Cargando…

Clustering challenges in biological networks /

This text offers introductory knowledge of a wide range of clustering and other quantitative techniques used to solve biological problems.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: DIMACS Workshop on Clustering Problems in Biological Networks
Otros Autores: Butenko, Sergiy, Chaovalitwongse, W. Art, Pardalos, P. M. (Panos M.), 1954-
Formato: Electrónico Congresos, conferencias eBook
Idioma:Inglés
Publicado: New Jersry : World Scientific, ©2009.
Temas:
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Fixed-parameter algorithms for graph-modeled data clustering / F. Hüffner, R. Niedemeier and S. Wernicke
  • Probabilistic distance clustering : algorithm and applications / C. Iyigun and A. Ben-Israel
  • Analysis of regulatory and interaction networks from clusters of co-expressed genes / E. Yang [and others]
  • Graph-based approaches for motif discovery / E. Zaslavsky
  • Statistical clustering analysis : an introduction / H. Zhang
  • Diversity graphs / P. Blain [and others]
  • Identifying critical nodes in protein-protein interaction networks / V. Boginski and C.W. Commander
  • Faster algorithms for constructing a concept (Galois) lattice / V. Choi
  • A projected clustering algorithm and its biomedical application / P. Deng, Q. Ma and W. Wu
  • Graph algorithms for integrated biological analysis, with applications to Type 1 diabetes data / J.D. Eblen [and others]
  • A novel similarity-based modularity function for graph partitioning / Z. Feng [and others]
  • Mechanism-based clustering of genome-wide RNA levels : roles of transcription and transcript-degradation rates / S. Ji [and others]
  • The complexity of feature selection for consistent biclustering / O.E. Kundakcioglu and P.M. Pardalos
  • Clustering electroencephalogram recordings to study mesial temporal lobe epilepsy / C.-C. Liu [and others]
  • Relating subjective and objective pharmacovigilance association measures / R.K. Pearson
  • A novel clustering approach : global optimum search with enhanced positioning / M.P. Tan and C.A. Floudas.