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A Note On "a Branch-and-prune Algorithm For The Molecular Distance Geometry Problem"
No full text[No abstract available]186751752Liberti, L., Lavor, C., Maculan, N., A Branch-and-Prune algorithm for the Molecular Distance Geometry Problem (2008) International Transactions in Operational Research, 15, pp. 1-1
Computing Artificial Backbones Of Hydrogen Atoms In Order To Discover Protein Backbones
No full textNMR experiments are able to provide some of the distances between pairs of hydrogen atoms in molecular conformations. The problem of finding the coordinates of such atoms is known as the molecular distance geometry problem. This problem can be reformulated as a combinatorial optimization problem and efficiently solved by an exact algorithm. To this purpose, we show how an artificial backbone of hydrogens can be generated that satisfies some assumptions needed for having the combinatorial reformulation. Computational experiments show that the combinatorial approach to this problem is very promising. © 2009 IEEE.4759764Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E., The protein data bank (2000) Nucleic Acids Research, 28, pp. 235-242Biswas, P., Toh, K.-C., Ye, Y., A distributed sDP approach for large-scale noisy anchor-free graph realization with applications to molecular conformation (2008) SIAM Journal on Scientific Computing, 30, pp. 1251-1277Crippen, G.M., Havel, T.F., (1988) Distance Geometry and Molecular Conformation, , John Wiley & Sons, New YorkDong, Q., Wu, Z., A linear-time algorithm for solving the molecular distance geometry problem with exact inter-atomic distances (2002) Journal of Global Optimization, 22, pp. 365-375Havel, T.F., Distance geometry (1995) Encyclopedia of Nuclear Magnetic Resonance, pp. 1701-1710. , D.M. Grant and R.K. Harris (Eds.), Wiley, New YorkLavor, C., Liberti, L., Maculan, N., Molecular distance geometry problem (2009) Encyclopedia of Optimization, pp. 2305-2311. , C. Floudas and P. Pardalos (Eds.), 2nd edition, Springer, New YorkLavor, C., Liberti, L., Maculan, N., Discretizable molecular distance geometry problem (2006) Tech. Rep. Q-bio.BM/0608012, , arXivLavor, C., Liberti, L., Mucherino, A., Maculan, N., On a discretizable subclass of instances of the molecular distance geometry problem (2009) ACM Conference Proceedings, 24th Annual ACM Symposium on Applied Computing (SAC09), pp. 804-805. , Hawaii USALiberti, L., Lavor, C., Maculan, N., A branch-and-prune algorithm for the molecular distance geometry problem (2008) International Transactions in Operational Research, 15 (1), pp. 1-17Mucherino, A., Liberti, L., Lavor, C., Maculan, N., Comparisons between an exact and a metaheuristic algorithm for the molecular distance geometry problem (2009) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO09), , Montréal, Canada, JulyProtein Data Bank, , http://www.rcsb.org/pdb/Schlick, T., (2002) Molecular Modelling and Simulation: An Interdisciplinary Guide, , Springer, New YorkWu, D., Wu, Z., An updated geometric build-up algorithm for solving the molecular distance geometry problem with sparse distance data (2007) Journal of Global Optimization, 37, pp. 661-67
On Suitable Orders For Discretizing Molecular Distance Geometry Problems Related To Protein Side Chains
No full textProteins are important molecules that are widely studied in biology. Their three-dimensional conformations can give clues about their function, however an optimal methodology for the identification of such conformations has not been found yet. Experiments of Nuclear Magnetic Resonance (NMR) are able to estimate distances between some pairs of atoms forming the protein, and the problem of identifying the possible conformations satisfying the available distance constraints is known in the scientific literature as the Molecular Distance Geometry Problem (MDGP). Since some years, some of us have been working on a suitable discretization for the MDGP and on an efficient Branch & Prune (BP) algorithm which is based on a tree search. In order to perform this discretization, however, some assumptions need to be satisfied. We recently hand-crafted a special order for protein backbone atoms which allows us to discretize all MDGPs concerning backbones. In this paper, we do the same for the side chains of some amino acids. Our computational experiments show that the inclusion of the side chain information allows to improve the performances of the BP algorithm. © 2012 Polish Info Processing Socit.379384Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I., Bourne, P., The protein data bank (2000) Nucleic Acids Research, 28, pp. 235-242Crippen, G., Havel, T., (1988) Distance Geometry and Molecular Conforma-Tion, , John Wiley & Sons, New YorkLavor, C., Liberti, L., MacUlan, N., Mucherino, A., The discretizable molecular distance geometry problem (2012) Computational Optimization and Applications, 52, pp. 115-146Lavor, C., Liberti, L., MacUlan, N., Mucherino, A., Recent advances on the discretizable molecular distance geometry problem (2012) European Journal of Operational Research, 219, pp. 698-706Lavor, C., Liberti, L., Mucherino, A., The interval Branch-and-Prune Algorithm for the Discretizable Molecular Distance Geometry Problem with Inexact Distances (2011) To Appear in Journal of Global OptimizationLiberti, L., Lavor, C., MacUlan, N., A branch-and-prune algorithm for the molecular distance geometry problem (2008) International Transactions in Operational Research, 15, pp. 1-17Liberti, L., Lavor, C., Mucherino, A., MacUlan, N., Molecular distance geometry methods: From continuous to discrete (2010) International Transactions in Operational Research, 18, pp. 33-51Liberti, L., Masson, B., Lavor, C., Mucherino, A., Branch-and-prune trees with bounded width (2011) Proceedings of the 10th Cologne-Twente Workshop on Graphs and Combinatorial Optimization (CTW11), pp. 189-193. , Rome, ItalyNilges, M., Gronenborn, A., Brunger, A., Clore, G., Determination of three-dimensional structures of proteins by simulated annealing with interproton distance restraints. application to crambin (1988) Potato Carboxypeptidase Inhibitor and Barley Serine Proteinase Inhibitor 2, Protein Engineering, 2, pp. 27-38More, J., Wu, Z., Distance geometry optimization for protein structures (1999) Journal of Global Optimization, 15, pp. 219-234Mucherino, A., Lavor, C., Malliavin, T., Liberti, L., Nilges, M., MacUlan, N., Influence of pruning devices on the solution of molecular distance geometry problems (2011) Lecture Notes in Computer Science 6630, Proceedings of the 10th International Symposium on Experimental Algorithms (SEA11), pp. 206-217. , P.M. Pardalos and S. Rebennack (Eds.) Crete, GreeceSaxe, J., Embeddability of Weighted Graphs in k-Space is Strongly NP-hard (1979) Proceedings of 17th Allerton Conference in Communications, Control and Computing, pp. 480-48
Influence Of Pruning Devices On The Solution Of Molecular Distance Geometry Problems
No full textThe Molecular Distance Geometry Problem (MDGP) is the problem of finding the conformation of a molecule from inter-atomic distances. In some recent work, we proposed the interval Branch & Prune (iBP) algorithm for solving instances of the MDGP related to protein backbones. This algorithm is based on an artificial ordering given to the atoms of the protein backbones which allows the discretization of the problem, and hence the applicability of the iBP algorithm. This algorithm explores a discrete search domain having the structure of a tree and prunes its infeasible branches by employing suitable pruning devices. In this work, we use information derived from Nuclear Magnetic Resonance (NMR) to conceive and add new pruning devices to the iBP algorithm, and we study their influence on the performances of the algorithm. © 2011 Springer-Verlag.6630 LNCS206217Berg, J.M., Tymoczko, J.L., Stryer, L., (2006) Biochemistry, , 6th edn. W.H. Freeman publications, New YorkBerman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E., The protein data bank (2000) Nucleic Acid Research, 28, pp. 235-242Lavor, C., Liberti, L., Maculan, N., (2006) The Discretizable Molecular Distance Geometry Problem, , Technical Report q-bio/0608012, arXivLavor, C., Liberti, L., Maculan, N., Molecular distance geometry problem (2009) Encyclopedia of Optimization, pp. 2305-2311. , Floudas, C., Pardalos, P. (eds.) 2nd edn., Springer, New YorkLavor, C., Liberti, L., Mucherino, A., On the solution of molecular distance geometry problems with interval data (2010) IEEE Conference Proceedings, International Workshop on Computational Proteomics, International Conference on Bioinformatics & Biomedicine (BIBM 2010), Hong Kong, pp. 77-82Lavor, C., Liberti, L., Mucherino, A., The iBP Algorithm for the Discretizable Molecular Distance Geometry Problem with Interval Data, , (submitted) (Available on Optimization Online)Lavor, C., Mucherino, A., Liberti, L., Maculan, N., On the computation of protein backbones by using artificial backbones of hydrogens (2011) Journal of Global Optimization, , To appear in Available online from July 24, 2010Lavor, C., Mucherino, A., Liberti, L., Maculan, N., Discrete approaches for solving molecular distance geometry problems using NMR data (2010) International Journal of Computational Biosciences, 1 (1), pp. 88-94Liberti, L., Lavor, C., Maculan, N., A branch-and-prune algorithm for the molecular distance geometry problem (2008) International Transactions in Operational Research, 15, pp. 1-17Liberti, L., Lavor, C., Mucherino, A., Maculan, N., Molecular distance geometry methods: From continuous to discrete (2010) International Transactions in Operational Research, 18 (1), pp. 33-51Mielke, S.P., Krishnan, V.V., An evaluation of chemical shift index-based secondary structure determination in proteins: Influence of random coil chemical shifts (2004) Journal of Biomolecular NMR, 30 (2), pp. 143-196Mucherino, A., Lavor, C., The branch and prune algorithm for the molecular distance geometry problem with inexact distances (2009) Proceedings of the International Conference on Computational Biology, 58, pp. 349-353. , World Academy of Science, Engineering and TechnologyMucherino, A., Lavor, C., Liberti, L., The Discretizable Distance Geometry Problem, , submittedMucherino, A., Liberti, L., Lavor, C., Maculan, N., Comparisons between an exact and a metaheuristic algorithm for the molecular distance geometry problem (2009) Proceedings of the Genetic and Evolutionary Computation Conference, Montreal, pp. 333-340. , Rothlauf, F. (ed.) ACM, New YorkNilges, M., Gronenborn, A.M., Brunger, A.T., Clore, G.M., Determination of three-dimensional structures of proteins by simulated annealing with interproton distance restraints. application to crambin, potato carboxypeptidase inhibitor and barley serine proteinase inhibitor 2 (1988) Protein Engineering, 2, pp. 27-38Saxe, J.B., Embeddability of weighted graphs in k-space is strongly NP-hard (1979) Proceedings of 17th Allerton Conference in Communications, Control and Computing, pp. 480-489Shen, Y., Delaglio, F., Cornilescu, G., Bax, A., TALOS+: A hybrid method for predicting protein backbone torsion angles from NMR chemical shifts (2009) Journal of Biomolecular NMR, 44 (4), pp. 213-236Wishart, D.S., Sykes, B.D., Richards, F.M., The chemical shift index: A fast and simple method for the assignment of protein secondary structure through NMR spectroscopy (1992) Biochemistry, 31 (6), pp. 1647-169
Multiobjective combinatorial optimization problems with a cost and several bottleneck objective functions: An algorithm with reoptimization
No full textThis paper addresses multicriteria combinatorial optimization problems involving one cost and several bottleneck objective functions. An algorithm is developed which generates the minimal complete set of Pareto-optimal solutions. This algorithm runs in polynomial time as long as the single objective problem considering only the cost function can be solved polynomially. A reoptimization procedure is used to accelerate the convergence of the algorithm. Applications are given. Computational results on randomly generated instances and planar grid graphs concerning the minimum cost spanning tree and the shortest path problem are presented. © 2011 Elsevier Ltd
An Artificial Backbone Of Hydrogens For Finding The Conformation Of Protein Molecules
No full textNMR experiments can provide distances between pairs of hydrogens of a protein molecule. The problem of identifying the coordinates of such hydrogens by exploiting the information on the distances is a Molecular Distance Geometry Problem (MDGP). In a previous work, we defined an artificial backbone of hydrogens related to the protein backbones, where a particular ordering was given to the hydrogens. This ordering allows to formulate the MDGP as a combinatorial optimization problem, to which we refer as the Discretizable MDGP (DMDGP) and that we efficiently solve by an exact algorithm, the Branch and Prune (BP) algorithm. Once the coordinates of the hydrogens have been found, the problem of finding the remaining backbone atoms (N, C and C) is another MDGP. In this short paper, we propose a simple method for solving the MDGP related to the backbone atoms N, C and C of a protein, where the coordinates of the hydrogens previously found by the BP algorithm are exploited. ©2009 IEEE.152155Crippen, G.M., Havel, T.F., (1988) Distance Geometry and Molecular Conformation, , John Wiley & Sons, New YorkDong, Q., Wu, Z., A Linear-Time Algorithm for Solving the Molecular Distance Geometry Problem with Exact Inter-Atomic Distances (2002) Journal of Global Optimization, 22, pp. 365-375Havel, T.F., Distance Geometry (1701) Encyclopedia of Nuclear Magnetic Resonance, , D.M. Grant and R.K. Harris Eds, Wiley, New York, 1995C. Lavor, On generating Instances for the Molecular Distance Geometry Problem, In: Global Optimization Prom Theory to Implementation, Leo Liberti and Nelson Maculan (Eds.), Series: Nonconvex Optimization and Its Applications 84, Springer, 405-414, 2006C. Lavor, L. Liberti, and N. Maculan, Discretizable Mnlecular Distance Geometry Problem, Tech. Rep. q-bio.BM/0608012, arXiv, 2006Lavor, C., Liberti, L., Maculan, N., Molecular Distance Geometry Problem (2009) Encyclopedia of Optimization, pp. 2305-2311. , C. Floudas and P. Pardalos Eds, 2ïï edition, Springer, New YorkC. Lavor, L. Liberti, A. Mucherino, and N. Maculan, On a Discretizable Subclass of Instances of the Molecular Distance Geometry Problem, ACM Conference Proceedings, 24ïï Annual ACM Symposium on Applied Computing (SAC09), Hawaii USA, 804-805, 2009Lavor, C., Mucherino, A., Liberti, L., Maculan, N., Computing Artificial Backbones of Hydrogen Atoms in order to Discover Protein Backbones (2009) IEEE Conference Proceedings, International Conference IM-CSITO9, Workshop on Combinatorial Optimization (WCOO9), , Poland, OctoberLiberti, L., Lavor, C., Maculan, N., A Branch-and-Prune Algorithm for the Molecular Distance Geometry Problem (2008) International Transactions in Operational Research, 15 (1), pp. 1-17A. Mucherino, C. Lavor, The Branch and Prune Algorithm for the Molecular Distance Geometry Problem with Inexact Distances, World Academy of Science, Engineering and Technology (WASET), Proceedings of the International Conference on Bioinformatics and Biomedicine (ICBBO9), Venice, Italy, October 2009A. Mucherino, C. Lavor, and N. Maculan, The Molecular Distance Geometry Problem Applied to Protein Conformations, Proceedings of the 8 ïï Cologne-Twente Workshop on Graphs and Combinatorial Optimization (CTWO9), S. Cafieri, A. Mucherino, G. Nannicini, P. Tarissan, L. Liberti (Eds.), 337-340, Paris, 2009Mucherino, A., Liberti, L., Lavor, C., Maculan, N., Comparisons between an Exact and a MetaHeuristic Algorithm for the Molecular Distance Geometry Problem (2009) ACM Conference Proceedings, Genetic and Evolutionary Computation Conference (GECCOO9), pp. 333-340. , Montréal, CanadaJ.B. Saxe, Embeddability of Weighted Graphs in k-space is Strongly NP-hard, Proceedings of 17ïï Allerton Conference in Communications, Control, and Computing, Monticello, IL, 480-489, 1979Schlick, T., (2002) Molecular Modelling and Simulation: An Interdisciplinary Guide, , Springer, New YorkWu, D., Wu, Z., An Updated Geometric Build-Up Algorithm for Solving the Molecular Distance Geometry Problem with Sparse Distance Data (2007) Journal of Global Optimization, 37, pp. 661-67
A Parallel Bp Algorithm For The Discretizable Distance Geometry Problem
No full textWe propose a parallel version of the Branch & Prune (BP) algorithm for the Discretizable Distance Geometry Problem (DDGP), which consists in a subclass of Distance Geometry Problems (DGPs) that can be discretized. The main idea is to split a DDGP instance in as many sub instances as the number of processors involved in the computation, and to invoke the sequential version of BP on each processor. Due to the flexibility of the discretizing orderings that can be defined on the vertex sets of graphs G representing DDGP instances, the subdivision of the original instance can be performed so that all solutions generated by locally solving the several sub instances are represented in a common coordinate system. This way, the communication phase of the parallel algorithm, where the local solutions are combined in order to generate the final set of solutions, is very efficient. We present some preliminary computational experiments and we study the behavior of the algorithm in relation to the number of considered processors. We also give some directions for transforming DDGP instances in parallelizable instances, and to modify them in order to improve the efficiency of the proposed parallel algorithm. © 2012 IEEE.17621768 IEEE Computer Society Technical Committee on Parallel ProcessingAloise, D., Cafieri, S., Caporossi, G., Hansen, P., Liberti, L., Perron, S., Column Generation Algorithms for Exact Modularity Maximization in Networks (2010) Physical Review E, 82, p. 046112Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E., The Protein Data Bank (2000) Nucleic Acids Research, 28, pp. 235-242Biswas, P., Toh, K.C., Ye, Y., A distributed SDP Approach for Large-Scale Noisy Anchor-Free Graph Realization with Applications to Molecular Conformation (2008) SIAM Journal on Scientific Computing, 30, pp. 1251-1277Cafieri, S., Hansen, P., Liberti, L., Locally Optimal Heuristic for Modularity Maximization of Networks (2011) Physical Review E, 83, p. 056105Cafieri, S., Hansen, P., Liberti, L., Loops and Multiple Edges in Modularity Maximization of Networks (2010) Physical Review E, 81, p. 046102Coope, I.D., Reliable Computation of the Points of Intersection of n Spheres in n-space (2000) ANZIAM Journal, 42, pp. 461-477Crippen, G.M., Havel, T.F., (1988) Distance Geometry and Molecular Conformation, , John Wiley & Sons, New YorkDong, Q., Wu, Z., A Geometric Build-Up Algorithm for Solving the Molecular Distance Geometry Problem with Sparse Distance Data (2003) Journal of Global Optimization, 26, pp. 321-333Havel, T.F., Distance Geometry (1995) Encyclopedia of Nuclear Magnetic Resonance, pp. 1701-1710. , D.M. Grant and R.K. Harris (Eds.), Wiley, New YorkKrislock, N., Wolkowicz, H., Explicit Sensor Network Localization using Semidefinite Representations and Facial Reductions (2010) SIAM Journal on Optimization, 20, pp. 2679-2708Lavor, C., Lee, J., Lee-St.John, A., Liberti, L., Mucherino, A., Sviridenko, M., Discretization Orders for Distance Geometry Problems (2012) Optimization Letters, , to appearLavor, C., Liberti, L., Maculan, N., Mucherino, A., The Discretizable Molecular Distance Geometry Problem (2012) Computational Optimization and Applications, , to appearLavor, C., Liberti, L., Maculan, N., Mucherino, A., Recent Advances on the Discretizable Molecular Distance Geometry Problem (2012) European Journal of Operational Research, 219, pp. 698-706Lavor, C., Liberti, L., Mucherino, A., On the Solution of Molecular Distance Geometry Problems with Interval Data (2010) IEEE Conference Proceedings, International Workshop on Computational Proteomics, International Conference on Bioinformatics & Biomedicine (BIBM10), Hong Kong, pp. 77-82Lavor, C., Liberti, L., Mucherino, A., The interval Branch-and-Prune Algorithm for the Discretizable Molecular Distance Geometry Problem with Inexact Distances (2012) Journal of Global Optimization, , to appearLavor, C., Liberti, L., Mucherino, A., Maculan, N., On a Discretizable Subclass of Instances of the Molecular Distance Geometry Problem (2009) ACM Conference Proceedings, 24 th Annual ACM Symposium on Applied Computing, Hawaii, USA, pp. 804-805Lavor, C., Mucherino, A., Liberti, L., Maculan, N., On the Computation of Protein Backbones by using Artificial Backbones of Hydrogens (2011) Journal of Global Optimization, 50, pp. 329-344Liberti, L., Lavor, C., Maculan, N., A Branch-and-Prune Algorithm for the Molecular Distance Geometry Problem (2008) International Transactions in Operational Research, 15, pp. 1-17Liberti, L., Lavor, C., Mucherino, A., An Exponential Algorithm for the Discretizable Molecular Distance Geometry Problem is Polynomial on Proteins Proceedings of the 7 th International Symposium on Bioinformatics Research and Applications (ISBRA11), Changsha, China, May 2011Liberti, L., Lavor, C., Mucherino, A., Maculan, N., Molecular Distance Geometry Methods: From Continuous to Discrete (2010) International Transactions in Operational Research, 18, pp. 33-51Liberti, L., Masson, B., Lavor, C., Mucherino, A., Branch-and-Prune Trees with Bounded Width (2011) Proceedings of the 10 th Cologne-Twente Workshop on Graphs and Combinatorial Optimization (CTW11), Rome, Italy, pp. 189-193Liberti, L., Masson, B., Lee, J., Lavor, C., Mucherino, A., On the Number of Solutions of the Discretizable Molecular Distance Geometry Problem (2011) Lecture Notes in Computer Science, 6831, pp. 322-342. , Proceedings of the 5 th Annual International Conference on Combinatorial Optimization and Applications (COCOA11), Zhangjiajie, ChinaNoack, A., Rotta, R., Multi-level Algorithms for Modularity Clustering (2009) Lecture Notes in Computer Science, 5526, pp. 257-268Mucherino, A., Lavor, C., Liberti, L., The Discretizable Distance Geometry Problem (2012) Optimization Letters, , to appearMucherino, A., Lavor, C., Liberti, L., Talbi, E.-G., A Parallel Version of the Branch & Prune Algorithm for the Molecular Distance Geometry Problem (2010) IEEE Conference Proceedings, ACS/IEEE International Conference on Computer Systems and Applications (AICCSA10), Hammamet, Tunisia, pp. 1-6Mucherino, A., Lavor, C., Malliavin, T., Liberti, L., Nilges, M., Maculan, M., Influence of Pruning Devices on the Solution of Molecular Distance Geometry Problems (2011) Lecture Notes in Computer Science, 6630, pp. 206-217. , P.M. Pardalos and S. Rebennack (Eds.), Proceedings of the 10th International Symposium on Experimental Algorithms (SEA11), Crete, GreeceMucherino, A., Liberti, L., Lavor, C., MD-jeep: An Implementation of a Branch & Prune Algorithm for Distance Geometry Problems (2010) Lectures Notes in Computer Science, 6327, pp. 186-197. , K. Fukuda et al. (Eds.), Proceedings of the Third International Congress on Mathematical Software (ICMS10), Kobe, JapanNilges, M., Gronenborn, A.M., Brunger, A.T., Clore, G.M., Determination of Three-Dimensional Structures of Proteins by Simulated Annealing with Interproton Distance Restraints. Application to Crambin, Potato Carboxypeptidase Inhibitor and Barley Serine Proteinase Inhibitor 2 (1988) Protein Engineering, 2, pp. 27-38Saxe, J.B., Embeddability of Weighted Graphs in κ-Space is Strongly NP-hard (1979) Proceedings of 17 th Allerton Conference in Communications, Control and Computing, pp. 480-489Wu, D., Wu, Z., Yuan, Y., Rigid Versus Unique Determination of Protein Structures with Geometric Buildup (2008) Optimization Letters, 2, pp. 319-33
Euclidean Distance Geometry And Applications
No full textEuclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consist of an incomplete set of distances and the output is a set of points in Euclidean space realizing those given distances. We survey the theory of Euclidean distance geometry and its most important applications, with special emphasis on molecular conformation problems. © 2014 Society for Industrial and Applied Mathematics.561369Alexandrov, A., (1950) Convex Polyhedra, Gosudarstv. Izdat. Tekhn.-Theor. Lit., , MoscowAlfakih, A., Khandani, A., Wolkowicz, H., Solving Euclidean distance matrix completion problems via semidefinite programming (1999) Comput. Optim. Appl., 12, pp. 13-30Alves, R., Cassioli, A., Mucherino, A., Lavor, C., Liberti, L., Adaptive branching in iBP with Clifford algebra (2013) Proceedings of the Workshop on Distance Geometry and Applications, pp. 65-69. , A. Andrioni, C. Lavor, L. Liberti, A. 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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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