scholarly journals Nonnegative Matrix Factorization Model-Based Construction For Molecular Clustering and Prognostic Assessment of Squamous Carcinoma of Head and Neck

2021 ◽  
Author(s):  
Xin-Yu Li ◽  
Xi-Tao Yang
Keyword(s):  
Head And Neck ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Cox Regression ◽  
Nonnegative Matrix ◽  
Squamous Carcinoma ◽  
Prognostic Models ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Prognostic Assessment

Abstract Purpose: Exploring nonnegative matrix factorization (NMF) model-based clustering and prognostic modeling of head and neck squamous carcinoma (HNSCC). Methods: The transcriptome microarray data of HNSCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Shanghai Ninth People’s Hospital, and NMF clustering was constructed using the R software package. Relevant prognostic models were developed based on clustering. Results: Based on NMF, all samples were divided into 2 subgroups. Predictive models were constructed by analysing the differential gene between the two subgroups. Results of survival analysis in the current study revealed that the high-risk group had a poor prognosis. Further, results of multi-factor Cox regression analysis revealed that the predictive model was an independent predictor of prognosis. Conclusion: It was evident that the NMF-based prognostic model is a useful guide to the prognostic assessment of HNSCC.

scholarly journals Model-Based Method for Acoustic Echo Cancelation and Near-End Speaker Extraction: Non-negative Matrix Factorization

2018 ◽  
Vol 2 ◽  
pp. 15-22
Author(s):  
Pallavi Agrawal ◽  
Madhu Shandilya
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Audio Signal ◽  
Nonnegative Matrix ◽  
Adaptive Algorithms ◽  
Full Duplex ◽  
Positive Real ◽  
Model Based ◽  
Acoustic Echo ◽  
Non Negative Matrix Factorization

Rapid escalation of wireless communication and hands-free telephony creates a problem with acoustic echo in full-duplex communication applications. In this paper a simulation of model-based acoustic echo cancelation and near-end speaker extraction using statistical methods relying on nonnegative matrix factorization (NMF) is proposed. Acoustic echo cancelation using the NMF algorithm is developed and its implementation is presented, along with all positive, real time elements and factorization techniques. Experimental results are compared against the widely used existing adaptive algorithms which have a disadvantage in terms of long impulse response, increased computational load and wrong convergence due to change in near-end enclosure. All these shortcomings have been eliminated in the statistical method of NMF that reduces echo and enhances audio signal processing.

scholarly journals Development and validation of an immune-related gene prognostic model for stomach adenocarcinoma

2020 ◽  
Vol 40 (10) ◽  
Author(s):  
Ming Wu ◽  
Yu Xia ◽  
Yadong Wang ◽  
Fei Fan ◽  
Xian Li ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Models ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Immune Related Gene ◽  
Stomach Adenocarcinoma

Abstract Purpose: Stomach adenocarcinoma (STAD) is one of the most common malignant tumors, and its occurrence and prognosis are closely related to inflammation. The aim of the present study was to identify gene signatures and construct an immune-related gene (IRG) prognostic model in STAD using bioinformatics analysis. Methods: RNA sequencing data from healthy samples and samples with STAD, IRGs, and transcription factors were analyzed. The hub IRGs were identified using univariate and multivariate Cox regression analyses. Using the hub IRGs, we constructed an IRG prognostic model. The relationships between IRG prognostic models and clinical data were tested. Results: A total of 289 differentially expressed IRGs and 20 prognostic IRGs were screened with a threshold of P<0.05. Through multivariate stepwise Cox regression analysis, we developed a prognostic model based on seven IRGs. The prognostic model was validated using a GEO dataset (GSE 84437). The IRGs were significantly correlated with the clinical outcomes (age, histological grade, N, and M stage) of STAD patients. The infiltration abundances of dendritic cells and macrophages were higher in the high-risk group than in the low-risk group. Conclusions: Our results provide novel insights into the pathogenesis of STAD. An IRG prognostic model based on seven IRGs exhibited the predictive value, and have potential application value in clinical decision-making and individualized treatment.

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