Statistical Analysis of Microarray Data Clustering using NMF, Spectral Clustering, Kmeans, and GMM

Mirzal, Andri

doi:10.1109/tcbb.2020.3025486

Cited by 43 publications

(16 citation statements)

References 69 publications

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“…It should be pointed out that the NMF-based methods achieve strong and economic performance which is a leading factor in the widespread use of these techniques in many research fields, and particularly in computational biology [46,47,48]. A large number of studies in physiology and neuropsychology have presented evidence to propose that representing a non-negative matrix corresponding to a dataset by parts-based factors should be a proper approach to analyze the recognition system of the human brain [43].…”

Section: Matrix Factorizationmentioning

confidence: 99%

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Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Saberi-Movahed

Mohammadifard

Mehrpooya

et al. 2021

Preprint

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One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

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Section: Matrix Factorizationmentioning

confidence: 99%

“…in which n and d denote the number of samples and that of features, respectively. The To decompose a given (non-negative) matrix into the product of two low-rank (non-negative) matrices [24,27,36,37,38,39,40,41,42,43,44,45,46,47,48] Subspace Learning…”

Section: Notationsmentioning

confidence: 99%

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Saberi-Movahed

Mohammadifard

Mehrpooya

et al. 2021

Preprint

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“…A new genome-wide method is presented to identify genes with bimodal patterns of expression using GMM analysis (Titterington et al, 1986) for the stratification of samples. GMM has been previously used in the analysis of gene expression data (Ficklin et al, 2017;Golumbeanu et al, 2019;Mirzal, 2020) but to our knowledge this is the first application of such a method for the identification of genes with bimodal expression patterns. The applicability of the method is shown by using gene expression and clinical data for 25 tumor types available from TCGA.…”

Section: Discussionmentioning

confidence: 99%

An integrated approach to identify bimodal genes associated with prognosis in câncer

et al. 2021

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Bimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA). Bimodality was observed for 554 genes in expression data from 25 tumor types. Furthermore, 96 of these genes presented different prognosis when patients belonging to the two expression peaks were compared. The software to execute the method and the corresponding documentation are available at the Data access section.

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“…A new genome-wide method is presented to identify genes with bimodal patterns of expression using GMM analysis [14] for the stratification of samples. GMM has been previously used in the analysis of gene expression data [23][24][25] but to our knowledge this is the first application of such a method for the identification of genes with bimodal expression patterns.…”

Section: Discussionmentioning

confidence: 99%

A new genome-wide method to identify genes with bimodal gene expression

Justino

Reis²,

Fonseca³

et al. 2020

Preprint

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A new method is presented to detect bimodality in gene expression data using the Gaussian Mixture Models to cluster samples in each mode. We have used the method to search for bimodal genes in data from 25 tumor types available from The Cancer Genome Atlas. The method identified 554 genes with bimodal gene expression, of which 46 were identified in more than one cancer type. To further illustrate the impact of the method, we show that 96 out of the 554 genes with bimodal expression patterns presented different prognosis when patients belonging to the two expression peaks are compared. The software to execute the method and the corresponding documentation are available at https://github.com/LabBiosystemUFRN/Bimodality_Genes.

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Statistical Analysis of Microarray Data Clustering using NMF, Spectral Clustering, Kmeans, and GMM

Cited by 43 publications

References 69 publications

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

An integrated approach to identify bimodal genes associated with prognosis in câncer

A new genome-wide method to identify genes with bimodal gene expression

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