Inductive learning and biological sequence analysis. The PLAGE program.
Inductive learning, also called 'learning from examples', is a subfield of artificial intelligence. Inductive learning methods are able to deal with 'structural descriptions'. These portray objects as composite structures consisting of various components. The use of structural descriptions to represent biological objects is appealing. For instance, they have been used by Rawlings et al [1] for symbolically and comprehensively representing the folding of proteins. This paper shows how inductive learning techniques may be used for extracting information from biological objects. We briefly describe some general techniques for describing objects in a structural way and for learning from these descriptions. We present details of a program that we developed, PLAGE, and show the application of this program for a study on signal peptides, which was done in collaboration with A Danchin [2,3]. Finally, we survey some other approaches and applications of inductive learning to molecular biology.
Gascuel
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Département d'Informatique Fondamentale, LIRMM, Montpellier, France.
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D008969:Molecular Sequence Data; D021382:Protein Sorting Signals / Q000737:chemistry*; D017433:Protein Structure, Secondary; D017421:Sequence Analysis; D017422:Sequence Analysis, DNA; D017423:Sequence Analysis, RNA; D012984:Software
D021382:Protein Sorting Signals