TopA Few Words In The End
Now when you, my dear readers, are reading these words, you got a lot of knowledge. One journey is over but another one is about to begin. I will not summarize what you have learned from this book as you can do so yourselves. I would like to talk a bit about what you may need to learn beyond this book.
First of all, you need to always remember about the potential limitations of microarray gene expression data. For example, Androulakis (Androulakis, 2005) provided a good analysis of such limitations, while Braga-Neto (Braga-Neto, 2007) described pitfalls that expect everyone dealing with small-sample microarray classification. The next logical step is to look for the integration of microarray gene expression data with other sources of data (see, e.g., (Zhang, Li, Wei, Yap, & Chen, 2007)).
It makes sense to visit the web site of Microarray Gene Expression Data Society (http://www.genomeweb.com if you type “gene patent” there. If you want to be your own patent engineer/lawyer, then I recommend reading the book by Ma (Ma, 2009) so that you will become familiar with the basics.
As MATLAB® is not the only programming environment useful for bioinformatics, you may be interested to read the article by Dudley and Butte (Dudley & Butte, 2009) about alternatives and programming skills required from the above-the-average researcher. Like me, they also emphasize the importance of embracing standards. Our second common point is hardware technology called Field Programmable Gate Array (FPGA). FPGA-based hardware acceleration has demonstrated its benefits for several bioinformatics applications, including sequence alignment, molecular dynamics, and proteomics (Dudley & Butte, 2009), (Storaasli, Yu, Strenski, & Maltby, 2007), (Derrien & Quinton, 2010).
I am sure you can add your own topics to study and perfect. So, let me wish you all the best in your work and life!