Major topics of research. 1. Clustering: QC and DQC We have developed the Quantum Clustering (QC) method in NIPS01 and PRL 2002. This has since been applied to various problems, mostly in bioinformatics, several of which are listed below. The algorithm is incorporated in the Matlab program COMPACT that can be downloaded from the Research section of my website. Recently we have developed the Dynamic Quantum Clustering method (PRL 2009). It can be tested on the webtool http://dqc.cs.tau.ac.il/dqc which runs a MapleNet application (only one user at a time…). References (downloadable from the section Publications my website): The Method of Quantum Clustering. (David Horn and Assaf Gottlieb) in proceedings of NIPS*01 Algorithm for data clustering in pattern recognition problems based on quantum mechanics (David Horn and Assaf Gottlieb) Phys. Rev. Lett. 88 (2002) 18702 Novel clustering algorithm for microarray expression data in a truncated SVD space (David Horn and Inon Axel) Bioinformatics 19, 1110-1115, 2003. COMPACT: A Comparative Package for Clustering Assessment (Roy Varshavsky, Michal Linial and David Horn) in G. Chen, Y. Pan, M. Guo and J. Lu (Eds.): Lecture Notes in Computer Science 3759 (2005) 159-167 Springer. Novel Unsupervised Feature Filtering of Biological Data (Roy Varshavsky, Assaf Gottlieb, Michal Linial and David Horn) oral presentation at ISMB 2006, Bioinformatics 22(14):e507-513. Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data. (Roy Varshavshy, David Horn and Michal Linial) PLoSOne 2008 Dynamic quantum clustering: a method for visual exploration of structures in data. (Marvin Weinstein and David Horn) Physical Review E 2009 (80) 066117 2. Bioinformatics: predicting function from sequence motifs of enzymes It started from applying MEX, a motif extraction algorithm (PNAS 2005) to protein sequences. We have applied it to all enzymes (Plos compbio 2007), deriving motifs that are specific to EC categories, called Specific Peptides. We have shown their connection to relevant biological markers (Proteins 2008). We have built an enzymes prediction scheme called DME (BMC Bioinformatics 2009). A webtool is available at http://adios.tau.ac.il/DME11.html. Recently we have applied SP searches directly to short read metagenomic data. Another application of MEX is creating common peptides from particular families of proteins. We have looked for evolutionary patters using such analyses in Olfactory Receptors and in aminoacyl tRNA synthetases. References (downloadable from the section Publications on my website): Unsupervised learning of natural languages (Zach Solan, David Horn, Eytan Ruppin and Shimon Edelman) Proc. Natl. Acad. Sc. 102 (2005) 11629-11634 Functional representation of enzymes by specific peptides (Vered Kunik, Yasmine Meroz, Zach Solan, Ben Sandbank, Uri Weingart, Eytan Ruppin and David Horn) PLOS Computational Biology 2007, 3(8):e167. Biological roles of specific peptides in enzymes. (Yasmine Meroz and David Horn) Proteins: Structure, Function, and Bioinformatics 72 (2), 606-612, 2008 Common peptides shed light on evolution of Olfactory Receptors (Assaf Gottlieb, Tsviya Olender, Doron Lancet and David Horn) BMC Evolutionary Biology 2009, 9:91 Data mining of enzymes using specific peptides. (Uri Weingart, Yair Lavi and David Horn) BMC Bioinformatics 2009, 10:446 Deriving Enzymatic and Taxonomic Signatures of Metagenomes from Short Read Data (Uri Weingart, Erez Persi, Uri Gophna and David Horn) BMC Bioinformatics 2010, 11:390 Common Peptides Study of Aminoacyl-tRNA Synthetases. (Assaf Gottlieb, Milana Frenkel-Morgenstern, Mark Safro and David Horn) PLoS ONE 2011, 6(5): e20361. doi:10.1371/journal.pone.0020361 Peptide Markers of Aminoacyl tRNA Synthetases Facilitate Taxa Counting in Metagenomic Data (Erez Persi, Uri Weingart, Shiri Freilich and David Horn) BMC Genomics 2012, 13:65