Bioinformatics and discovery: induction beckons again
John F. Allen
School of Biological and Chemical Sciences
Mile End Road
London E1 4NS
In the life sciences there is a strong resurgence of the view that there is a direct route from observation to understanding. By this route, knowledge can flow securely from data without the human and fallible intervention of guesswork, imagination or hypothesis. Information technology now puts oceans of data at our immediate disposal, and even the ubiquitous personal computer can process and analyse these data at huge speed. Surely we can now expect computer programs themselves to derive significance, relevance and meaning from chunks of information, be they nucleotide sequences or gene expression profiles? Increasingly, life scientists are advised to rely on computers, or on predetermined software algorithms, to do their thinking for them. In contrast with this view, many are convinced that no purely logical process can turn observation into understanding. We owe this conviction to the work of Karl Popper. Here I argue that Popper was correct, and outline the way in which I think his philosophy applies to the newly data-rich areas of the Life Sciences, and to bioinformatics itself. I predict that even the formidable combination of computing power with ease of access to data does not amount to a qualitative shift in the way we do science: making hypotheses remains an indispensable component in the growth of knowledge.
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