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. 2022 Feb 22:2022:2263194.
doi: 10.1155/2022/2263194. eCollection 2022.

Healthcare Biclustering-Based Prediction on Gene Expression Dataset

M Ramkumar  1 , N Basker  2 , D Pradeep  3 , Ramesh Prajapati  4 , N Yuvaraj  5 , R Arshath Raja  5 , C Suresh  6 , Rahul Vignesh  7 , U Barakkath Nisha  8 , K Srihari  9 , Assefa Alene  10
Affiliations

Healthcare Biclustering-Based Prediction on Gene Expression Dataset

M Ramkumar et al. Biomed Res Int. .

Retraction in

Abstract

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods.

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Conflict of interest statement

There is no conflict of interest.

Figures

Figure 1
Figure 1
Proposed FCM architecture.
Figure 2
Figure 2
Nonoverlapping modules with increasing noise levels for constant bicluster.
Figure 3
Figure 3
Overlapping modules in case of constant bicluster with increasing overlap degree.
Figure 4
Figure 4
Nonoverlapping modules for additive bicluster with increasing noise levels.
Figure 5
Figure 5
Overlapping modules in case of additive bicluster with increasing overlap degree.
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References

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