.2; range 0-11.3). Nerve contacts were recorded in 52 cases, in 14 of which the nerve was penetrated by the needle. De qi was elicited in 85 cases. We found no association between the number of nerve contacts and de qi. The 1-week follow-up showed no complications or neurologic problems. CONCLUSIONS: This is the first investigation demonstrating the relationship between acupuncture needle placement and adjacent neural structures using US technology. The rate of median nerve penetrations by the acupuncture needle at P6 was surprisingly high, but these seemed to carry no risk of neurologic sequelae. De qi at P6 does not depend on median nerve contact, nor does it prevent median nerve penetration.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.