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Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks.

Haiying Wang·Francisco Azuaje·Norman Black
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Abstract

There is an increasing need to develop powerful techniques to improve biomedical pattern discovery and visualization. This paper presents an automated approach, based on hybrid self-adaptive neural networks, to pattern identification and visualization for biomolecular data. The methods are tested on two datasets: leukemia expression data and DNA splice-junction sequences. Several supervised and unsupervised models are implemented and compared. A comprehensive evaluation study of some of their intrinsic mechanisms is presented. The results suggest that these tools may be useful to support biological knowledge discovery based on advanced classification and visualization tasks.

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AlgorithmsBiomarkers, TumorGene Expression ProfilingHumansLeukemiaNeoplasm ProteinsNeural Networks, ComputerOligonucleotide Array Sequence AnalysisPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySequence Analysis, DNAUser-Computer InterfaceEvaluation StudyJournal Article

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Haiying Wang, Francisco Azuaje, Norman Black. 2002. Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks.. https://doi.org/10.1109/tnb.2003.809465

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