Medical target prediction from genome sequence: combining different sequence analysis algorithms with expert knowledge and input from artificial intelligence approaches

T Dandekar; F Du; R H Schirmer; S Schmidt

doi:10.1016/s0097-8485(01)00095-x

Medical target prediction from genome sequence: combining different sequence analysis algorithms with expert knowledge and input from artificial intelligence approaches

Comput Chem. 2001 Dec;26(1):15-21. doi: 10.1016/s0097-8485(01)00095-x.

Authors

T Dandekar¹, F Du, R H Schirmer, S Schmidt

Affiliation

¹ European Molecular Biology Laboratory, PO Box 102209, Meyerhostrasse 1, D-69012 Heidelberg, Germany. dandekar@embl-heidelberg.de

PMID: 11765847
DOI: 10.1016/s0097-8485(01)00095-x

Abstract

By exploiting the rapid increase in available sequence data, the definition of medically relevant protein targets has been improved by a combination of: (i) differential genome analysis (target list): and (ii) analysis of individual proteins (target analysis). Fast sequence comparisons, data mining, and genetic algorithms further promote these procedures. Mycobacterium tuberculosis proteins were chosen as applied examples.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Artificial Intelligence*
Bacterial Proteins / chemistry
Expert Systems*
Genomics / instrumentation*
Models, Molecular
Mycobacterium / chemistry
Mycobacterium / genetics*
Predictive Value of Tests
Protein Structure, Secondary

Substances

Bacterial Proteins