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An effective measure corresponding to biological significance


Shifting and scaling correlations are correspondent of biological significance in gene expression data analysis. Recent works have mentioned about the significance of negative correlation as well. In this paper, we distinguish and define the negative form of shifting and scaling correlations as negatively shifted correlation and negatively scaled correlation, respectively. Another issue in gene expression data analysis is simultaneous detection of both negative and positive correlation (shifting and scaling) over two disjoint subsets of features in a pair of gene expressions while most existing measures can detect only either of the correlations at a time. Here we propose a measure that can detect positive and negative forms of shifting and scaling correlation simultaneously between a pair of gene expressions over two disjoint subsets of features.

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  • Ahmed HA, Mahanta P, Bhattacharyya D, Kalita JK (2011) Gerc: tree based clustering for gene expression data. In: IEEE 11th international conference on bioinformatics and bioengineering (BIBE), pp 299–302

  • Ahmed H, Mahanta P, Bhattacharyya D (2012) Negative correlation aided network module extraction. Proc Technol 6 (Elsevier):658–665

  • Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium., vol 25 (Nature genetics, Nature Publishing Group)

  • Attwood TK, Parry-Smith DJ, Phukan S (1999) Introduction to bioinformatics. Longman, England

    Google Scholar 

  • Barrett T, Suzek T, Troup D, Wilhite S, Ngau W, Ledoux P, Rudnev D, Lash A, Fujibuchi W, Edgar R (2005) NCBI GEO: mining millions of expression profilesGdatabase and tools, vol 33 (Nucleic acids research, Oxford Univ Press)

  • Bandyopadhyay S, Bhattacharyya M (2011) A biologically inspired measure for coexpression analysis. IEEE/ACM Trans Comput Biol Bioinform 8:929–942

  • Berriz G, King O, Bryant B, Sander C, Roth F (2003) Characterizing gene sets with FuncAssociate, vol 19 (Bioinformatics, Oxford Univ Press)

  • Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares M, Haussler D (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines, vol 97 (National Acad Sciences)

  • Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown P, Herskowitz I (1998) The transcriptional program of sporulation in budding yeast. Sci Am Assoc Adv Sci 282

  • Chenna R, Sugawara H, Koike T, Lopez R, Gibson TJ, Higgins DG, Thompson JD (2003) Multiple sequence alignment with the Clustal series of programs, vol 31 (Nucleic acids research, Oxford Univ Press)

  • Dhillon IS, Marcotte EM, Roshan U (2003) Diametrical clustering for identifying anti-correlated gene clusters. Bioinformatics 19 (Oxford Univ Press)

  • Goyal A, Ahmed H, Bhattacharyya D (2014) PNCSIM: an effective measure to identify gene coexpressed patterns, vol. 5. In: Proceedings of International Conference on Advances in Communication, Network and Computing, CNC, Elsevier, Chennai, India

  • Jiang D, Tang C, Zhang A (2004) Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng 16

  • Lin D (1998) An information-theoretic definition of similarity. In: Proceedings of ICML, Morgan Kaufmann

  • Larkin M, Blackshields G, Brown N, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R et al (2007) Clustal W and Clustal X version 2.0, vol 23 (Bioinformatics, Oxford Univ Press)

  • Madeira SC, Oliveira AL (2004) IEEE Biclustering algorithms for biological data analysis: a survey, vol 1

  • Mahanta P, Ahmed H, Bhattacharyya D, Kalita JK (2011) Triclustering in gene expression data analysis: a selected survey. In: 2nd national conference on emerging trends and applications in computer science (NCETACS), pp 1–6

  • Mahanta P, Ahmed H, Bhattacharyya D, Kalita J (2012) An effective method for network module extraction from microarray data. BMC Bioinform 13 (BioMed Central Ltd)

  • Pesquita C, Faria D, Bastos H, Ferreira A, Falcao A, Couto F (2008) Metrics for GO based protein semantic similarity: a systematic evaluation, vol 9 (BMC bioinformatics, BioMed Central Ltd)

  • Rousseeuw P, Kaufman L (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York

    Google Scholar 

  • Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 11

  • Schlicker A, Domingues FS, Rahnenführer J, Lengauer T (2006) A new measure for functional similarity of gene products based on Gene Ontology, vol 7 (BMC bioinformatics, BioMed Central Ltd)

  • Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees, vol 4 (Molecular biology and evolution, SMBE)

  • Stekel D (2003) Microarray bioinformatics. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Tavazoie S, Hughes J, Campbell M, Cho R, Church G et al (1999) Systematic determination of genetic network architecture, vol 22 (Nature genetics, NATURE PUBLISHING CO.)

  • Tan P et al (2007) Introduction to data mining. Pearson Education India

  • Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT et al (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function, vol 38 (Nucleic acids research, Oxford Univ Press)

  • Wang JZ, Du Z, Payattakool R, Yu PS, Chen CF (2007) A new method to measure the semantic similarity of GO terms. Bioinformatics 23:1274–1281

  • Yu G, Li F, Qin Y, Bo X, Wu Y, Wang S (2010) GOSemSim: an R package for measuring semantic similarity among GO terms and gene products, vol 26 (Bioinformatics, Oxford Univ Press)

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Correspondence to Dhruba K. Bhattacharyya.

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Goyal, A., Ahmed, H.A. & Bhattacharyya, D.K. An effective measure corresponding to biological significance. Netw Model Anal Health Inform Bioinforma 3, 72 (2014).

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  • Microarray data
  • Gene expression data analysis
  • Shifting correlation
  • Scaling correlation
  • Negative correlation