=Paper=
{{Paper
|id=None
|storemode=property
|title=Towards a Semantic Foundation for Bioinformatics
|pdfUrl=https://ceur-ws.org/Vol-611/paper10.pdf
|volume=Vol-611
}}
==Towards a Semantic Foundation for Bioinformatics==
<pdf width="1500px">https://ceur-ws.org/Vol-611/paper10.pdf</pdf>
<pre>
      Towards a Semantic Foundation for Bioinformatics

                                               Ross D. King

       Department of Computer Science, Aberystwyth University, UK, rdk@aber.ac.uk



1 Abstract

   With a two and half thousand year tradition logic is the best understood way of
representing scientific knowledge. Only logic provides the semantic clarity necessary
to ensure the comprehensibility, reproducibility, and free exchange of knowledge.
The use of logic is also necessary to enable computers to play a full part in science
[1]. The semantic web is transforming the dissemination of science by making for the
first time making a large amount of scientific knowledge available expressed in logic.
   Bioinformatics is one of the undoubted successes stories of the semantic web, with
bioinformatic knowledge making up a large percentage of the scientific semantic web.
Many of the problems that make semantic web reasoning difficult don't apply to
bioinformatics: a ground truth of scientific knowledge exists, top level ontologies
have been agreed (BFO), many other ontological standards exist, and the
bioinformatic semantic web is large but not too large.
   The use of bioinformatic software is essential to modern biology. However, there
is a clear mismatch between the increasing use of the semantic web and logic, and the
way bioinformatic systems utlilise and make inferences with this knowledge. This is
because almost all computer based bioinformatic reasoning is done using ad hoc
programs. From a formal point of view these programs are invariably making logical
inferences: deductions, abductions, inductions, with perhaps a probabilistic element.
However, what exactly these inferences exactly are is generally unclear.
   The aim of my research is to make these inferences clear and to express them in
logic, and make them executable across the semantic web.
   For example, we argue that abductive inference is central to modern evolutionary
based phylogenetics - clustering. This can be seen in evolutionary definition of a
taxon (grouping of organisms): “that all members of a taxon are descendants of the
nearest common ancestor (monophyly sensu stricto)” [2]. We express this in logic as:
   ∀A . A ∈ taxon1 ⇒ (∃Ancestor . ∀B . B∉ taxon1 ⋀ ancestor(Ancestor, A) ⋀ ¬ancestor(Ancestor, B)).

   This clustering is based on the abductive inference of the existence of an ancestor
organism not shared by any other taxon.
   References
1. King, R.D., Rowland, J., Oliver, S.G., Young, M., Aubrey, W., Byrne, E., Liakata, M.,
   Markham, M., Pir, P., Soldatova, L.N., Sparkes, A., Whelan, K.E., Clare, C. (2009) The
   Automation of Science. Science. 324, 85-89.
2. Mayr, E. (1982). The Growth of Biological Thought: Diversity, Evolution, and Inheritance.
   Cambridge, Mass: Belknap Press.

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