Eakasit Pacharawongsakda

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Predicting protein subcellular location is one of major challenges in Bioinformatics area since such knowledge helps us understand protein functions and enables us to select the targeted proteins during drug discovery process. While many computational techniques have been proposed to improve predictive performance for protein subcellular location, they have(More)
As the occurrence of natural compounds is related to the spatial distribution and evolution of microorganisms for biological and ecological relevance, the data integration of chemistry, geography, and phylogeny within an analytical framework is needed to make better decisions on sourcing the microbes for drug discovery. Such a framework should help(More)
BACKGROUND In bacterial pathogens, both cell surface-exposed outer membrane proteins and proteins secreted into the extracellular environment play crucial roles in host-pathogen interaction and pathogenesis. Considerable efforts have been made to identify outer membrane (OM) and extracellular (EX) proteins produced by Leptospira interrogans, which may be(More)
Online reviews on a service are important sources for service providers to improve their service delivery and service consumers to obtain information for decision making before their service acquisition. However, in the real situation, there are several points of view (dimensions) in service assessment using online reviews. This paper shows an empirical(More)
UNLABELLED sMOL Explorer is a 2D ligand-based computational tool that provides three major functionalities: data management, information retrieval and extraction and statistical analysis and data mining through Web interface. With sMOL Explorer, users can create personal databases by adding each small molecule via a drawing interface or uploading the data(More)
While multi-label classification can be widely applied for problems where multiple classes can be assigned to an object, its effectiveness may be sacrificed due to curse of dimensionality in the feature space and sparseness of dimensionality in the label space. As a solution, this paper presents two alternative methods, namely Dependent Dual Space Reduction(More)
Multi-label classification has been increasingly recognized since it can classify objects into multiple classes, simultaneously. However , its effectiveness might be sacrificed due to high dimensionality problem in feature space and sparseness problem in label space. To address these issues, this paper proposes a Two-Stage Dual Space Reduction (2SDSR)(More)
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