publications

bioinformatics / machine learning:

• A. Sokolov and A. Ben-Hur. Hierarchical classification of Gene Ontology terms using the GOstruct method. Submitted, JBCB. [ preprint ]
• A. Sokolov and A. Ben-Hur. GOstruct: utilizing the structure of the Gene Ontology for accurate prediction of protein function. 8th Annual International Conference on Computational System Bioinformatics (CSB2009). An extended version is available [ here ]
• M. Rogers and A. Ben-Hur. The use of Gene Ontology evidence codes in preventing classifier assessment bias. Bioinformatics 25(9):1173-1177, 2009. [ preprint ]
• A. Ben-Hur, C-S. Ong, S. Sonnenburg, B. Schoelkopf, and G. Raetsch. Support vector machines and kernels for computational biology. PLoS Computational Biology 4(10): e1000173, 2008. [ web supplement ]
• H. Wang, E. Segal, A. Ben-Hur, Q. Li, M. Vidal and D. Koller. InSite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale. Genome Biology, 8(9): R192, 2007.
• J. Qiu, Martial Hue, A. Ben-Hur, J-P. Vert and W.S. Noble. An alignment kernel for protein structures. Bioinformatics 23(9): 1090-1098, 2007.
• W.S. Noble and A. Ben-Hur. Integrating information for protein function prediction. In: Bioinformatics - From Genomes to Therapies Vol. 3. Thomas Lengauer (ed.) 2007 pp. 1297-1314.
• S.Y. Rhee, J. Taylor, G. Wadhera, A. Ben-Hur, D. Brutlag, and R.W. Shafer. Genotypic predictors of human immonodeficiency virus type 1 drug resistance. PNAS 103(46): 17355-17360, 2006.
• A. Ben-Hur and W.S. Noble. Choosing negative examples for the prediction of protein-protein interactions. BMC Bioinformatics 7(Suppl 1), 2006.
• A. Ben-Hur and D. Brutlag. Protein sequence motifs: Highly predictive features of protein function. In: Feature extraction, foundations and applications. I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh (eds.) Springer Verlag, 2006. [ publisher's version ]
• J.P. Miller, R.S. Lo, A. Ben-Hur, C. Desmarais, I. Stagljar, W.S. Noble and S. Fields. Large-scale identification of yeast integral membrane protein interactions. PNAS 102(34): 12123-12128, 2005.
• A. Ben-Hur and W.S. Noble. Kernel methods for predicting protein-protein interactions. In: Proceedings, thirteenth international conference on intelligent systems for molecular biology. Bioinformatics 21 Suppl. 1: i38-i46, 2005.
• H. Wang, E. Segal, A. Ben-Hur, D. Brutlag and D. Koller. Identifying protein-protein interaction sites on a genome-wide scale. Advances in Neural Information Processing Systems 17, 2004.
• I. Guyon, S.R. Gunn, A. Ben-Hur and G. Dror. Results analysis of the NIPS 2003 feature selection challenge. Advances in Neural Information Processing Systems 17, 2004.
• R. Sharan, A. Ben-Hur, G. Loots and I. Ovcharenko.   CREME: cis-regulatory module explorer for the human genome.  Nucleic Acids Research 32: W83-W88, 2004. See also the Creme website.
• A. Ben-Hur and D. Brutlag.  Remote homology detection:  a motif based approach.  In: Proceedings, eleventh international conference on intelligent systems for molecular biology. Bioinformatics 19 Suppl. 1: i26-i33, 2003.
• R. Sharan, I. Ovcharenko, A. Ben-Hur and R.M. Karp.  CREME:  A framework for identifying cis-regulatory modules in human-mouse conserved segments.  In: Proceedings, eleventh international conference on intelligent systems for molecular biology. Bioinformatics 19 Suppl. 1: i283-i291, 2003.
• A. Ben-Hur and I. Guyon.  Detecting stable clusters using principal component analysis. In Methods in Molecular Biology, M.J. Brownstein and A. Khodursky (eds.) Humana press, 2003 pp. 159-182.
• A. Ben-Hur, A. Elisseeff and I. Guyon. A stability based method for discovering structure in clustered data. Pacific Symposium on Biocomputing, 2002.
• A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik. Support vector clustering. Journal of Machine Learning Research 2:125-137, 2001.
• A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik. A support vector method for clustering.  Advances in Neural Information Processing Systems 13, 367-373, 2001.
• A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik. A kernel clustering method. 15th International Conference on Pattern Recognition (ICPR), 728-731, 2000.

analog computation:

• A. Ben-Hur, J. Feinberg, S. Fishman and H.T. Siegelmann. Probabilistic analysis of a differential equation for linear programming . Journal of Complexity 19(4) : 474-510, 2003.
• A. Roitershtein, A. Ben-Hur and H.T. Siegelmann. On probabilistic analog automata . Theoretical Computer Science 320(2-3):449-464, 2004.
• A. Ben-Hur, H.T. Siegelmann and S. Fishman. Complexity for continuous time systems. Journal of Complexity 18(1) : 51-86, 2002.
• H.T. Siegelmann, A. Ben-Hur  and S. Fishman. Computational complexity for continuous time dynamics. Physical Review Letters, 83(7):1463-1466, 1999.
• A. Ben-Hur and H.T. Siegelmann. Computation in gene networks. in: M. Margenstern and Y. Rogozhin (Eds.): MCU 2001, LNCS 2055, pp. 11-24, 2001.
• H.T. Siegelmann, A. Roitershtein, and A. Ben-Hur. Noisy neural networks and generalizations. In Advances in Neural Information Processing Systems 12, Cambridge, MA, 2000. MIT Press.

physics:

• A. Ben-Hur, R. Hallgass  and V. Loreto. A renormalization procedure for directed self-organized critical models. Physical Review E, 54:1426-1432, 1996.
• A. Ben-Hur and O. Biham. Universality in sandpile models. Physical Review E, 53:1317-1321, 1996.