Section 2: Patient Records: Integrating Bioinformatics into Clinical Practice: Progress and Evaluation

Summary

Objectives

To summarize current excellent research in the field of bioinformatics.

Method

Synopsis of the articles selected for the IMIA Yearbook 2007.

Results

Current research in the field of bioinformatics is characterized by careful evaluation of methods and by improved integration of methods into clinical workflows. Ongoing research on genetic causes of diseases is performed on more and better sources of reference data (genome sets and respective annotations), but is still hampered by insufficient, lacking or biased patient data. The application area of bioinformatics has been broadened, leading to amendment or even replacement of traditional methods in fields like characterization of microorganisms. Researchers carry out thorough statistical analyses in order to ensure quality and methodological correctness of new methods based on bioinformatic approaches which are more and more competitive compared to well-established techniques.

Conclusions

The best paper selection of articles on bioinformatics shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. The crucial role of reliable and comprehensive data sources is affirmed, while technical development draws attention to the increasing problem of comparability of data derived some years ago with weaker equipment and those that are of up-to-date quality.

 

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Keywords

Medical informatics - International Medical Informatics Association - yearbook - bioinformatics

 

 

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• References

• 1 Calabrese P, Mecklin JP, Jarvinen HJ, Aaltonen LA, Tavare S, Shibata D. Numbers of mutations to different types of colorectal cancer. BMC Cancer 2005; 05: 126-32.
• 2 Lai C, Reinders MJ, van’t Veer LJ, Wessels LF. A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets. BMC Bioinformatics 2006; 07: 236-45.
• 3 Roy M, Xu Q, Lee C. Evidence that public database records for many cancer-associated genes reflect a splice form found in tumors and lack normal splice forms. Nucleic Acids Res 2005; 33: 5026-33.
• 4 Sjoblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber TD. et al. The consensus coding sequences of human breast and colorectal cancers. Science 2006; 314: 268-74.
• 5 Brors B. Microarray annotation and biological information on function. Methods Inf Med 2005; 44: 468-72.
• 6 Tsou AP, Sun YM, Liu CL, Huang HD, Horng JT, Tsai MF. et al. Biological data warehousing system for identifying transcriptional regulatory sites from gene expressions of microarray data. IEEE Trans Inf Technol Biomed 2006; 10: 550-8.
• 7 Snitkin ES, Gustafson AM, Mellor J, Wu J, DeLisi C. Comparative assessment of performance and genome dependence among phylogenetic profiling methods. BMC Bioinformatics 2006; 07: 420-31.
• 8 Cox D, Cox DR. Planning of experiments. New York: Wiley & Sons; 1958
• 9 Knaup P, Ammenwerth E, Brandner R, Brigl B, Fischer G, Garde S. et al. Towards clinical bioinformatics: advancing genomic medicine with informatics methods and tools. Methods Inf Med 2004; 43: 302-7.
• 10 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.
• 11 Lang E. Bioinformatics and its Impact on Clinical Research Methods. Methods Inf Med 2006; 45: 104-6.
• 12 Cai Z, Mao X, Li S, Wei L. Genome Comparison using Gene Ontology (GO) with statistical testing. BMC Bioinformatics 2006; 07: 374.
• 13 Choi JK, Yu U, Yoo OJ, Kim S. Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 2005; 21 (24) 4348-4355.
• 14 Goh CS, Gianoulis TA, Liu Y, Li J, Paccanaro A, Lussier YA. et al. Integration of curated databases to identify genotype-phenotype associations. BMC Genomics 2006; 07: 257.
• 15 Mansmann U. Genomic profiling. Methods Inf Med 2005; 44: 454-60.
• 16 van ’t Veer L, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415: 530-6.
• 17 Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, Horng CF. et al. Gene expression predictors of breast cancer outcomes. The Lancet 2003; 361: 1590-6.
• 18 Chang JC, Wooten EC, Tsimelzon A, Hilsenbeck SG, Gutierrez MC, Elledge R. et al. Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. The Lancet 2003; 362: 362-9.