Predicting the sub-cellular location of proteins from text using support vector machines.
We present an automatic method to classify the sub-cellular location of proteins based on the text of relevant medline abstracts. For each protein, a vector of terms is generated from medline abstracts in which the protein/gene's name or synonym occurs. A Support Vector Machine (SVM) is used to automatically partition the term space and to thus discriminate the textual features that define sub-cellular location. The method is benchmarked on a set of proteins of known sub-cellular location from S. cerevisiae. No prior knowledge of the problem domain nor any natural language processing is used at any stage. The method out-performs support vector machines trained on amino acid composition and has comparable performance to rule-based text classifiers. Combining text with protein amino-acid composition improves recall for some sub-cellular locations. We discuss the generality of the method and its potential application to a variety of biological classification problems.
Stapley
B J
BJ
Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, 44 Lincoln's Inn Field, London, WC2A 3PX, United Kingdom. b.stapley@icrf.icnet.uk
Kelley
L A
LA
Sternberg
M J E
MJ
D001331:Automation; D002462:Cell Membrane / Q000737:chemistry; D015825:Chromosomes, Fungal; D003593:Cytoplasm / Q000737:chemistry; D003599:Cytoskeleton / Q000737:chemistry; D005656:Fungal Proteins / Q000032:analysis* / Q000235:genetics; D005822:Genetic Vectors; D008247:Lysosomes / Q000737:chemistry; D016239:MEDLINE; D012441:Saccharomyces cerevisiae / Q000737:chemistry* / Q000235:genetics; D013347:Subcellular Fractions / Q000737:chemistry*; D014617:Vacuoles / Q000737:chemistry
D005656:Fungal Proteins