Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2D fragment bit-strings

This paper compares 22 different similarity coefficients when they are used for searching databases of 2D fragment bit-strings. Experiments with the National Cancer Institute's AIDS and IDAlert databases show that the coefficients fall into several well-marked clusters, in which the members of a cluster will produce comparable rankings of a set of molecules. These clusters provide a basis for selecting combinations of coefficients for use in data fusion experiments. The results of these experiments provide a simple way of increasing the effectiveness of fragment-based similarity searching systems

Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2D fragment bit-strings

This paper compares 22 different similarity coefficients when they are used for searching databases of 2D fragment bit-strings. Experiments with the National Cancer Institute's AIDS and IDAlert databases show that the coefficients fall into several well-marked clusters, in which the members of a cluster will produce comparable rankings of a set of molecules. These clusters provide a basis for selecting combinations of coefficients for use in data fusion experiments. The results of these experiments provide a simple way of increasing the effectiveness of fragment-based similarity searching systems

Getting Things in Order: An Introduction to the R Package seriation

Seriation, i.e., finding a suitable linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides an infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.

Similarity Measures for Clustering SNP Data

The issue of suitable similarity measures for a particular kind of genetic data – so called SNP data – arises from the GENICA (Interdisciplinary Study Group on Gene Environment Interaction and Breast Cancer in Germany) case-control study of sporadic breast cancer. The GENICA study aims to investigate the influence and interaction of single nucleotide polymorphic (SNP) loci and exogenous risk factors. A single nucleotide polymorphism is a point mutation that is present in at least 1 % of a population. SNPs are the most common form of human genetic variations. In particular, we consider 65 SNP loci and 2 insertions of longer sequences in genes involved in the metabolism of hormones, xenobiotics and drugs as well as in the repair of DNA and signal transduction. Assuming that these single nucleotide changes may lead, for instance, to altered enzymes or to a reduced or enhanced amount of the original enzymes – with each alteration alone having minor effects – we aim to detect combinations of SNPs that under certain environmental conditions increase the risk of sporadic breast cancer. The search for patterns in the present data set may be performed by a variety of clustering and classification approaches. We consider here the problem of suitable measures of proximity of two variables or subjects as an indispensable basis for a further cluster analysis. Generally, clustering approaches are a useful tool to detect structures and to generate hypothesis about potential relationships in complex data situations. Searching for patterns in the data there are two possible objectives: the identification of groups of similar objects or subjects or the identification of groups of similar variables within the whole or within subpopulations. Comparing the individual genetic profiles as well as comparing the genetic information across subpopulations we discuss possible choices of similarity measures, in particular similarity measures based on the counts of matches and mismatches. New matching coefficients are introduced with a more flexible weighting scheme to account for the general problem of the comparison of SNP data: The large proportion of homozygous reference sequences relative to the homo- and heterozygous SNPs is masking the accordances and differences of interest. --GENICA,single nucleotide polymorphism (SNP),sporadic breast cancer,similarity,Matching Coefficient,Flexible Matching Coefficient