[Retracted] Healthcare Biclustering‐Based Prediction on Gene Expression Dataset

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.

[Retracted] Dense Convolutional Neural Network for Detection of Cancer from CT Images

“…There are several processes in the quantitative radiomics pipeline that include identifying the region of interest to be explored, and the extraction of quantitative information [ 2 – 6 ]. These characteristics must first be statistically analyzed in order to be used in the development of classification models that reliably anticipate the outcome of the research.…”

Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data.

“…Future work would involve conducting a new comparison between old and new machine learning methods and deep learning methods to cluster patients based on clinical risk of relapse [27] . The field of unsupervised machine learning in bioinformatics is developing rapidly, with the emergence of new methods such as model-based clustering [28] , bi-clustering [29] and deep learning. Karim M. and al.…”

Biclustering Models Under Collinearity in Simulated Biological Experiments

“…Certain overlapping biclustering models are able to capture member clusters contained in two or more biclusters in the data matrix. Such methods have been applied to many biological data for the classification and identification of biological entities [7][8][9].…”