Fusion of systems for automated cell phenotype image classification. (English)
Expert Syst. Appl. 37, No. 2, 1556-1562 (2010).
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Improving protein localization prediction using amino acid group based physichemical encoding. (English)
Rajasekaran, Sanguthevar (ed.), Bioinformatics and computational biology. First international conference, BICoB 2009, New Orleans, LA, USA, April 8‒10, 2009. Proceedings. Berlin: Springer (ISBN 978-3-642-00726-2/pbk). Lecture Notes in Computer Science 5462. Lecture Notes in Bioinformatics, 248-258 (2009).
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Graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns. (English)
J. Mach. Learn. Res. 9, 651-682 (2008).
3
Extracting features from gene ontology for the identification of protein subcellular location by semantic similarity measurement. (English)
Washio, Takashi (ed.) et al., Emerging technologies in knowledge discovery and data mining. PAKDD 2007 international workshops Nanjing, China, May 22‒25, 2007. Revised selected papers. Berlin: Springer (ISBN 978-3-540-77016-9/pbk). Lecture Notes in Computer Science 4819. Lecture Notes in Artificial Intelligence, 112-118 (2007).
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A novel method to predict protein subcellular localizations. (English)
OJB, Online J. Bioinform. 6, 8-21 (2005).
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In silico prediction of peroxisomal proteins in mouse. (English)
Genome Inform. Online 14, 539-540 (2003).
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Robust numerical features for description and classification of subcellular location patterns in fluorescence microscope images. (English)
J. VLSI Signal Process. Syst. Signal Image Video Technol. 35, No. 3, 311-321 (2003).
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