Numerous Similarity Measures Research Articles

A Novel Cartesian Plot Analysis for Fixed Monolayers That Relates Cell Phenotype to Transfer of Contents between Fibroblasts and Cancer Cells by Cell-Projection Pumping.

We recently described cell-projection pumping as a mechanism transferring cytoplasm between cells. The uptake of fibroblast cytoplasm by co-cultured SAOS-2 osteosarcoma cells changes SAOS-2 morphology and increases cell migration and proliferation, as seen by single-cell tracking and in FACS separated SAOS-2 from co-cultures. Morphological changes in SAOS-2 seen by single cell tracking are consistent with previous observations in fixed monolayers of SAOS-2 co-cultures. Notably, earlier studies with fixed co-cultures were limited by the absence of a quantitative method for identifying sub-populations of co-cultured cells, or for quantitating transfer relative to control populations of SAOS-2 or fibroblasts cultured alone. We now overcome that limitation by a novel Cartesian plot analysis that identifies individual co-cultured cells as belonging to one of five distinct cell populations, and also gives numerical measure of similarity to control cell populations. We verified the utility of the method by first confirming the previously established relationship between SAOS-2 morphology and uptake of fibroblast contents, and also demonstrated similar effects in other cancer cell lines including from melanomas, and cancers of the ovary and colon. The method was extended to examine global DNA methylation, and while there was no clear effect on SAOS-2 DNA methylation, co-cultured fibroblasts had greatly reduced DNA methylation, similar to cancer associated fibroblasts.

Development of a Structural Comparison Method to Promote Exploration of the Potential Energy Surface in the Global Optimization of Nanoclusters.

A structural comparison method (SCM) was created to quantify the structural diversity of nanoclusters and was implemented into a global optimization algorithm to evaluate structural diversity between generated clusters on the fly and promote exploration of the potential energy surface. The SCM evaluated topological differences between clusters using the common neighbor analysis and provided a numerical measure of similarity between the two clusters. The SCM was implemented into a genetic algorithm by integrating it into a new structure + energy fitness operator such that structural diversity of clusters in the population and their energies were used to assign fitness values to clusters. The efficiency of the genetic algorithm with this new fitness operator was benchmarked against several Lennard-Jones clusters (LJ38, LJ75, and LJ98) known to be difficult cases for global optimization algorithms. For LJ38 and LJ75, this new structure + energy fitness operator performed equally well or better than the energy fitness operator. However, the efficiency of locating the global minimum of LJ98 decreased using this new structure + energy fitness operator. Further analysis of the genetic algorithm with this fitness operator showed that the algorithm did indeed promote exploration of the PES of LJ98 as desired but hindered refinement of clusters, preventing it from locating the global minimum even if the energy funnel of the global minimum had been located.

Trigonometric Similarity Measures for Neutrosophic Hypersoft Sets With Application to Renewable Energy Source Selection

Cosine and cotangent similarity measurements are critical in applications for determining degrees of difference and similarity between objects. In the literature, numerous similarity measures for various extensions of fuzzy set, soft set, Intuitionistic Fuzzy Sets (IFSs), Pythagorean Fuzzy Sets (PFSs) and HyperSoft Sets (HSSs) have been explored. Neutrosophic HyperSoft Sets (NHSSs), on the other hand, has fewer cosine and cotangent similarity measures. In this paper, we propose the trigonometric similarity measures of NHSSs. We further investigate the basic operators, theorems, and propositions for the proposed similarity measures. We know that global warming causes environmental problems. One of applications for solving global warming is the concept of renewable energy. To show the effectiveness of the proposed similarity measures, we apply them to renewable energy source selection problems. The study reveals the best geographical area to install the energy production units, under some technical attributive factors. To check the validity and superiority of the proposed work, it is compared with some existing techniques which reveal that, decision-making problems with further bifurcated attributes, have more accurate and precise results and can only be solved with this technique. In the future, the proposed techniques can be applied to case studies, in which attributes are more than one and further bifurcated along with more than one decision-maker. Also, this proposed work can be extended for several existing hybrids of hypersoft sets, intuitionistic hypersoft, neutrosophic hypersoft set, bi-polar hypersoft, m-polar hypersoft sets, and Pythagorean hypersoft set to solve Multi-Criteria Decision Making (MCDM) problems.

A New Dynamic Neighbourhood-Based Semantic Dissimilarity Measure for Ontology

The semantic web is a global initiative which employs ontologies to offer rich, semantic-based knowledge representation. Concepts in these ontologies are explored to find (dis)similarities between them using (dis)similarity measures. Despite the existence of numerous (dis)similarity measures, none have dynamically determined the quantum of information required to discover (dis)similarities between concepts. In this article, a new, efficient, feature-based semantic dissimilarity measure is proposed where the prime novelty lies in the dynamic selection of the semantic neighourhood (features) of the concepts. The neighbourhood is dynamically selected in accordance with the local density of the concept and the density of the ontology determined by the proposed density coefficient. Further, the proposed measure also scales down the dissimilarity value in accordance with the depth of the concept pair, using the novel Depth Coefficient.

Design of chemical space networks incorporating compound distance relationships.

Networks, in which nodes represent compounds and edges pairwise similarity relationships, are used as coordinate-free representations of chemical space. So-called chemical space networks (CSNs) provide intuitive access to structural relationships within compound data sets and can be annotated with activity information. However, in such similarity-based networks, distances between compounds are typically determined for layout purposes and clarity and have no chemical meaning. By contrast, inter-compound distances as a measure of dissimilarity can be directly obtained from coordinate-based representations of chemical space. Herein, we introduce a CSN variant that incorporates compound distance relationships and thus further increases the information content of compound networks. The design was facilitated by adapting the Kamada-Kawai algorithm. Kamada-Kawai networks are the first CSNs that are based on numerical similarity measures, but do not depend on chosen similarity threshold values.

Design of chemical space networks incorporating compound distance relationships.

Networks, in which nodes represent compounds and edges pairwise similarity relationships, are used as coordinate-free representations of chemical space. So-called chemical space networks (CSNs) provide intuitive access to structural relationships within compound data sets and can be annotated with activity information. However, in such similarity-based networks, distances between compounds are typically determined for layout purposes and clarity and have no chemical meaning. By contrast, inter-compound distances as a measure of dissimilarity can be directly obtained from coordinate-based representations of chemical space. Herein, we introduce a CSN variant that incorporates compound distance relationships and thus further increases the information content of compound networks. The design was facilitated by adapting the Kamada-Kawai algorithm. Kamada-Kawai networks are the first CSNs that are based on numerical similarity measures, but do not depend on chosen similarity threshold values.

Semantic Particularity Measure for Functional Characterization of Gene Sets Using Gene Ontology

BackgroundGenetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity.ResultsWe propose a new approach to compute gene set particularities based on the information conveyed by GO terms. A GO term informativeness can be computed using either its information content based on the term frequency in a corpus, or a function of the term's distance to the root. We defined the semantic particularity of a set of GO terms Sg1 compared to another set of GO terms Sg2. We combined our particularity measure with a similarity measure to compare gene sets. We demonstrated that the combination of semantic similarity and semantic particularity measures was able to identify genes with particular functions from among similar genes. This differentiation was not recognized using only a semantic similarity measure.ConclusionSemantic particularity should be used in conjunction with semantic similarity to perform functional analysis of GO-annotated gene sets. The principle is generalizable to other ontologies.

Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

Automatic classification of sentiment is important for numerous applications such as opinion mining, opinion summarization, contextual advertising, and market analysis. Typically, sentiment classification has been modeled as the problem of training a binary classifier using reviews annotated for positive or negative sentiment. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is costly. Applying a sentiment classifier trained using labeled data for a particular domain to classify sentiment of user reviews on a different domain often results in poor performance because words that occur in the train (source) domain might not appear in the test (target) domain. We propose a method to overcome this problem in cross-domain sentiment classification. First, we create a sentiment sensitive distributional thesaurus using labeled data for the source domains and unlabeled data for both source and target domains. Sentiment sensitivity is achieved in the thesaurus by incorporating document level sentiment labels in the context vectors used as the basis for measuring the distributional similarity between words. Next, we use the created thesaurus to expand feature vectors during train and test times in a binary classifier. The proposed method significantly outperforms numerous baselines and returns results that are comparable with previously proposed cross-domain sentiment classification methods on a benchmark data set containing Amazon user reviews for different types of products. We conduct an extensive empirical analysis of the proposed method on single- and multisource domain adaptation, unsupervised and supervised domain adaptation, and numerous similarity measures for creating the sentiment sensitive thesaurus. Moreover, our comparisons against the SentiWordNet, a lexical resource for word polarity, show that the created sentiment-sensitive thesaurus accurately captures words that express similar sentiments.

A Comprehensive Comparison of Different Clustering Methods for Reliability Analysis of Microarray Data

In this study, we considered some competitive learning methods including hard competitive learning and soft competitive learning with/without fixed network dimensionality for reliability analysis in microarrays. In order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of function optimization), we decided to investigate the abilities of mixture decomposition schemes. Therefore, we assert that this study covers the algorithms based on function optimization with particular insistence on different competitive learning methods. The destination is finding the most powerful method according to a pre-specified criterion determined with numerical methods and matrix similarity measures. Furthermore, we should provide an indication showing the intrinsic ability of the dataset to form clusters before we apply a clustering algorithm. Therefore, we proposed Hopkins statistic as a method for finding the intrinsic ability of a data to be clustered. The results show the remarkable ability of Rayleigh mixture model in comparison with other methods in reliability analysis task.

On the transitivity of a parametric family of cardinality-based similarity measures

We introduce a parametric family of cardinality-based similarity measures for ordinary sets (on a finite universe) harbouring numerous well-known similarity measures. We characterize the Łukasiewicz-transitive and product-transitive members of this family. Their importance derives from their one-to-one correspondence with pseudo-metrics. Fuzzification schemes based on a commutative quasi-copula are then used to transform these similarity measures for ordinary sets into similarity measures for fuzzy sets, rendering them applicable on graded feature set representations of objects. The main result of this paper is that transitivity, and hence also the corresponding dual metric interpretation, is preserved along this fuzzification process.

Comparative evaluation of similarity measures for the rigid registration of multi-modal head images

Image registrations that are based on similarity measures simply adjust the parameters of an appropriate spatial transformation model until the similarity measure reaches an optimum. The numerous similarity measures that have been proposed in the past are differently sensitive to imaging modality, image content and differences in the image content, selection of the floating and target image, partial image overlap, etc. In this paper, we evaluate and compare 12 similarity measures for the rigid registration. To study the impact of different imaging modalities on the behavior of similarity measures, we have used 16 CT/MR and 6 PET/MR image pairs with known ‘gold standard’ registrations. The results for the PET/MR registration and for the registration of CT to both rectified and unrectified MR images indicate that mutual information, normalized mutual information and the entropy correlation coefficient are the most accurate similarity measures and have the smallest risk of being trapped in a local optimum. The results of an experiment on the impact of exchanging the floating and target image indicate that, especially in MR/PET registrations, the behavior of some similarity measures, such as mutual information, significantly depends on which image is the floating and which is the target.

Significance testing of a cluster of multivariate binary variables: comparison of the tripartite T index to three common similarity measures

Similarity measures quantify resemblance between pairs of items when each consists of a pattern of two-state (eg, presence versus absence) variables. Numerous similarity measures, many of which are straightforward to calculate and interpret, have been developed and characterized. Methods for testing if items within a specified cluster are significantly more similar to each other than to items outside the cluster have not been extensively developed for binary responses, but a permutation test procedure using a measure of distinctness is available to do this. We compare three well known similarity measures, the Dice, Jaccard and simple matching coefficients, with the more complex tripartite T similarity index recently proposed by Tulloss. Each measure is used in significance tests of whether hypothesized subsets of items are legitimately grouped for resemblance. Theoretically derived measures reflecting diverse scenarios found in medical research and data from neuropsychological research illustrate the methods. Results for the tripartite T measure were comparable to the other methods in some settings, and essentially the same as the Dice coefficient overall when compared theoretically and on the same clinical data. Some shortcomings with the Tulloss algorithm were found and limit the usefulness of the tripartite T index in medical applications.

Surface Map Comparison: Studying Function Diversity of Homologous Proteins

A simplified protein surface cartography approach has been developed to assist in the analysis of surface features in homologous families, and thus to predict conservation or divergence of protein functions and protein-protein interaction patterns. A spherical approximation of protein surface was used, with a focus on charged and hydrophobic residues. The resulting surface map allows for qualitative analysis and comparison of surfaces of proteins, but can also be used to define a simple numerical measure of map similarity between two or more proteins. The latter was shown to be useful for function based classifications within large protein families. Surface map analysis was tested on several test cases: haemoglobins, death domains and TRAF domains. It was shown that surface map comparison allows a better function prediction than general sequence analysis methods and can reproduce known examples of functional variation within a divergent group of proteins. In another example, we predict novel, unexpected sets of common functional properties for seemingly distant members of a large group of divergent proteins. The method was also shown to be robust enough to allow using protein models from comparative modelling instead of experimental structures.

Numerical similarity and dissimilarity measures between two trees

Quantifying the measure of similarity between two trees is a problem of intrinsic importance in the study of algorithms and data structures and has applications in computational molecular biology, structural/syntactic pattern recognition and in data management. In this paper we define and formulate an abstract measure of comparison, /spl Omega/(T/sub 1/, T/sub 2/), between two trees T/sub 1/ and T/sub 2/ presented in terms of a set of elementary intersymbol measures /spl omega/(.,.) and two abstract operators /spl oplus/ and /spl otimes/. By appropriately choosing the concrete values for these two operators and for /spl omega/(.,.), this measure can be used to define various quantities including: (1) the edit distance between two trees, (2) the size of their largest common subtree, (3) Prob(T/sub 2/|T/sub 1/), the probability of receiving T/sub 2/ given that T/sub 1/ was transmitted across a channel causing independent substitution and deletion errors, and (4) the a posteriori probability of T/sub 1/ being the transmitted tree given that T/sub 2/ is the received tree containing independent substitution, insertion and deletion errors. The recursive properties of /spl Omega/(T/sub 1/, T/sub 2/) have been derived and a single generic iterative dynamic programming scheme to compute all the above quantities has been developed. The time and space complexities of the algorithm have been analyzed and the implications of our results in both theoretical and applied fields has been discussed.

Application of the shape group method to conformational processes: Shape and conjugation changes in the conformers of 2‐phenyl pyrimidine

AbstractThe shape group method (SGM) and the associated (a,b)‐parameter maps provide a detailed shape characterization of molecular charge distributions. This method is applied to the study of the variations of shape and conjugation of conformers of 2‐phenyl pyrimidine in their electronic ground state. Within the SGM framework, the method of (a,b)‐parameter maps provides a concise, nonvisual, algorithmic technique for shape characterization of molecules with fixed nuclear geometries. Moreover, shape codes derived from the (a,b)‐parameter maps afford a practical means for efficiently storing the shape properties of molecules in an electronic database. The shape codes of two or more charge distributions can be compared directly, and numerical measures of molecular shape similarity can be computed using a technique that is simple, fast, and inexpensive, especially in relation to direct, pairwise comparisons of electronic charge densities. The quantitative and automated nature of the method suggests applications in the field of computer‐aided molecular design. In this study, the method is used for the first time to determine detailed numerical shape codes and shape similarity measures for a nontrivial conformational problem involving changes in energy and in conjugation. Numerical shape similarity measures of eight conformers of 2‐phenyl pyrimidine are determined and correlated with variations in conformational energy and conjugation. The competing effects of steric repulsion and conjugation lead to important correlations between conformational energy and shape. © 1995 John Wiley & Sons, Inc.

Toward similarity measures for macromolecular bodies: Medla test calculations for substituted benzene systems

AbstractA new method is proposed for the evaluation of numerical similarity measures for large molecules, defined in terms of their electron density (ED) distributions. The technique is based on the Molecular Electron Density Lego Assembler (MEDLA) approach, proposed earlier for the generation of ab initio quality electron densities for proteins and other macromolecules. The reliability of the approach is tested using a family of 13 substituted aromatic systems for which both standard ab initio electron density computations and the MEDLA technique are applicable. These tests also provide additional examples for evaluating the accuracy of the MEDLA technique. Electron densities for a series of 13 substituted benzenes were calculated using the standard ab initio method with STO‐3G, 3‐21G, and 6‐31G** basis sets as well as the MEDLA approach with a 6‐31G** database of electron density fragments. For each type of calculation, pairwise similarity measures of these compounds were calculated using a point‐by‐point numerical comparison of the EDs. From these results, 2D similarity maps were constructed, serving as an aid for quick visual comparisons for the entire molecular family. The MEDLA approach is shown to give virtually equivalent numerical similarity measures and similarity maps as the standard ab initio method using a 6‐31G** basis set. By contrast, significant differences are found between the standard ab initio 6‐31G** results and the standard ab initio results obtained with smaller STO‐3G and 3‐21G basis sets. These tests indicate that the MEDLA‐based similarity measures faithfully mimic the actual, standard ab initio 6‐31G** similarity measures, suggesting the MEDLA method as a reliable technique to assess the shape similarities of proteins and other macromolecules. The speed of the MEDLA computations allows rapid, pairwise comparisons of the actual EDs for a series of molecules, requiring no more computer time than other simplified, less detailed representations of molecular shape. The MEDLA method also reduces the need to store large volumes of numerical density data on disk, as these densities can be quickly recomputed when needed. For these reasons, the proposed MEDLA similarity analysis technique is likely to become a useful tool in computational drug design. © 1995 John Wiley & Sons, Inc.

The degree of similarity of three-dimensional bodies: Application to molecular shape analysis

Similarity of shape features of three-dimensional bodies is of importance in many fields. Computational methods that are suitable to provide numerical measures for such similarities are expected to find applications in a wide variety of areas. Whereas relative measures based on direct pair comparisons are useful, nevertheless, methods that involve absolute shape descriptors are expected to be more universally applicable. The general “grade of similarity” concept proposed in this study is based on such absolute shape descriptors of three-dimensional bodies. The study of similarity of the three-dimensional shapes of molecules as represented, for example, by their electronic charge distributions, or electrostatic potentials, or simply by their fused spheres Van der Waals surfaces, is an important component of modem drug design. A family of topological methods, the shape group methods (SGM), have been proposed recently for the study of the shapes of formal molecular bodies, evaluating and comparing numerical shape codes for the non-visual comparison of molecules by the computer. In this contribution a new, and conceptually simpler numerical measure of shape similarity is proposed, applicable for the computer evaluation of similarity of arbitrary three-dimensional objects of closed surfaces. The technique is suggested for the non-visual, numerical evaluation of shape similarity of formal molecular bodies and contour surfaces.

A comparison between conceptual clustering and conventional clustering

Clustering is a process of partitioning a given set of patterns into meaningful groups. The clustering process can be viewed as consisting of the following three phases: (i) feature selection phase, (ii) classification phase, and (iii) description generation phase. Conventional clustering algorithms implicitly use knowledge about the clustering environment to a large extent in the feature selection phase. This reduces the need for the environmental knowledge in the remaining two phases, permitting the usage of simple numerical measure of similarity in the classification phase. Conceptual clustering algorithms proposed by Michalski and Stepp [ IEEE Trans. PAMI, PAMI-5, 396–410 (1983)] and Stepp and Michalski [ Artif. Intell., pp. 43–69 (1986)] make use of the knowledge about the clustering environment in the form of a set of predefined concepts to compute the conceptual cohesiveness during the classification phase. Michalski and Stepp [ IEEE Trans. PAMI, PAMI-5, 396–410 (1983)] have argued that the results obtained with the conceptual clustering algorithms are superior to conventional methods of numerical classification. However, this claim was not supported by the experimental results obtained by Dale [ IEEE Trans. PAMI, PAMI-7, 241–244 (1985)]. In this paper a theoretical framework, based on an intuitively appealing set of axioms, is developed to characterize the equivalence between the conceptual clustering and conventional clustering. In other words, it is shown that any classification obtained using conceptual clustering can also be obtained using conventional clustering and vice versa.

Similarity measure for mixed attribute types.

SUPPOSE we wish to examine the relationships among n objects (units, samples) with respect to m attributes (variates, characters, tests). Such a need arises in geology and archaeology, literary studies, medicine and all the biological sciences, particularly the taxonomy of plants and animals. I define here a numerical measure of similarity between objects that can be applied when any mixture of quantitative and qualitative attributes is involved and when observations are missing.