Bibliography

AED⁺00

A. Alizadeh, M. Eisen, R. Davis, C. Ma, I. Lossos, A. Rosenwald, J. Boldrick, J. Sabet, T. Tran, X. Yu, J. Powell, L. UYang, G. Marti, T. Moore, J. Hudson, L. Lu, D. Lewis, R. Tibshirani, G. Sherlock, W. Chan, T. Greiner, D. Weisenburger, J. Armitage, R. Warnke, R. Levy, W. WIlson, M. Grever, J. Byurd, D. Botstein, P. Brown, and L. Staudt.
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.
Genome Biology, 1(2), 2000.

AGM⁺90

Stephen F. Altschul, Warren Gish, Webb Miller, Eugene W. Myers, and David J. Lipman.
Basic local alignment search tool.
J. Mol. Biol., 215:403-410, 1990.

AMS⁺93

S. Arikawa, S. Miyano, A. Shinohara, S. Kuhara, Y. Mukouchi, and T. Shinohara.
A machine discovery from amino-acid-sequences by decision trees over regular patterns.
New Generation Computing, 11:361-375, 1993.

AMS⁺97

Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schäffer, Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman.
Gapped BLAST and PSI-BLAST: A new generation of protein database search programs.
Nucleic Acids Research, 25(17):3389-3402, 1997.

BB98

P. Baldi and S. Brunak.
Bioinformatics: The Machine Learning Approach.
MIT Press, Cambridge, MA, third edition, 1998.

BCH98

Steven E. Brenner, Cyrus Chothia, and Tim J.P. Hubbard.
Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships.
Proc. Natl. Acad. Sci. USA, 95(11):6073-6078, 1998.

BF84

E. Boros and Z. Füredi.
Triangles covering the centre of an n-set.
Geometriae Dedicata, 17:69-77, 1984.

BO98

Andreas D. Baxevanis and B.F. Francis Ouellette, editors.
Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins.
John Wiley & Sons, Inc., 1998.

CO98

Andrew Y. Cheng and Ming Ouyang.
On algorithms for simplicial depth.
Technical Report dcs-tr-368, Rutgers University, 1998.

FMS01

Y. Freund, Y. Mansour, and R. Schapire.
Why averaging classifiers can protect against overfitting.
In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.

FS96

Y. Freund and R. Schapire.
Experiments with a new boosting algorithm.
In Proceedings of the Thirteenth International Conference on Machine Learning (ICML-96), pages 148-156. Morgan Kaufmann, San Francisco, CA, 1996.

FS97

Y. Freund and R. Schapire.
A decision-theoretic generalization of on-line learning and an application to boosting.
Journal of Computer and System Sciences, 55:119-139, 1997.

FS99

Y. Freund and R. Schapire.
A short introduction to boosting.
Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, 1999.
In Japanese and translated by Naoki Abe; English version at http://www.research.att.com/$\!\sim$schapire/cgi-bin/uncompress-papers/Freun dSc99.ps.

FSBL98

Y. Freund, R. Schapire, P. Bartlett, and W. Lee.
Boosting the margin: A new explanation for the effectiveness of voting methods.
The Annals of Statistics, 26(5):1651-1686, 1998.

GME⁺92

X. Guan, R.J. Mural, J.R. Einstein, R.C. Mann, and E.C. Uberbacher.
GRAIL: An integrated artificial intelligence system for gene recognition and interpretation.
In Eighth IEEE Conference on AI Applications, pages 9-13, Monterey, CA, March 2-6 1992. IEEE Computer Society Press.

Got82

O. Gotoh.
An improved algorithm for matching biological sequences.
J. Mol. Biol., 162:705-708, 1982.

GW92

C. Gonzalez and R. Woods.
Digital Image Processing.
Addison-Wesley Publishing Company, 1992.

HL98

M.A. Hershkovitz and D.D. Leipe.
Phylogentic analysis.
In A.D. Baxevanis and B.F.F. Ouellette, editors, Bioinformatics. Wiley-Interscience, 1998.

HTE⁺00

T. Hastie, R. Tibshirani, M. Eisen, A. Alizadeh, R. Levy, L. Staudt, W. Chan, and D. Botstein.
`Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns.
Genome Biology, 1(2), 2000.

KA90

Samuel Karlin and Stephen F. Altschul.
Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes.
Proc. Natl. Acad. Sci. USA, 87:2264-2268, 1990.

Lan00

E. Lander, 2000.
Private communication.

Lee98

T-W Lee.
Independent Component Analysis: Theory and Applications.
Kluwer Academic Publishers, 1998.

Li97

Wen-Hsiung Li.
Molecular Evolution.
Sinauer Associates, Inc., 1997.

Liu90

R.Y. Liu.
On a notion of data depth based on random simplices.
The Annals of Statistics, pages 405-414, 1990.

LS93

R.Y. Liu and K. Singh.
A quality index based on data depth and multivariate rank tests.
Journal of American Statistical Association, 88:252-260, 1993.

Mit97

T. Mitchell.
Machine Learning.
McGraw Hill, 1997.

MST94

D. Michie, D. Spiegelhalter, and C. Taylor, editors.
Machine Learning, Neural and Statistical Classification.
Ellis Horwood, NY, 1994.

NW70

Saul B. Needleman and Christian D. Wunsch.
A general method applicable to the search for similarities in the amino acid sequence of two proteins.
J. Mol. Biol., 48:443-453, 1970.

OCB01

M. Ouyang, J. Case, and J. Burnside.
Divide & conquer machine learning for a genomics analogy problem.
In Proceedings of the Fourth International Conference on Discovery Science 2001 (DS'01), 2001.
Washington, D.C., November 25-28, to appear.

Oja83

E. Oja.
Subspace Methods of Pattern Recognition, volume 6 of Pattern Recognition and Image Processing Series.
John Wiley & Sons, 1983.

Oja89

E. Oja.
Neural networks, principal components, and subspaces.
International Journal of Neural Systems, 1(1):61-68, 1989.

Pea95

William R. Pearson.
Comparison of methods for searching protein sequence databases.
Protein Science, 4:1145-1160, 1995.

Qui93

J.R. Quinlan.
C4.5: Programs for Machine Learning.
Morgan Kaufmann Publishers, San Mateo, CA, 1993.

RN95

S. Russell and P. Norvig.
Artificial Intelligence: A Modern Approach.
Prentice-Hall, NJ, 1995.

SDFH98

Steven Salzberg, Arthur L. Delcher, Kenneth H. Fasman, and John Henderson.
A decision tree system for finding genes in DNA.
Journal of Computational Biology, 5(4):667-680, 1998.

WM00

C. Wu and J. McLarty.
Neural Networks and Genome Informatics.
Elsevier Science, Amsterdam, 2000.