Bioinformatics Zen

An anti-post to my previous post on data mining. The point, to illustrate how data mining can quickly become unrewarding, and worse demotivating.

I spend much of my day analysing yeast high throughput data, recently produced in the laboratory. On one hand I’m very lucky to have access to fresh data at many cellular levels. On the other hand, with all this information, I’m easily swept away by the amount of variables I have access to - the dark side of data mining.

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In other words, you’re a bioinformatician, and data has been dumped in your lap. Find the patterns, trend, answers, or what ever meaningful knowledge the data is hiding.

From experience, I can say that is one of the most frustrating positions to be in. Data mining is a huge field and can easily be bewildering for a beginner. However, high through-put techniques in molecular biology require, more and more, that bioinformatics is required to interpret the data. Furthermore, people working in bioinformatics generally come from computer science, or biology backgrounds. Data mining, however, involves statistics to one degree or another, which means entering a field that is may not be your strong point.

Here are some tips from my own forays in the quagmire of data mining in bioinformatics. Hopefully this will give you some guidance, and an introduction to starting.

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