Techniques bioinformatics

Bazzan, A. L., P. M. Engel, L. F. Schroeder, and S. C. Da Silva. 2002. Automated annotation of keywords for proteins related to mycoplasmataceae using machine learning techniques. Bioinformatics 18, Suppl. no. 2 S35-43. 

Brenke R, Kozakov D, Chuang GY et al (2009) Fragment-based identification of draggable hot spots of proteins using Fourier domain correlation techniques. Bioinformatics 25 621-627... 

However, there are a number of issues here. In the first place, stress itself is a somewhat nebulous concept, and there is continuing debate about how it should be defined. Second, even with the benefit of multivariate statistics and the techniques of bioinformatics, measuring stress from all sources in a meaningful way is dauntingly complex and may not be realizable in practice. 

Yang, Z. R., Thomson, R., McNeil, P., and Esnouf, R. M. (2005). RONN The bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 21, 3369-3376. 

R. Steuer and G. Zamora Lopez, Global network properties. Analysis of Biological Networks, Wiley Series on Bioinformatics Computational Techniques and Engineering. B. H. Junker and F. Schreiber, eds., John Wiley Sons, Inc. 2008. 

It is only natural that, to date, bioinformatics tools contribute most to the analysis of amino acid sequences. Only a small amount of current sequence data is subjected to direct experimentation. The majority of amino acid sequences currently accessible in public databases have been derived by in silico translations of nucleic acid sequence data, despite the fact that amino acid sequencing was introduced historically long before nucleic acid sequencing. It is hard to predict the future of the experimental generation of primary data. Certainly, sequencing of nucleic acids continues to become cheaper and faster, and novel techniques may further enhance the production of data. DNA chips are already used to detect differences between very similar sequences other methods may generate DNA data even more efficiently. 

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