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Comparison of Profile Similarity Measures for Genetic Interaction Networks  [PDF]
Raamesh Deshpande, Benjamin VanderSluis, Chad L. Myers
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0068664
Abstract: Analysis of genetic interaction networks often involves identifying genes with similar profiles, which is typically indicative of a common function. While several profile similarity measures have been applied in this context, they have never been systematically benchmarked. We compared a diverse set of correlation measures, including measures commonly used by the genetic interaction community as well as several other candidate measures, by assessing their utility in extracting functional information from genetic interaction data. We find that the dot product, one of the simplest vector operations, outperforms most other measures over a large range of gene pairs. More generally, linear similarity measures such as the dot product, Pearson correlation or cosine similarity perform better than set overlap measures such as Jaccard coefficient. Similarity measures that involve L2-normalization of the profiles tend to perform better for the top-most similar pairs but perform less favorably when a larger set of gene pairs is considered or when the genetic interaction data is thresholded. Such measures are also less robust to the presence of noise and batch effects in the genetic interaction data. Overall, the dot product measure performs consistently among the best measures under a variety of different conditions and genetic interaction datasets.
Measures of Distributional Similarity  [PDF]
Lillian Lee
Computer Science , 2000,
Abstract: We study distributional similarity measures for the purpose of improving probability estimation for unseen cooccurrences. Our contributions are three-fold: an empirical comparison of a broad range of measures; a classification of similarity functions based on the information that they incorporate; and the introduction of a novel function that is superior at evaluating potential proxy distributions.
Analysis of the mRNA expression similarity of genes in the same gene expression regulatory pathway
调控通路内基因表达的相关性分析

LI Chuan-Xing~,LI Xia~,
李传星
,李霞,郭政,宫滨生,屠康

遗传 , 2004,
Abstract: In this work we analysed the relationship of gene expression from the point of view of gene expression regulatory pathway. Using seven sets of Saccharomyces cerevisiae gene chip expression profile data, and information from two pathway database (KEGG and CYGD), we analysed the mRNA expression similarity of genes in the same gene expression regulatory pathway by Genehub software, that involved totally 16 pathways with more than 495 genes. From the calculation of two different similarity measures-Pearson correlation coefficient and Spearman correlation coefficient, we found that about 94 percent of gene expression regulatory pathways are correlatively expressed in more than 4(including 4) sets of expression profile data, and it presents additional evidence for the correlation between gene function and its expression in the view of gene expression regulatory pathways.
Metrics for GO based protein semantic similarity: a systematic evaluation  [cached]
Pesquita Catia,Faria Daniel,Bastos Hugo,Ferreira António EN
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-s5-s4
Abstract: Background Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic similarity in this context, since there is no conclusive evaluation of the various measures. Another issue, is whether electronic annotations should or not be used in semantic similarity calculations. Results We conducted a systematic evaluation of GO-based semantic similarity measures using the relationship with sequence similarity as a means to quantify their performance, and assessed the influence of electronic annotations by testing the measures in the presence and absence of these annotations. We verified that the relationship between semantic and sequence similarity is not linear, but can be well approximated by a rescaled Normal cumulative distribution function. Given that the majority of the semantic similarity measures capture an identical behaviour, but differ in resolution, we used the latter as the main criterion of evaluation. Conclusions This work has provided a basis for the comparison of several semantic similarity measures, and can aid researchers in choosing the most adequate measure for their work. We have found that the hybrid simGIC was the measure with the best overall performance, followed by Resnik's measure using a best-match average combination approach. We have also found that the average and maximum combination approaches are problematic since both are inherently influenced by the number of terms being combined. We suspect that there may be a direct influence of data circularity in the behaviour of the results including electronic annotations, as a result of functional inference from sequence similarity.
Understanding (dis)similarity measures  [PDF]
Lluís A. Belanche
Computer Science , 2012,
Abstract: Intuitively, the concept of similarity is the notion to measure an inexact matching between two entities of the same reference set. The notions of similarity and its close relative dissimilarity are widely used in many fields of Artificial Intelligence. Yet they have many different and often partial definitions or properties, usually restricted to one field of application and thus incompatible with other uses. This paper contributes to the design and understanding of similarity and dissimilarity measures for Artificial Intelligence. A formal dual definition for each concept is proposed, joined with a set of fundamental properties. The behavior of the properties under several transformations is studied and revealed as an important matter to bear in mind. We also develop several practical examples that work out the proposed approach.
Distance and Similarity Measures for Soft Sets  [PDF]
Athar Kharal
Mathematics , 2010, DOI: 10.1142/S1793005710001724
Abstract: In [P. Majumdar, S. K. Samanta, Similarity measure of soft sets, New Mathematics and Natural Computation 4(1)(2008) 1-12], the authors use matrix representation based distances of soft sets to introduce matching function and distance based similarity measures. We first give counterexamples to show that their Definition 2.7 and Lemma 3.5(3) contain errors, then improve their Lemma 4.4 making it a corllary of our result. The fundamental assumption of Majumdar et al has been shown to be flawed. This motivates us to introduce set operations based measures. We present a case (Example 28) where Majumdar-Samanta similarity measure produces an erroneous result but the measure proposed herein decides correctly. Several properties of the new measures have been presented and finally the new similarity measures have been applied to the problem of financial diagnosis of firms.
A new measure for functional similarity of gene products based on Gene Ontology
Andreas Schlicker, Francisco S Domingues, J?rg Rahnenführer, Thomas Lengauer
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-302
Abstract: We present a new method for comparing sets of GO terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; simRel and funSim. One measure (simRel) is applied in the comparison of the biological processes found in different groups of organisms. The other measure (funSim) is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families.The approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families.Genome annotation relies heavily on bioinformatics methods. The identification of homologous relationships is a powerful and frequently used approach for protein-level annotation [1], where query protein sequences are compared to sequences of characterized proteins in order to find homologies. Based on this comparison, proteins of unknown function are assigned to characterized protein families, generating testable hypotheses of their molecular function. However, this established annotation approach has several limitations. Devos and Valencia [2,3] suggest that up to 30% of the function annotations made through sequence similarity searches might be erroneous.
A Survey of Binary Similarity and Distance Measures
Seung-Seok Choi,Sung-Hyuk Cha,Charles C. Tappert
Journal of Systemics, Cybernetics and Informatics , 2010,
Abstract: The binary feature vector is one of the most common representations of patterns and measuring similarity and distance measures play a critical role in many problems such as clustering, classification, etc. Ever since Jaccard proposed a similarity measure to classify ecological species in 1901, numerous binary similarity and distance measures have been proposed in various fields. Applying appropriate measures results in more accurate data analysis. Notwithstanding, few comprehensive surveys on binary measures have been conducted. Hence we collected 76 binary similarity and distance measures used over the last century and reveal their correlations through the hierarchical clustering technique.
Image Steganalysis with Binary Similarity Measures  [cached]
Avc?ba? ?smail,Kharrazi Mehdi,Memon Nasir,Sankur Bülent
EURASIP Journal on Advances in Signal Processing , 2005,
Abstract: We present a novel technique for steganalysis of images that have been subjected to embedding by steganographic algorithms. The seventh and eighth bit planes in an image are used for the computation of several binary similarity measures. The basic idea is that the correlation between the bit planes as well as the binary texture characteristics within the bit planes will differ between a stego image and a cover image. These telltale marks are used to construct a classifier that can distinguish between stego and cover images. We also provide experimental results using some of the latest steganographic algorithms. The proposed scheme is found to have complementary performance vis-à-vis Farid's scheme in that they outperform each other in alternate embedding techniques.
Description and Evaluation of Semantic Similarity Measures Approaches  [PDF]
Thabet Slimani
Computer Science , 2013, DOI: 10.5120/13897-1851
Abstract: In recent years, semantic similarity measure has a great interest in Semantic Web and Natural Language Processing (NLP). Several similarity measures have been developed, being given the existence of a structured knowledge representation offered by ontologies and corpus which enable semantic interpretation of terms. Semantic similarity measures compute the similarity between concepts/terms included in knowledge sources in order to perform estimations. This paper discusses the existing semantic similarity methods based on structure, information content and feature approaches. Additionally, we present a critical evaluation of several categories of semantic similarity approaches based on two standard benchmarks. The aim of this paper is to give an efficient evaluation of all these measures which help researcher and practitioners to select the measure that best fit for their requirements.
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