Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis

Seisenova, Ainur; Daniyarov, Asset; Molkenov, Askhat; Sharip, Aigul; Zinovyev, Andrei; Kairov, Ulykbek

doi:10.3389/fgene.2021.683632

Cited by 2 publications

(1 citation statement)

References 76 publications

(75 reference statements)

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“…In future studies, we will try to reduce the dimension of data from another angle to expand the research so that the processed data can have practical significance and independent attributes can be obtained that will explain the internal structure of the original variable. Independent component analysis is a novel analysis method based on information theory [ 41 ]. It is similar to principal component analysis (PCA) in form, and it can be tested as a supplemental or replacement method that is theoretically more suitable to find the hidden factors behind the observed data [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm

Zhang,

Xiang,

Zhao

et al. 2023

Entropy

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Identifying the driver genes of cancer progression is of great significance in improving our understanding of the causes of cancer and promoting the development of personalized treatment. In this paper, we identify the driver genes at the pathway level via an existing intelligent optimization algorithm, named the Mouth Brooding Fish (MBF) algorithm. Many methods based on the maximum weight submatrix model to identify driver pathways attach equal importance to coverage and exclusivity and assign them equal weight, but those methods ignore the impact of mutational heterogeneity. Here, we use principal component analysis (PCA) to incorporate covariate data to reduce the complexity of the algorithm and construct a maximum weight submatrix model considering different weights of coverage and exclusivity. Using this strategy, the unfavorable effect of mutational heterogeneity is overcome to some extent. Data involving lung adenocarcinoma and glioblastoma multiforme were tested with this method and the results compared with the MDPFinder, Dendrix, and Mutex methods. When the driver pathway size was 10, the recognition accuracy of the MBF method reached 80% in both datasets, and the weight values of the submatrix were 1.7 and 1.89, respectively, which are better than those of the compared methods. At the same time, in the signal pathway enrichment analysis, the important role of the driver genes identified by our MBF method in the cancer signaling pathway is revealed, and the validity of these driver genes is demonstrated from the perspective of their biological effects.

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Section: Discussionmentioning

confidence: 99%