6533b7d4fe1ef96bd1263070

RESEARCH PRODUCT

Application of Graph Clustering and Visualisation Methods to Analysis of Biomolecular Data

Paulis ĶIkustsKārlis FreivaldsMārtiņš OpmanisJuris VīksnaDārta RitumaGatis MelkusPēteris RučevskisEdgars CelmsLelde LāceKārlis ČErāns

subject

0301 basic medicineComputer scienceComputationcomputer.software_genreVisualization03 medical and health sciencesIdentification (information)ComputingMethodologies_PATTERNRECOGNITION030104 developmental biology0302 clinical medicineGraph drawingFeature (machine learning)Data miningCluster analysiscomputer030217 neurology & neurosurgeryConnectivityClustering coefficient

description

In this paper we present an approach based on integrated use of graph clustering and visualisation methods for semi-supervised discovery of biologically significant features in biomolecular data sets. We describe several clustering algorithms that have been custom designed for analysis of biomolecular data and feature an iterated two step approach involving initial computation of thresholds and other parameters used in clustering algorithms, which is followed by identification of connected graph components, and, if needed, by adjustment of clustering parameters for processing of individual subgraphs.

yearjournalcountryeditionlanguage
2018-01-01
https://doi.org/10.1007/978-3-319-97571-9_20