Section 7: Bioinformatics: Bioinformatics and its Impact on Clinical Research Methods

E. Lang; Managing Editor for the IMIA-Yearbook Section on Bioinformatics

doi:10.1055/s-0038-1638478

Yearbook of Medical Informatics, Table of Contents

Yearb Med Inform 2006; 15(01): 104-106
DOI: 10.1055/s-0038-1638478

Synopsis

Georg Thieme Verlag KG Stuttgart

Section 7: Bioinformatics: Bioinformatics and its Impact on Clinical Research Methods

Findings from the Section on Bioinformatics

Authors

E. Lang

¹University of Applied Sciences Darmstadt, Dept. of Information and Kowledge Management, Darmstadt, Germany

Managing Editor for the IMIA-Yearbook Section on Bioinformatics

Abstract

Summary

Objective

To summarize current excellent research in the field of bioinformatics.

Method

Synopsis of the articles selected for the IMIA Yearbook 2006. Results: Current research in the field of bioinformatics clearly shows ongoing unification of experimental findings and clinical outcomes. Microarray data, gene sequences and clinical data are more and more perceived as different but related facets of one entity. Significant work is done in the area of text and data mining in order to bring together patient data and biochemical phenomena by means of ontologies. A strong trend in the clinical field is performance of exhaustive studies on DNA material derived from patients that suffer from diseases that are already known to be inherited. Examination of appropriate methods covers data and text mining, ontologies as well as machine learning and classification.

Conclusions

The best paper selection of articles on bioinformatics shows examples of excellent research on methods used for studying inherited diseases and their underlying genetic dispositions. Clinical studies, inclusion of experimental findings like microarray data, and of knowledge representation formats all lead to a better understanding the linkage between gene sequences, biological functions and clinical findings in the form of healthy state or physiological disorders.

Keywords

Medical Informatics - International Medical Informatics Association - Yearbook - Bioinformatics

Full Text

References

References
1 Gaizauskas R, Demetriou G, Artymiuk PJ, Willett P. Protein Structures and Information Extraction from Biological Texts: The PASTA System. Bioinformatics 2003; 19 (01) 135-43.
2 Ivanciuc O, Schein CH, Braun W. Data mining of sequences and 3D structures of allergenic proteins. Bioinformatics 2002; 18 (10) 1358-64.
3 Ohno-Machado L, Vinterbo S, Weber G. Classification of gene expression data using fuzzy logic. Journal of Intelligent and Fuzzy Systems 2002; 12: 19-24.
4 Petricoin EF, Liotta LA. Proteomic analysis at the bedside: early detection of cancer. Trends in Biotechnology 2002; 20 12 Suppl S30-4.
5 Tanabe L, Wilbur WJ. Tagging gene and protein names in biomedical text. Bioinformatics 2002; 18 (08) 1124-32.
6 Wallqvist A, Rabow AA, Shoemaker RH, Sausville EA, Covell DG. Linking the growth inhibition response from the National Cancer Institute’s anticancer screen to gene expression levels and other molecular target data. Bioinformatics 2003; 19 (17) 2212-24.
7 Sharma R, Maheshwari JK, Prakash T, Dash D, Brahmachari SK. Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirus. Bioinformatics 2004; 20 (07) 1074-80.
8 Ein-Dor L, Kela I, Getz G, Givol D, Domany E. Outcome signature genes in breast cancer: is there a unique set?. Bioinformatics 2005; 21 (02) 171-8.
9 Maojo V, Martin-Sanchez F. Bioinformatics: towards new directions for public health. Methods Inf Med 2004; 43: 208-14.
10 Knaup P, Ammenwerth E, Brandner R, Brigl B, Fischer G, Garde S, Lang E, Pilgram R, Ruderich F, Singer R, Wolff AC, Haux R, Kulikowski C. Towards clinical bioinformatics: advancing genomic medicine with informatics methods and tools. Methods Inf Med 2004; 43: 302-7.
11 Bott OJ, Ammenwerth E, Brigl B, Knaup P, Lang E, Pilgram R. et al. The challenge of ubiquitous computing in health care: technology, concepts and solutions. Findings from the IMIA Yearbook of Medical Informatics 2005. Methods Inf Med 2005; 44: 473-9.
12 Baumgartner C, Bohm C, Baumgartner D, Marini G, Weinberger K, Olgemoller B, Liebl B, Roscher AA. Supervised machine learning techniques for the classification of metabolic disorders in newborns. Bioinformatics 2004; 20 (17) 2985-96.
13 Hauser ER, Crossman DC, Granger CB, Haines JL, Jones CJ, Mooser V. et al. A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD Study. Am J Hum Genet 2004; 75: 436-47.
14 Hristovski D, Peterlin B, Mitchell JA, Humphrey SM. Using literature-based discovery to identify disease candidate genes. Int J Med Inform 2005; 74: 289-98.
15 Koike A, Niwa Y, Takagi T. Automatic extraction of gene/protein biological functions from biomedical text. Bioinformatics 2005; 21 (07) 1227-36.
16 Tiffin N, Kelso JF, Powell AR, Pan H, Bajic VB, Hide WA. Integration of text- and data-mining using ontologies successfully selects disease gene candidates. Nucleic Acids Research 2005; 33 (05) 1544-1552.