Fuzzy clustering with biological knowledge for gene selection

Computational functional genomics-based reduction of disease-related gene sets to their key components

MOTIVATION

The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction are applied to laboratory-derived genetic data, the analysis of topical sets of genes gathered from knowledge bases requires a modified approach as no quantitative information about gene expression is available.

RESULTS

We propose a computational functional genomics-based approach at reducing sets of genes to the most relevant items based on the importance of the gene within the polyhierarchy of biological processes characterizing the disease. Knowledge bases about the biological roles of genes can provide a valid description of traits or diseases represented as a directed acyclic graph (DAG) picturing the polyhierarchy of disease relevant biological processes. The proposed method uses a gene importance score derived from the location of the gene-related biological processes in the DAG. It attempts to recreate the DAG and thereby, the roles of the original gene set, with the least number of genes in descending order of importance. This obtained precision and recall of over 70% to recreate the components of the DAG charactering the biological functions of n=540 genes relevant to pain with a subset of only the k=29 best-scoring genes.

CONCLUSIONS

A new method for reduction of gene sets is shown that is able to reproduce the biological processes in which the full gene set is involved by over 70%; however, by using only ∼5% of the original genes.

AVAILABILITY AND IMPLEMENTATION

The necessary numerical parameters for the calculation of gene importance are implemented in the R package dbtORA at https://github.com/IME-TMP-FFM/dbtORA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies

BACKGROUND

Biologists 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.

METHOD

We 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.

RESULTS

The 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.

CONCLUSIONS

Integrating 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.

Classifier ensemble selection based on affinity propagation clustering

A small number of features are significantly correlated with classification in high-dimensional data. An ensemble feature selection method based on cluster grouping is proposed in this paper. Classification-related features are chosen using a ranking aggregation technique. These features are divided into unrelated groups by an affinity propagation clustering algorithm with a bicor correlation coefficient. Some diversity and distinguishing feature subsets are constructed by randomly selecting a feature from each group and are used to train base classifiers. Finally, some base classifiers that have better classification performance are selected using a kappa coefficient and integrated using a majority voting strategy. The experimental results based on five gene expression datasets show that the proposed method has low classification error rates, stable classification performance and strong scalability in terms of sensitivity, specificity, accuracy and G-Mean criteria.

Classification by integrating plant stress response gene expression data with biological knowledge

Classification of microarray data has always been a challenging task because of the enormous number of genes. In this study, a clustering method by integrating plant stress response gene expression data with biological knowledge is presented. Clustering is one of the promising tools for attribute reduction, but gene clusters are biologically uninformative. So we integrated biological knowledge into genomic analysis to help to improve the interpretation of the results. Biological similarity based on gene ontology (GO) semantic similarity was combined with gene expression data to find out biologically meaningful clusters. Affinity propagation clustering algorithm was chosen to analyze the impact of the biological similarity on the results. Based on clustering result, neighborhood rough set was used to select representative genes for each cluster. The prediction accuracy of classifiers built on reduced gene subsets indicated that our approach outperformed other classical methods. The information fusion was proven to be effective through quantitative analysis, as it could select gene subsets with high biological significance and select significant genes.