Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editing
The explosion of biological data has largely influenced the focus of today’s biology research. Integrating and analysing large quantity of data to provide meaningful insights has become the main challenge to biologists and bioinformaticians. One major problem is the combined data analysis of data fr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2012-06-01
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| Series: | Journal of Integrative Bioinformatics |
| Online Access: | https://doi.org/10.1515/jib-2012-197 |
| _version_ | 1818582205706797056 |
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| author | Sun Peng Guo Jiong Baumbach Jan |
| author_facet | Sun Peng Guo Jiong Baumbach Jan |
| author_sort | Sun Peng |
| collection | DOAJ |
| description | The explosion of biological data has largely influenced the focus of today’s biology research. Integrating and analysing large quantity of data to provide meaningful insights has become the main challenge to biologists and bioinformaticians. One major problem is the combined data analysis of data from different types, such as phenotypes and genotypes. This data is modelled as bi-partite graphs where nodes correspond to the different data points, mutations and diseases for instance, and weighted edges relate to associations between them. Bi-clustering is a special case of clustering designed for partitioning two different types of data simultaneously. We present a bi-clustering approach that solves the NP-hard weighted bi-cluster editing problem by transforming a given bi-partite graph into a disjoint union of bi-cliques. Here we contribute with an exact algorithm that is based on fixed-parameter tractability. We evaluated its performance on artificial graphs first. Afterwards we exemplarily applied our Java implementation to data of genome-wide association studies (GWAS) data aiming for discovering new, previously unobserved geno-to-pheno associations. We believe that our results will serve as guidelines for further wet lab investigations. Generally our software can be applied to any kind of data that can be modelled as bi-partite graphs. To our knowledge it is the fastest exact method for weighted bi-cluster editing problem. |
| first_indexed | 2024-12-16T07:45:41Z |
| format | Article |
| id | doaj.art-d14562d1129641e39a31ddd0b591a334 |
| institution | Directory Open Access Journal |
| issn | 1613-4516 |
| language | English |
| last_indexed | 2024-12-16T07:45:41Z |
| publishDate | 2012-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Integrative Bioinformatics |
| spelling | doaj.art-d14562d1129641e39a31ddd0b591a3342022-12-21T22:38:59ZengDe GruyterJournal of Integrative Bioinformatics1613-45162012-06-0192536710.1515/jib-2012-197biecoll-jib-2012-197Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editingSun Peng0Guo Jiong1Baumbach Jan2Computational Systems Biology group, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken, Germany GermanyCluster of Excellence for Multimodal Computing and Interaction, Saarland University, Campus E1.7, 66123 Saarbrücken, Germany GermanyComputational Systems Biology group, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken, Germany GermanyThe explosion of biological data has largely influenced the focus of today’s biology research. Integrating and analysing large quantity of data to provide meaningful insights has become the main challenge to biologists and bioinformaticians. One major problem is the combined data analysis of data from different types, such as phenotypes and genotypes. This data is modelled as bi-partite graphs where nodes correspond to the different data points, mutations and diseases for instance, and weighted edges relate to associations between them. Bi-clustering is a special case of clustering designed for partitioning two different types of data simultaneously. We present a bi-clustering approach that solves the NP-hard weighted bi-cluster editing problem by transforming a given bi-partite graph into a disjoint union of bi-cliques. Here we contribute with an exact algorithm that is based on fixed-parameter tractability. We evaluated its performance on artificial graphs first. Afterwards we exemplarily applied our Java implementation to data of genome-wide association studies (GWAS) data aiming for discovering new, previously unobserved geno-to-pheno associations. We believe that our results will serve as guidelines for further wet lab investigations. Generally our software can be applied to any kind of data that can be modelled as bi-partite graphs. To our knowledge it is the fastest exact method for weighted bi-cluster editing problem.https://doi.org/10.1515/jib-2012-197 |
| spellingShingle | Sun Peng Guo Jiong Baumbach Jan Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editing Journal of Integrative Bioinformatics |
| title | Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editing |
| title_full | Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editing |
| title_fullStr | Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editing |
| title_full_unstemmed | Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editing |
| title_short | Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editing |
| title_sort | integrated simultaneous analysis of different biomedical data types with exact weighted bi cluster editing |
| url | https://doi.org/10.1515/jib-2012-197 |
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