Challenges in Bioinformatics for Statistical Data Miners
Diego Kuonen · 2003
Starting with possible definitions of statistical data mining and bioinformatics, this article will give a general side-by-side view of both fields, emphasising on the statistical data mining part and its techniques, illustrate possible synergies and discuss how statistical data miners may collaborate in bioinformatics’ challenges in order to unlock the secrets of the cell. What is statistics and why is it needed? Statistics is the science of “learning from data”. It includes everything from planning for the collection of data and subsequent data management to end-of-the-line activities, such as drawing inferences from numerical facts called data and presentation of results. Statistics is concerned with one of the most basic of human needs: the need to find out more about the world and how it operates in face of variation and uncertainty. Because of the increasing use of statistics, it has become very important to understand and practise statistical thinking. Or, in the words of H. G. Wells: “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write”. But, why is statistics needed? Knowledge is what we know. Information is the communication of knowledge. Data are known to be crude information and not knowledge by itself. The way leading from data to knowledge is as follows: from data to information (data become information when they become relevant to the decision problem); from information to facts (information becomes facts when the data can support it); and finally, from facts to knowledge (facts become knowledge when they are used in the successful completion of the decision process). That is why we need statistics and statistical thinking. Statistics arose from the need to place knowledge on a systematic evidence base. What is data mining? Data mining has been defined in almost as many ways as there are authors who have written about it. Because it sits at the interface between statistics, computer science, artificial intelligence, machine learning, database management and data visualisation (to name some of the fields), the definition changes with the perspective of the user. Here is a not so random sample of a few: