1/18/2024 0 Comments Random forest clustering![]() An SNP has three categories: homozygous with the common allele (genotype AA), heterozygous (genotype AB), and homozygous with the rare allele (genotype BB). SNPs occur when a single nucleotide from a DNA sequence differs at the same position between individuals. Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation used to infer population structure. This kind of analysis aims to group individuals into subpopulations based on shared genetic variations. The analysis of population structures is a crucial prerequisite for any further analysis of genetic data, such as genome-wide association mapping for reducing false positive rates, and forensics for developing reference panels to provide information on an individual’s ancestry. Such an approach is formally used to find the underlying population substructure from genetic data without considering prior information. In bioinformatics, clustering has been extensively used as an approach for detecting interesting patterns in genetic data. Clustering plays an essential role in several application domains, such as text mining, image segmentation, and bioinformatics. In data analysis, clustering is the process of partitioning objects into groups based on their similarities, where objects in the same group are more similar to one another than to objects in different groups. The paper also illustrates that applying a cluster ensemble approach, combining multiple RF clusterings, produces more robust and higher-quality results as a consequence of feeding the ensemble with diverse views of high-dimensional genetic data obtained through bagging and random subspace, the two key features of the RF algorithm.Ĭlustering is an unsupervised learning technique aimed at uncovering the underlying natural structure of data. This paper proposes, RFcluE, a cluster ensemble approach based on RF clustering to address the problem of population structure analysis and demonstrate the effectiveness of the approach. The experiments included an examination of the impact of parameter changes, comparing RFcluE performance against other clustering methods, and an assessment of the relationship between the diversity and quality of the ensemble and its effect on RFcluE performance. Experiments were conducted on high-dimensional, real genetic dataset to evaluate the proposed approach. The approach comprises a cluster ensemble framework to combine multiple runs of RF clustering. This paper proposes, RFcluE, a cluster ensemble approach for determining the underlying structure of genetic data based on RFs. Therefore, RF-derived proximity measure combined with a clustering technique may be well suited for determining the underlying structure of unlabeled data. RFs has been widely considered a supervised learning method, although it can be converted into an unsupervised learning method. RFs provides a proximity measure that can capture different levels of co-occurring relationships between variables. Random Forests (RFs) has emerged as an efficient algorithm capable of handling high-dimensional data. Genetic data usually contain hundreds of thousands of genetic markers genotyped for thousands of individuals, making an efficient means for handling such data desirable. Advances in DNA sequencing technology have facilitated the obtainment of genetic datasets with exceptional sizes. One application is population structure analysis, which aims to group individuals into subpopulations based on shared genetic variations, such as single nucleotide polymorphisms. ![]() Clustering plays a crucial role in several application domains, such as bioinformatics.
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