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| Bioinformatics |
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| Protein |
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Problems related with the protein structure determination problem
have dominated work in computational biology, from the very inception
of this field. The prediction of protein structure has been a major
focus of biophysical research over the last two decades. The protein
secondary structure prediction problem has become a classic, challenging
problem for the artificial-intelligence and machine learning community.
Virtually every conceivable computational technique in these fields
(e.g., information theory, artificial neural networks, cascaded
networks, hybrid systems, nearest neighbor methods, hidden markov
chains, machine learning, mutual information) has been applied in
the context of protein structure prediction. The reason for this
attention is well-founded and clear: If protein structure, even
secondary structure, can be accurately predicted from the now abundantly
available gene and protein sequences, such sequences become immensely
more valuable for the understanding of drug-design, the genetic
basis of disease, the role of protein structure in its enzymatic,
structural, and signal transduction functions, and basic physiology
from molecular to cellular, to fully systemic levels. In short,
the solution of the protein structure prediction problem (and the
related protein folding problem) will bring on the second phase
of the molecular biology revolution.
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| References |
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| [1] |
I. Koch, T. Lengauer, and E. Wanke:
An algorithm for finding maximal common subtopologies in a
set of protein structures
Journal of Computational Biology 3: p 289 - 306, 1996 |
| [2] |
J. Selbig: Machine Learning for Protein Structure Prediction
In: Bock, H.H., Lenski, W., and Richter, M.M. (Eds.): Information
Systems and Data Analysis. Prospects-Foundations-Applications.
Proc. 17th Annual Conference of the GfKl, Univ. of Kaiserslautern,
1993, Springer-Verlag, Heidelberg-Berlin 1994, p 389 - 395
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| [3] |
J. Selbig and P. Argos:
Relationships between protein sequence and structure patterns
based on residue contacts.
PROTEINS: Structure, Function, and Genetics 31:172-185 (1998) |
| [4] |
F. Kaden, I. Koch, and J. Selbig:
Knowledge-based prediction of protein structures
Journal of Theoretical Biology 147: p 85-100, 1990 |
| [5] |
B. de la Iglesia, J.C. W. Debuse, V. J. Rayward-Smith. Discovering
Knowledge in commercial databases using modern heuristic techniques.
Proc 2nd Int Conf In Knowledge Discovery and Data Mining,
AAAI Press, 1996 |
| [6] |
Erez Hartuv, Armin Schmitt, J¨org Lang, et al. An algorithm
for clustering cdnas for gene expression analysis. In Proceedings
of the Third Annual International Conference
on Computational Molecular Biology (RECOMB 99), 1999. |
| [7] |
Andrade, M. and Valencia, A. (1998) Automatic extraction of keywords from scientific text: Application to the knowledge domain of protein families. Bioinformatics, 14, 600-607. |
| [8] |
Blaschke, C. et al. (1999) Automatic extraction of biological information from scientific text: Protein-protein interactions. ISMB, 7, 60-67. |
| [9] |
Friedman, C. et al. (2001) GENIES: A natural language processing system for the extraction of molecular pathways from journal articles. Bioinformatics, 17, S74-S82. |
| [10] |
Marcotte, E.M. et al. (2001) Mining literature for protein-protein interactions. Bioinformatics, 17, 359-363. Nature (1997) Obstacles of nomenclature. Nature, 389, 1. |
| [11] |
Mohammed J. Zaki and Jason T.L. Wang (Eds.), Data Management in Bioinformatics in Information Systems: An International Journal, guest editorial for special issue on Data Management in Bioinformatics, Volume 28, No. 4, pp. 241-242, June, 2003. |
| [12] |
Mohammed J. Zaki, Hannu Toivonen, and Jason Wang, BIOKDD02: Recent Advances in Data Mining in Bioinformatics, in SIGKDD Explorations, Volume 4. Issue 2, pp. 112-114, December 2002. |
| [13] |
Mohammed J. Zaki, Shan Jin and Chris Bystroff, Mining Residue Contacts in Proteins Using Local Structure Predictions, in IEEE Transactions on Systems, Man and Cybernetics -- Part B, special issue on Bio-imaging and Bio-informatics, N. Bourbakis (ed.), Vol. 33, No. 5, pp. 789-801, October 2003. |
| [14] |
Vinay Nadimpally and Mohammed J. Zaki, A Novel Approach to Determine Normal Variation in Gene Expression Data, in SIGKDD Explorations, special issue on Microarray Data Analysis, Gregory Piatetsky-Shapiro and Pablo Tamayo (eds.), Vol. 5, Issue 2, pp 4-13, December 2003. |
| [15] |
Mount, D.W.: Bioinformatics: Sequence and Genome Analysis, Cold Spring Harbor Laboratory Press, 2001. |
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