Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer’s disease

Afzal, Muhammad; Alharbi, Khalid Saad; Alzarea, Sami I.; Alyamani, Najiah M.; Kazmi, Imran; Güven, Emine Seda Güvendağ

doi:10.1016/j.heliyon.2022.e12202

Cited by 8 publications

(2 citation statements)

References 76 publications

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“…One of the arguments related to the choice of rank is to remove noise and recover the signatures [ 52 ]. However, when it comes to NMF, the choice of noise is not obvious as the noisy version of the target matrix must be nonnegative as well, which suggests that injected noise may also introduce bias [ 53 ].…”

Section: Discussionmentioning

confidence: 99%

Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection

Güven¹

2023

JMIR Bioinform Biotech

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Background There is a great need to develop a computational approach to analyze and exploit the information contained in gene expression data. The recent utilization of nonnegative matrix factorization (NMF) in computational biology has demonstrated the capability to derive essential details from a high amount of data in particular gene expression microarrays. A common problem in NMF is finding the proper number rank (r) of factors of the degraded demonstration, but no agreement exists on which technique is most appropriate to utilize for this purpose. Thus, various techniques have been suggested to select the optimal value of rank factorization (r). Objective In this work, a new metric for rank selection is proposed based on the elbow method, which was methodically compared against the cophenetic metric. Methods To decide the optimum number rank (r), this study focused on the unit invariant knee (UIK) method of the NMF on gene expression data sets. Since the UIK method requires an extremum distance estimator that is eventually employed for inflection and identification of a knee point, the proposed method finds the first inflection point of the curvature of the residual sum of squares of the proposed algorithms using the UIK method on gene expression data sets as a target matrix. Results Computation was conducted for the UIK task using gene expression data of acute lymphoblastic leukemia and acute myeloid leukemia samples. Consequently, the distinct results of NMF were subjected to comparison on different algorithms. The proposed UIK method is easy to perform, fast, free of a priori rank value input, and does not require initial parameters that significantly influence the model’s functionality. Conclusions This study demonstrates that the elbow method provides a credible prediction for both gene expression data and for precisely estimating simulated mutational processes data with known dimensions. The proposed UIK method is faster than conventional methods, including metrics utilizing the consensus matrix as a criterion for rank selection, while achieving significantly better computational efficiency without visual inspection on the curvatives. Finally, the suggested rank tuning method based on the elbow method for gene expression data is arguably theoretically superior to the cophenetic measure.

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Section: Discussionmentioning

confidence: 99%

Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection

Güven¹

2023

JMIR Bioinform Biotech

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“… Ye et al (2023) identified 10 shared hub genes between AD and T2D. Afzal et al (2023) uncovered the mutual genomics motifs between AD and T2D via non-negative matrix factorization, and screened of six shared genes. Kang et al (2022) constructed a PPI network consisting of AD and T2DM DEGs and found that the hub gene SLC2A2 (coding transmembrane carrier protein GLUT2), which connects the most DEGs in both AD and T2DM, plays a key regulator in linking T2DM and AD via glucose metabolism-related pathways.…”

Section: Identification Of Shared Candidate Biomarkers Between Ad And...mentioning

confidence: 99%

A review and analysis of key biomarkers in Alzheimer’s disease

Zhang,

Liu,

Zhang

et al. 2024

Front. Neurosci.

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein–protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

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Aronia Melanocarpa Elliot Anthocyanins Inhibits Alcoholic Liver Disease by Activation of α7nAChR

Wei,

Tang,

et al. 2024

Plant Foods Hum Nutr

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Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer’s disease

Cited by 8 publications

References 76 publications

Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection

Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection

A review and analysis of key biomarkers in Alzheimer’s disease

Aronia Melanocarpa Elliot Anthocyanins Inhibits Alcoholic Liver Disease by Activation of α7nAChR

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