ArticleBioData mining2018
A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies.
Article in BioData mining, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark.PeerJ. Computer science · 2021Article
- RoCoLe: AData in brief · 2019Article
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3 authors.
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Abstract
backgroundBiologists aim to understand the genetic background of diseases, metabolic disorders or any other genetic condition. Microarrays are one of the main high-throughput technologies for collecting information about the behaviour of genetic information on different conditions. In order to analyse this data, clustering arises as one of the main techniques used, and it aims at finding groups of genes that have some criterion in common, like similar expression profile. However, the problem of finding groups is normally multi dimensional, making necessary to approach the clustering as a multi-objective problem where various cluster validity indexes are simultaneously optimised. They are usually based on criteria like compactness and separation, which may not be sufficient since they can not guarantee the generation of clusters that have both similar expression patterns and biological coherence.
methodWe propose a Multi-Objective Clustering algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) to find clusters of genes with high levels of co-expression, biological coherence, and also good compactness and separation. Cluster quality indexes are used to optimise simultaneously gene relationships at expression level and biological functionality. Our proposal also includes intensification and diversification strategies to improve the search process.
resultsThe effectiveness of the proposed algorithm is demonstrated on four publicly available datasets. Comparative studies of the use of different objective functions and other widely used microarray clustering techniques are reported. Statistical, visual and biological significance tests are carried out to show the superiority of the proposed algorithm.
conclusionsIntegrating a-priori biological knowledge into a multi-objective approach and using intensification and diversification strategies allow the proposed algorithm to find solutions with higher quality than other microarray clustering techniques available in the literature in terms of co-expression, biological coherence, compactness and separation.
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