Gene expression profiling and protein–protein interaction analysis reveals the dynamic role of MCM7 in Alzheimer's disorder and breast cancer

Sriram, Navneeth; Mukherjee, Sunny; Sah, Mahesh Kumar

doi:10.1007/s13205-022-03207-1

Cited by 3 publications

(1 citation statement)

References 80 publications

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“…24,25 A previous study by our research team discovered the role of MCM7 in promoting cancer using gene expression analysis and protein-protein interaction data. 26 Therefore, the current study aims to further elucidate the investigation by decoding the complete tertiary structure of the MCM7 protein and identifying promising FDA-approved pharmaceutical drugs that can inhibit MCM7 for effective cancer therapy.…”

Section: Introductionmentioning

confidence: 99%

In silicoinvestigation of the role of vitamins in cancer therapy through inhibition of MCM7 oncoprotein

et al. 2022

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Section: Introductionmentioning

confidence: 99%

In silicoinvestigation of the role of vitamins in cancer therapy through inhibition of MCM7 oncoprotein

et al. 2022

Self Cite

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Identification of telomere-related genes associated with aging-related molecular clusters and the construction of a diagnostic model in Alzheimer's disease based on a bioinformatic analysis

Ruan¹,

Lv²,

Li³

et al. 2023

Computers in Biology and Medicine

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Leveraging protein-protein interactions in phenotype prediction through graph neural networks

Smeriglio,

Rosell-Mirmi,

Radeva

et al. 2024

Preprint

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Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer's disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer's disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases.

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Gene expression profiling and protein–protein interaction analysis reveals the dynamic role of MCM7 in Alzheimer's disorder and breast cancer

Cited by 3 publications

References 80 publications

In silicoinvestigation of the role of vitamins in cancer therapy through inhibition of MCM7 oncoprotein

In silicoinvestigation of the role of vitamins in cancer therapy through inhibition of MCM7 oncoprotein

Identification of telomere-related genes associated with aging-related molecular clusters and the construction of a diagnostic model in Alzheimer's disease based on a bioinformatic analysis

Leveraging protein-protein interactions in phenotype prediction through graph neural networks

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