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Journal articles on the topic 'Functional similarity'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Yin, Jie, and Ai Lun Wang. "Research on Functional Similarity and Structural Similarity of Mechanical Components." Applied Mechanics and Materials 541-542 (March 2014): 603–7. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.603.

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The similarity theory is in essence a method based on equation expression. In the application, the model is regarded to be similar to the prototype only if the similarity theorem can be met. But different physical objects have the same equation expression, so the model is always not similar to the prototype. To solve this problem, a new method which studies the similarity of mechanical components from the perspective of function and structure is put forward in this paper, based on the similarity theory and the ‘black box’ concept. The functional similarity criteria and structural similarity criteria derived from this method can accurately describe the similar properties of mechanical components under different similar requirements and can be used to set up the experimental model. Finally, a series of basic mechanical components are taken for example to verify the feasibility and superiority of this method. The method put forward in this paper can be applied to model test design and engineering test, etc. under different similar requirements.

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2

Peng, Jiajie, Yadong Wang, and Jin Chen. "Towards integrative gene functional similarity measurement." BMC Bioinformatics 15, Suppl 2 (2014): S5. http://dx.doi.org/10.1186/1471-2105-15-s2-s5.

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3

Pavoine, Sandrine, and Carlo Ricotta. "Functional and phylogenetic similarity among communities." Methods in Ecology and Evolution 5, no. 7 (May 10, 2014): 666–75. http://dx.doi.org/10.1111/2041-210x.12193.

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4

Geerlings, P., G. Boon, C. Van Alsenoy, and F. De Proft. "Density functional theory and quantum similarity." International Journal of Quantum Chemistry 101, no. 6 (October 22, 2004): 722–32. http://dx.doi.org/10.1002/qua.20329.

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5

Al Dallal, J. "Software similarity-based functional cohesion metric." IET Software 3, no. 1 (2009): 46. http://dx.doi.org/10.1049/iet-sen:20080054.

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6

Schlicker, A., and M. Albrecht. "FunSimMat: a comprehensive functional similarity database." Nucleic Acids Research 36, Database (December 23, 2007): D434—D439. http://dx.doi.org/10.1093/nar/gkm806.

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7

Glass, Kimberly, Edward Ott, Wolfgang Losert, and Michelle Girvan. "Implications of functional similarity for gene regulatory interactions." Journal of The Royal Society Interface 9, no. 72 (February 2012): 1625–36. http://dx.doi.org/10.1098/rsif.2011.0585.

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If one gene regulates another, those two genes are likely to be involved in many of the same biological functions. Conversely, shared biological function may be suggestive of the existence and nature of a regulatory interaction. With this in mind, we develop a measure of functional similarity between genes based on annotations made to the Gene Ontology in which the magnitude of their functional relationship is also indicative of a regulatory relationship. In contrast to other measures that have previously been used to quantify the functional similarity between genes, our measure scales the strength of any shared functional annotation by the frequency of that function's appearance across the entire set of annotations. We apply our method to both Escherichia coli and Saccharomyces cerevisiae gene annotations and find that the strength of our scaled similarity measure is more predictive of known regulatory interactions than previously published measures of functional similarity. In addition, we observe that the strength of the scaled similarity measure is correlated with the structural importance of links in the known regulatory network. By contrast, other measures of functional similarity are not indicative of any structural importance in the regulatory network. We therefore conclude that adequately adjusting for the frequency of shared biological functions is important in the construction of a functional similarity measure aimed at elucidating the existence and nature of regulatory interactions. We also compare the performance of the scaled similarity with a high-throughput method for determining regulatory interactions from gene expression data and observe that the ontology-based approach identifies a different subset of regulatory interactions compared with the gene expression approach. We show that combining predictions from the scaled similarity with those from the reconstruction algorithm leads to a significant improvement in the accuracy of the reconstructed network.

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8

Dalmis, Mehmet Ufuk, and Ata Akin. "Similarity analysis of functional connectivity with functional near-infrared spectroscopy." Journal of Biomedical Optics 20, no. 8 (August 21, 2015): 086012. http://dx.doi.org/10.1117/1.jbo.20.8.086012.

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9

Beven, K. J., and S. W. Franks. "Functional similarity in landscape scale SVAT modelling." Hydrology and Earth System Sciences 3, no. 1 (March 31, 1999): 85–93. http://dx.doi.org/10.5194/hess-3-85-1999.

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Abstract. In this study, it is shown that the complexity of Soil Vegetation Atmosphere Transfer (SVAT) models leads to an equifinality of functional behaviour - many parameterizations from many areas of the parameter space lead to very similar responses. Individual parameters derived by calibration (i.e. model inversion) against limited measurements are, therefore, highly uncertain. Due to the non-linear internal behaviour of SVAT models, aggregation of uncertainly known parameter fields to parameterize landscape scale variability in surface fluxes will yield highly uncertain predictions. A disaggregation approach suggested by Beven (1995) requires that the land surface be represented by a linear sum of a number of representative parameterizations or functional types. This study explores the nature of the parameter space in terms of a simple definition of functional behaviour. Parameter interactions producing similar predicted behaviours are investigated through application of Principal Component Analyses. These reveal the lack of a dominant global interaction indicating the presence of highly complex parameter interactions throughout the feasible parameter space.

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10

Huang, Yu-An, Xing Chen, Zhu-Hong You, De-Shuang Huang, and Keith C. C. Chan. "ILNCSIM: improved lncRNA functional similarity calculation model." Oncotarget 7, no. 18 (March 23, 2016): 25902–14. http://dx.doi.org/10.18632/oncotarget.8296.

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11

FÄUSTLE, S., M. G. FUGINI, and E. DAMIANI. "Retrieval of Reusable Components using Functional Similarity." Software: Practice and Experience 26, no. 5 (May 1996): 491–530. http://dx.doi.org/10.1002/(sici)1097-024x(199605)26:5<491::aid-spe24>3.0.co;2-n.

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12

WAKELING, DAVID. "EDUCATIONAL PEARL: Biological sequence similarity." Journal of Functional Programming 16, no. 1 (September 5, 2005): 1–12. http://dx.doi.org/10.1017/s095679680500571x.

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Functional languages provide an excellent framework for formulating biological algorithms in a naive form and then transforming them into an efficient form. This helps biologists understand what matters about programming and brings functional programming into the realm of the practical. In this column, we present an example from our MSc course on bioinformatics and report on our experiences teaching functional programming in this context.

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13

Pool, Thomas K., Julien Cucherousset, Stéphanie Boulêtreau, Sébastien Villéger, Angela L. Strecker, and Gaël Grenouillet. "Increased taxonomic and functional similarity does not increase the trophic similarity of communities." Global Ecology and Biogeography 25, no. 1 (November 9, 2015): 46–54. http://dx.doi.org/10.1111/geb.12384.

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14

Chen, Qingfeng, Zhao Zhe, Wei Lan, Ruchang Zhang, Zhiqiang Wang, Cheng Luo, and Yi-Ping Pheobe Chen. "Identifying MiRNA-disease association based on integrating miRNA topological similarity and functional similarity." Quantitative Biology 7, no. 3 (August 28, 2019): 202–9. http://dx.doi.org/10.1007/s40484-019-0176-7.

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15

Campbell, Stephen J., Nicola D. Gold, Richard M. Jackson, and David R. Westhead. "Ligand binding: functional site location, similarity and docking." Current Opinion in Structural Biology 13, no. 3 (June 2003): 389–95. http://dx.doi.org/10.1016/s0959-440x(03)00075-7.

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16

Goo, Yoon Hoe. "BOUNDEDNESS IN FUNCTIONAL DIFFERENTIAL SYSTEMS VIA t∞-SIMILARITY." Journal of the Chungcheong Mathematical Society 29, no. 2 (May 15, 2016): 347–59. http://dx.doi.org/10.14403/jcms.2016.29.2.347.

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17

Webster, Jason, and Ione Fine. "Investigating functional organization with Grouping by Response Similarity." Journal of Vision 16, no. 12 (September 1, 2016): 506. http://dx.doi.org/10.1167/16.12.506.

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18

Schlicker, Andreas, and Mario Albrecht. "FunSimMat update: new features for exploring functional similarity." Nucleic Acids Research 38, suppl_1 (November 18, 2009): D244—D248. http://dx.doi.org/10.1093/nar/gkp979.

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19

Schwilk, Dylan W., and David D. Ackerly. "Limiting similarity and functional diversity along environmental gradients." Ecology Letters 8, no. 3 (January 20, 2005): 272–81. http://dx.doi.org/10.1111/j.1461-0248.2004.00720.x.

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20

Goel, Manisha, Deepti Jain, Kanwal J. Kaur, Roopa Kenoth, Bhaskar G. Maiya, Musti J. Swamy, and Dinakar M. Salunke. "Functional Equality in the Absence of Structural Similarity." Journal of Biological Chemistry 276, no. 42 (August 14, 2001): 39277–81. http://dx.doi.org/10.1074/jbc.m105387200.

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21

Yu, Guangchuang, and Qing-Yu He. "Functional similarity analysis of human virus-encoded miRNAs." Journal of Clinical Bioinformatics 1, no. 1 (2011): 15. http://dx.doi.org/10.1186/2043-9113-1-15.

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22

Cheol Jeong, Jong, and Xuewen Chen. "A New Semantic Functional Similarity over Gene Ontology." IEEE/ACM Transactions on Computational Biology and Bioinformatics 12, no. 2 (March 2015): 322–34. http://dx.doi.org/10.1109/tcbb.2014.2343963.

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23

Almrayat, Sondus, Rana Yousef, and Ahmad Sharieh. "Evaluating the Impact of GUI Similarity between Android Applications to Measure their Functional Similarity." International Journal of Computer Applications 178, no. 21 (June 18, 2019): 31–38. http://dx.doi.org/10.5120/ijca2019919075.

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24

Chen, Qingfeng, Zhao Zhe, Wei Lan, Ruchang Zhang, Zhiqiang Wang, Cheng Luo, and Yi-Ping Phoebe Chen. "Erratum to: Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity." Quantitative Biology 8, no. 3 (September 2020): 277. http://dx.doi.org/10.1007/s40484-020-0220-7.

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25

Chen, Xing, Yu-An Huang, Xue-Song Wang, Zhu-Hong You, and Keith C. C. Chan. "FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model." Oncotarget 7, no. 29 (June 14, 2016): 45948–58. http://dx.doi.org/10.18632/oncotarget.10008.

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26

Wang, Shyue-Liang, Jenn-Shing Tsai, and Tzung-Pei Hong. "Discovering functional dependencies from similarity-based fuzzy relational databases." Intelligent Data Analysis 5, no. 2 (March 1, 2001): 131–49. http://dx.doi.org/10.3233/ida-2001-5204.

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27

GOO, YOON HOE. "BOUNDEDNESS IN NONLINEAR FUNCTIONAL DIFFERENTIAL SYSTEMS VIA t∞-SIMILARITY." Pure and Applied Mathematics 22, no. 3 (August 31, 2015): 215–27. http://dx.doi.org/10.7468/jksmeb.2015.22.3.215.

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28

Lee, Chengteh, and Shep Smithline. "An Approach to Molecular Similarity using Density Functional Theory." Journal of Physical Chemistry 98, no. 4 (January 1994): 1135–38. http://dx.doi.org/10.1021/j100055a015.

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29

Nakatani, Hiroshi, Isao Kobayashi, and Tsuguo Miyauchi. "Functional similarity of sea urchin and mammalian α-amylases." Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 115, no. 3 (November 1996): 389–92. http://dx.doi.org/10.1016/s0305-0491(96)00131-9.

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30

Pakeman, Robin J., and Rob J. Lewis. "Functional similarity analysis highlights ecosystem impacts and restoration needs." Applied Vegetation Science 21, no. 2 (January 19, 2018): 258–66. http://dx.doi.org/10.1111/avsc.12353.

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31

Liu, Fang, Bojun Yang, Zhonghang Bai, and Runhua Tan. "Research on product combinatorial design based on functional similarity." International Journal of Design Engineering 1, no. 3 (2008): 333. http://dx.doi.org/10.1504/ijde.2008.023768.

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32

Smith, Ronald L., Erik Gottlieb, Lisa M. Kucharski, and Michael E. Maguire. "Functional Similarity between Archaeal and Bacterial CorA Magnesium Transporters." Journal of Bacteriology 180, no. 10 (May 15, 1998): 2788–91. http://dx.doi.org/10.1128/jb.180.10.2788-2791.1998.

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ABSTRACT The constitutively expressed CorA Mg2+ transporter is the major Mg2+ influx system of Salmonella typhimurium and Escherichia coli. Genomic sequence data indicated the presence of a homolog in the archaeal organismMethanococcus jannaschii. The putative M. jannaschii CorA was expressed in an Mg2+-transport-deficient strain of S. typhimurium to determine its functional characteristics. The archaeal CorA homolog is a functional Mg2+ uptake system when expressed in S. typhimurium and has properties which are highly similar to those of the normal CorA transporter of S. typhimurium despite having a low level of sequence identity with the protein and being expressed in a lipid membrane of quite different composition than normal. This implies that the overall function of the proteins is the same and further suggests that their structures are very similar.

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33

Fukami-Kobayashi, Kaoru, Shirou Tomoda, and Mitiko Gō. "Evolutionary clustering and functional similarity of RNA-binding proteins." FEBS Letters 335, no. 2 (December 6, 1993): 289–93. http://dx.doi.org/10.1016/0014-5793(93)80749-k.

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34

Maji, Pradipta, and Ekta Shah. "Significance and Functional Similarity for Identification of Disease Genes." IEEE/ACM Transactions on Computational Biology and Bioinformatics 14, no. 6 (November 1, 2017): 1419–33. http://dx.doi.org/10.1109/tcbb.2016.2598163.

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35

CHOI, SANG IL, and YOON HOE GOO. "BOUNDEDNESS IN PERTURBED FUNCTIONAL DIFFERENTIAL SYSTEMS VIA t∞-SIMILARITY." Korean Journal of Mathematics 23, no. 2 (June 30, 2015): 269–82. http://dx.doi.org/10.11568/kjm.2015.23.2.269.

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36

Younas, Muhammad, D. N. A. Jawawi, Imran Ghani, and Muhammad Arif Shah. "Extraction of non-functional requirement using semantic similarity distance." Neural Computing and Applications 32, no. 11 (May 22, 2019): 7383–97. http://dx.doi.org/10.1007/s00521-019-04226-5.

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37

Kurtz, Donald M. "Structural similarity and functional diversity in diiron-oxo proteins." JBIC Journal of Biological Inorganic Chemistry 2, no. 2 (April 1997): 159–67. http://dx.doi.org/10.1007/s007750050120.

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38

McAdams, Daniel A., Robert B. Stone, and Kristin L. Wood. "Functional Interdependence and Product Similarity Based on Customer Needs." Research in Engineering Design 11, no. 1 (April 1, 1999): 1–19. http://dx.doi.org/10.1007/s001630050001.

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39

ANTONIADIS, ANESTIS, XAVIER BROSSAT, JAIRO CUGLIARI, and JEAN-MICHEL POGGI. "CLUSTERING FUNCTIONAL DATA USING WAVELETS." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 01 (January 2013): 1350003. http://dx.doi.org/10.1142/s0219691313500033.

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We present two strategies for detecting patterns and clusters in high-dimensional time-dependent functional data. The use on wavelet-based similarity measures, since wavelets are well suited for identifying highly discriminant local time and scale features. The multiresolution aspect of the wavelet transform provides a time-scale decomposition of the signals allowing to visualize and to cluster the functional data into homogeneous groups. For each input function, through its empirical orthogonal wavelet transform the first strategy uses the distribution of energy across scales to generate a representation that can be sufficient to make the signals well distinguishable. Our new similarity measure combined with an efficient feature selection technique in the wavelet domain is then used within more or less classical clustering algorithms to effectively differentiate among high-dimensional populations. The second strategy uses a similarity measure between the whole time-scale representations that is based on wavelet-coherence tools. The clustering is then performed using a k-centroid algorithm starting from these similarities. Practical performance is illustrated through simulations as well as daily profiles of the French electricity power demand.

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40

Pesaranghader, Ahmad, Stan Matwin, Marina Sokolova, and Robert G. Beiko. "simDEF: definition-based semantic similarity measure of gene ontology terms for functional similarity analysis of genes." Bioinformatics 32, no. 9 (December 26, 2015): 1380–87. http://dx.doi.org/10.1093/bioinformatics/btv755.

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41

ALEXANDROV, KIRILL, BORIS SOBOLEV, DMITRY FILIMONOV, and VLADIMIR POROIKOV. "RECOGNITION OF PROTEIN FUNCTION USING THE LOCAL SIMILARITY." Journal of Bioinformatics and Computational Biology 06, no. 04 (August 2008): 709–25. http://dx.doi.org/10.1142/s021972000800359x.

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The functional annotation of amino acid sequences is one of the most important problems in bioinformatics. Different programs have been successfully applied for recognition of some functional classes; nevertheless, many functional groups still cannot be predicted with the required accuracy. We developed a new method for protein function recognition using the original approach of sequence description. Each sequence of the training set is compared with the query sequence, and the local similarity scores are calculated for the query sequence positions and used as input data for the original classifier. The method was tested using leave-one-out cross-validation for three data sets covering 58 enzyme classes. Two tested sets including noncrossing functional classes were recognized with high accuracy at various levels of classification hierarchy. The majority of these classes were predicted with 100% accuracy, showing a prediction ability comparable with the HMMer method and an accuracy superior to the SVM-Prot program. When the tested set was composed of intersected classes of ligand specificity, the prediction accuracy was less; however, the accuracy increased as the size of the predicted class expanded. The proposed method can be used for both predicting protein functional class and selecting the functionally significant sites in a sequence.

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42

Wang, Dong, Juan Wang, Ming Lu, Fei Song, and Qinghua Cui. "Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases." Bioinformatics 26, no. 13 (May 3, 2010): 1644–50. http://dx.doi.org/10.1093/bioinformatics/btq241.

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43

Yang, Yang, Zhichen Wu, and Wei Kong. "Improving Clustering of MicroRNA Microarray Data by Incorporating Functional Similarity." Current Bioinformatics 13, no. 1 (February 19, 2018): 34–41. http://dx.doi.org/10.2174/1574893611666160711162634.

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44

González, Javier, and Alberto Muñoz. "Functional analysis techniques to improve similarity matrices in discrimination problems." Journal of Multivariate Analysis 120 (September 2013): 120–34. http://dx.doi.org/10.1016/j.jmva.2013.04.013.

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45

Kherif, F. "Group analysis in functional neuroimaging: selecting subjects using similarity measures." NeuroImage 20, no. 4 (December 2003): 2197–208. http://dx.doi.org/10.1016/j.neuroimage.2003.08.018.

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46

Atencio, Craig A., Victor Shen, and Christoph E. Schreiner. "Synchrony, connectivity, and functional similarity in auditory midbrain local circuits." Neuroscience 335 (October 2016): 30–53. http://dx.doi.org/10.1016/j.neuroscience.2016.08.024.

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47

Im, Dong Man. "BOUNDEDNESS FOR NONLINEAR PERTURBED FUNCTIONAL DIFFERENTIAL SYSTEMS VIA t∞-SIMILARITY." Journal of the Chungcheong Mathematical Society 29, no. 4 (November 15, 2016): 585–98. http://dx.doi.org/10.14403/jcms.2016.29.4.585.

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48

YU, Zhiwen, Xia CHEN, Jun WANG, Guoxian YU, and Chang LU. "Identifying noisy functional annotations of proteins using sparse semantic similarity." SCIENTIA SINICA Informationis 48, no. 8 (January 31, 2018): 1035–50. http://dx.doi.org/10.1360/n112017-00105.

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49

Inoue, Masayo, Namiko Mitarai, and Ala Trusina. "Circuit architecture explains functional similarity of bacterial heat shock responses." Physical Biology 9, no. 6 (October 31, 2012): 066003. http://dx.doi.org/10.1088/1478-3975/9/6/066003.

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50

Gong, Yan, Wenhui Liu, and James Bristow. "Functional Similarity and Regulatory Interactions between TenascinsX and C.† 250." Pediatric Research 41 (April 1997): 44. http://dx.doi.org/10.1203/00006450-199704001-00270.

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