My Bioinformatics professor said this once. I don’t think he really believed it to be true. What he meant was that bioinformaticians (or computational biologists) know better to deal with uncertainty and fuzziness, what becomes more and more important for applied computer science these days.

Uncertainty and fuzziness

Uncertainty and fuzziness are not the same. Uncertainty is the consequence of a lack of knowledge. Fuzziness is inherent to the process of measurement of complex and probabilistic systems like living beings on a molecular, cellular, individual and organizational level. Measurement itself may also affect the system and so changes the measured values.

Unpredictable and unreliable

The social behaviour of human beings is also unpredictable. People don’t behave deterministic. If you ask someone what his favourite song is, the person will reply depending on the mood, on who else is listening and on the way you ask. People aren’t that simple. You may ask: do you like this or that feature of a social online services? But do not expect a reliable answer. Many people e.g. avoid to say that they do social online networking for dating.

Everything is Miscellaneous

As David Weinberger puts it (in his equally named book): Everything is Miscellaneous. Modern software has to deal with complexity, with uncertainty and with human failure. Bioinformaticians did this for a long time period. The (genomic or proteomic) data is highly chaotic and in most cases contains many faults. So Bioinformatics relies heavily on statistics – in fact Bioinformatics is (in my definition) computer-aided applied statistics on biological data. They developed algorithms and more important patterns how to write programs that can produce reliable results from noisy data in huge quantity.

Everything is connected

Here is another reason, why bioinformaticians are valuable for social network providers: bioinformaticians love networks, i.e. paper networks retrieved from text mining, metabolic networks or protein-protein interaction networks. Biochemical networks are not that different from social networks. Both are scale-free networks, i.e. have similar structures (e.g. hubs) and evolve similarly (by preferential attachment).

Stay tuned

If you are interested in these topics (network, text and data mining), stay tuned. I will recommend some books about Bioinformatics or Data Mining in general on this blog in short. Here is my first recommendation: if you build a social online service hire at least one Bioinformatician for your team.

And have a good time!