PRIB 2010: Nijmegen, The Netherlands

Classification of Biological Sequences

• Bastiaan A. van den Berg, Jurgen F. Nijkamp, Marcel J. T. Reinders, Liang Wu, Herman J. Pel, Johannes A. Roubos, Dick de Ridder:
  Sequence-Based Prediction of Protein Secretion Success in Aspergillus niger. 3-14
  
• Gennady G. Fedonin, Mikhail S. Gelfand:
  Machine Learning Study of DNA Binding by Transcription Factors from the LacI Family. 15-26
  
• Jennifer Smith:
  Joint Loop End Modeling Improves Covariance Model Based Non-coding RNA Gene Search. 27-37
  
• Hongyu Su, Markus Heinonen, Juho Rousu:
  Structured Output Prediction of Anti-cancer Drug Activity. 38-49
  
• Norman E. Davey, Niall J. Haslam, Denis C. Shields, Richard J. Edwards:
  SLiMSearch: A Webserver for Finding Novel Occurrences of Short Linear Motifs in Proteins, Incorporating Sequence Context. 50-61
  
• Witold Dyrka, Jean-Christophe Nebel, Malgorzata Kotulska:
  Towards 3D Modeling of Interacting TM Helix Pairs Based on Classification of Helix Pair Sequence. 62-73
  
• Gökhan Yavas, Mehmet Koyutürk, Thomas LaFramboise:
  Optimization Algorithms for Identification and Genotyping of Copy Number Polymorphisms in Human Populations. 74-85
  
• Prem Raj Adhikari, Jaakko Hollmén:
  Preservation of Statistically Significant Patterns in Multiresolution 0-1 Data. 86-97
  
• Christian Widmer, Nora C. Toussaint, Yasemin Altun, Oliver Kohlbacher, Gunnar Rätsch:
  Novel Machine Learning Methods for MHC Class I Binding Prediction. 98-109
  

Unsupervised Learning Methods for Biological Sequences

• Shruthi Prabhakara, Raj Acharya:
  SIMCOMP: A Hybrid Soft Clustering of Metagenome Reads. 113-124
  
• Elijah Myers, Paul S. Fisher, Keith Irwin, Jinsuk Baek, João C. Setubal:
  The Complexity and Application of Syntactic Pattern Recognition Using Finite Inductive Strings. 125-136
  
• Ashish Kishor Bindal, R. Sabarinathan, J. Sridhar, D. Sherlin, Krishna Sekar:
  An Algorithm to Find All Identical Motifs in Multiple Biological Sequences. 137-148
  
• Andrew K. C. Wong, Dennis Zhuang, Gary C. L. Li, En-Shiun Annie Lee:
  Discovery of Non-induced Patterns from Sequences. 149-160
  
• Armando J. Pinho, Sara P. Garcia, Paulo Jorge S. G. Ferreira, Vera Afreixo, Carlos A. C. Bastos, António J. R. Neves, João M. O. S. Rodrigues:
  Exploring Homology Using the Concept of Three-State Entropy Vector. 161-170
  
• Valentina Sulimova, Nikolay Razin, Vadim Mottl, Ilya B. Muchnik, Casimir A. Kulikowski:
  A Maximum-Likelihood Formulation and EM Algorithm for the Protein Multiple Alignment Problem. 171-182
  
• Malte Brinkmeyer, Thasso Griebel, Sebastian Böcker:
  Polynomial Supertree Methods Revisited. 183-194
  
• Vincenzo Bonnici, Alfredo Ferro, Rosalba Giugno, Alfredo Pulvirenti, Dennis Shasha:
  Enhancing Graph Database Indexing by Suffix Tree Structure. 195-203
  

Learning Methods for Gene Expression and Mass Spectrometry Data

• George Lee, Anant Madabhushi:
  Semi-Supervised Graph Embedding Scheme with Active Learning (SSGEAL): Classifying High Dimensional Biomedical Data. 207-218
  
• Wassim Ayadi, Mourad Elloumi, Jin-Kao Hao:
  Iterated Local Search for Biclustering of Microarray Data. 219-229
  
• Alessandro Perina, Pietro Lovato, Vittorio Murino, Manuele Bicego:
  Biologically-aware Latent Dirichlet Allocation (BaLDA) for the Classification of Expression Microarray. 230-241
  
• Beatriz Pontes, Raúl Giráldez, Jesús S. Aguilar-Ruiz:
  Measuring the Quality of Shifting and Scaling Patterns in Biclusters. 242-252
  
• Ronnie Bathoorn, Monique C. M. Welten, Michael Richardson, Arno Siebes, Fons J. Verbeek:
  Frequent Episode Mining to Support Pattern Analysis in Developmental Biology. 253-263
  
• Carlotta Orsenigo, Carlo Vercellis:
  Time Series Gene Expression Data Classification via L₁-norm Temporal SVM. 264-274
  

Bioimaging

• Iman Rezaeian, Luis Rueda:
  Sub-grid and Spot Detection in DNA Microarray Images Using Optimal Multi-level Thresholding. 277-288
  
• Shiwen Zhu, Paul Matsudaira, Roy Welsch, Jagath C. Rajapakse:
  Quantification of Cytoskeletal Protein Localization from High-Content Images. 289-300
  
• Alexander E. Nezhinsky, Fons J. Verbeek:
  Pattern Recognition for High Throughput Zebrafish Imaging Using Genetic Algorithm Optimization. 301-312
  
• Scott Doyle, Anant Madabhushi:
  Consensus of Ambiguity: Theory and Application of Active Learning for Biomedical Image Analysis. 313-324
  
• Fabian Ojeda, Marco Signoretto, Raf Van de Plas, Etienne Waelkens, Bart De Moor, Johan A. K. Suykens:
  Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging. 325-334
  

Molecular Structure Prediction

• S. P. T. Krishnan, Mushfique Junayed Khurshid, Bharadwaj Veeravalli:
  A Matrix Algorithm for RNA Secondary Structure Prediction. 337-348
  
• Yizhao Ni, Mahesan Niranjan:
  Exploiting Long-Range Dependencies in Protein beta-Sheet Secondary Structure Prediction. 349-357
  
• Alfonso E. Márquez Chamorro, Federico Divina, Jesús S. Aguilar-Ruiz, Gualberto Asencio Cortés:
  Alpha Helix Prediction Based on Evolutionary Computation. 358-367
  
• Stefano Teso, Cristina Di Risio, Andrea Passerini, Roberto Battiti:
  An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps. 368-379
  

Protein Protein Interaction and Network Inference

• Luis Rueda, Carolina Garate, Sridip Banerjee, Md. Mominul Aziz:
  Biological Protein-Protein Interaction Prediction Using Binding Free Energies and Linear Dimensionality Reduction. 383-394
  
• Kristine A. Pattin, Jiang Gui, Jason H. Moore:
  Employing Publically Available Biological Expert Knowledge from Protein-Protein Interaction Information. 395-406
  
• Fabrício Martins Lopes, David Correa Martins Jr., Junior Barrera, Roberto M. Cesar:
  SFFS-MR: A Floating Search Strategy for GRNs Inference. 407-418
  
• Frédéric Cazals:
  Revisiting the Voronoi Description of Protein-Protein Interfaces: Algorithms. 419-430
  
• Daniel James Barker, Steven M. Hill, Sach Mukherjee:
  MC⁴: A Tempering Algorithm for Large-Sample Network Inference. 431-442
  
• Nicolas Brunel, Florence d'Alché-Buc:
  Flow-Based Bayesian Estimation of Nonlinear Differential Equations for Modeling Biological Networks. 443-454
  

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