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

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Major topics of research


: 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


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

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*0

Algorithm for data clustering in pattern recognition problems based on quantum

(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 an
d 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 Spr

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

Global Considerations in Hierarchical Clustering Reveal Meaning
ful 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


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 compb
io 2007), deriving motifs
that are

specific to EC cat
egories, called Specific Peptides. We have shown their
to relevant biologi
cal markers (Proteins 2008). W
e have built an enzymes
prediction scheme called DME (BMC Bioinformatics 2009)
. A webtool

is available at
. Recently


SP searches directly to
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)

. Natl. Acad. Sc. 102 (2005) 11629

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 r
oles of specific peptides in enzymes.

(Yasmine Meroz and David Horn) Proteins: Structure, Functi
on, and Bioinformatics
72 (2),
612, 2008

Common peptides shed light on evolution of Olfactory Receptors

(Assaf Gottlieb, Tsviya Olender, Doron Lancet and D
avid 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 W
eingart, 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.p