Measure Of Information Discrepancy Research Articles

Use of Information Discrepancy Measure to Register Medical Images

Function of Degree of Disagreement (FDOD), a new measure of information discrepancy, quantifies the discrepancy of multiple sequences. This function has some peculiar mathematical properties, such as symmetry, boundedness and monotonicity. In this contribution, we first introduce the FDOD function to solve the three-dimensional (3-D) medical image registration problem. Numerical experiments illustrate that the new registration method based on the FDOD function can obtain subvoxel registration accuracy, and it is a competitive method with the mutual information based method.

On “A comment on ‘Prediction of protein structural classes by a new measure of information discrepancy”’

This note points out that the comment article by Kedarisetti et al. (2006a, refer to ‘K-comment’ hereafter) was based on the code analysis with methodological and mathematical errors. The incorrect modification of the original code and FDOD method has resulted in the wrong conclusions in K-comment.

Analysis of similarity of DNA sequences based on a measure of information discrepancy

Function of Degree of Disagreement (FDOD) has been successfully applied to the research of bioinformatics. Its performance is greatly impacted by the length of subsequence, when the information included in a sequence is described by its subsequence distribution. In this paper, we proposed a new representation called base-base information set to characterize the sequence, then computed the distance between two sequences by FDOD. The base-base information set comprises the joint probabilities of base pair at a distance of 1 to L, where L is an alterable parameter. Also we analyzed how the distance changes when L is changed. As the experimental result shows, the distance between two sequences is insensitive to the change of L when they are closely similar, so our method is effective for analyzing similarity of sequences.

Detecting community structure in complex networks based on a measure of information discrepancy

Properties of complex networks, such as small-world property, power-law degree distribution, network transitivity, and network- community structure which seem to be common to many real-world networks have attracted great interest among researchers. In this study, global information of the networks is considered by defining the profile of any node based on the shortest paths between it and all the other nodes in the network; then a useful iterative procedure for community detection based on a measure of information discrepancy and the popular modular function Q is presented. The new iterative method does not need any prior knowledge about the community structure and can detect an appropriate number of communities, which can be hub communities or non-hub communities. The computational results of the method on real networks confirm its capability.

Discrimination of Outer Membrane Proteins by a New Measure of Information Discrepancy

Discriminating outer membrane proteins for globular proteins (GPs) and other types of membrane proteins from genomic sequences is an important and hot topic. In this paper, a measure based on information discrepancy is proposed and applied to the discrimination of outer membrane proteins. It differs from previous methods which are based on amino acid composition. Our approach focuses on the comparison of subsequence distributions and takes into account the effect of residue order in protein primary structures. As a result, the new approach outperforms all previous methods on the same benchmark datasets. In particular, we show that the proposed approach has correctly identified the outer membrane proteins at an accuracy of 99% for the training set of 337 proteins and has correctly excluded the GPs at an accuracy of 86% in a non-redundant dataset of 668 proteins. Furthermore, this method is able to correctly exclude alpha-helical membrane proteins at an accuracy of 100%.

A comment on “Prediction of protein structural classes by a new measure of information discrepancy”

Protein structural class describes the overall folding type of a protein or its domain. A number of methods were developed to predict protein structural class based on its primary sequence. The homology of the predicted sequences with respect to the training sequences is a key attribute for the prediction performance. In this article we investigated the FDOD method developed by Jin et al. [Jin, L., Fang, W., Tang, H., 2003. Prediction of protein structural classes by a new measure of information discrepancy. Comput. Biol. Chem. 27, 373–380], which gave high prediction accuracy on a low homology dataset, and we empirically confirmed that the reported results were an artifact of improper implementation.

MSAID: multiple sequence alignment based on a measure of information discrepancy

We propose an algorithm of global multiple sequence alignment that is based on a measure of what we call information discrepancy. The algorithm follows a progressive alignment iteration strategy that makes use of what we call a function of degree of disagreement (FDOD). MSAID begins with distance calculation of pairwise sequences, based on FDOD as a numerical scoring measure. In the next step, the resulting distance matrix is used to construct a guide tree via the neighbor-joining method. The tree is then used to produce a multiple alignment. Current alignment is next used to produce a new matrix and a new tree (with FDOD scoring measure again). This iterative process continues until convergence criteria (or a stopping rule) are satisfied. MSAID was tested and compared with other prior methods by using reference alignments from BAliBASE 2.01. For the alignments with no large N/C-terminal extensions or internal insertions MSAID received the top overall average in the tests. Moreover, the results of testing indicate that MSAID performs as well as other alignment methods with an occasional tendency to perform better than these prior techniques. We, therefore, believe that MSAID is a solid and reliable method of choice, which is often (if not always) superior to other global alignment techniques.

Accurate classification of homodimeric vs other homooligomeric proteins using a new measure of information discrepancy.

It has been shown that protein primary sequence encodes quaternary structure information. In this present work, function of degree of disagreement (FDOD), a new measure of information discrepancy, is applied to discriminating between homodimers and other homooligomeric proteins from the primary structure. This new approach is based on subsequence distributions of the primary sequences, so the effect of residue order on protein structure is taken into account. When the length of subsequence is 4, the overall accuracy of the 10-fold cross-validation test attains to 82.5%, which is much better than that of the previous method on the same data set. Our tests demonstrate that the residue order along protein sequences plays an important role in the prediction of homooligomers. In addition, our results suggest that FDOD measure is a simple and powerful tool for the prediction of protein multimeric states.

Prediction of protein structural classes by a new measure of information discrepancy

Since it was observed that the structural class of a protein is related to its amino acid composition, various methods based on amino acid composition have been proposed to predict protein structural classes. Though those methods are effective to some degree, their predictive quality is confined because amino acid composition cannot sufficiently include the information of protein sequences. In this paper, a measure of information discrepancy is applied to the prediction of protein structural classes; different from the previous methods, this new approach is based on the comparisons of subsequence distributions; therefore, the effect of residue order on protein structure is taken into account. The predictive results of the new approach on the same data set are better than those of the previous methods. As to a data set of 1401 sequences with no more than 30% redundancy, the overall correctness rates of resubstitution test and Jackknife test are 99.4 and 75.02%, respectively, and to other data sets the similar results are also obtained. All tests demonstrate that the residue order along protein sequences plays an important role on recognition of protein structural classes, especially for α/β proteins and α+β proteins. In addition, the tests also show that the new method is simple and efficient.

A New Approach of Information Discrepancy to Analysis of Questionnaire Data

Analysis of questionnaires plays an important role in many areas. In this article, a new approach based on a measure of information discrepancy is proposed to investigate variable selection and discriminant analysis which are two basic problems in analysis of questionnaire data. In contrast to other methods, this approach can be used in cases with multiple groups of samples, i.e., it can deal with more complicated questionnaires involving multiple questions, multiple answers, and multiple groups. Moreover, the elementary applications show that the new approach of discriminant analysis is not only effective but also simple.