Assessment of non-negative matrix factorization for the preprocessing of long-term ECG

Guyot, Pauline; Djermoune, El‐Hadi; Bastogne, Thierry

doi:10.1016/j.vascn.2019.05.095

Cited by 4 publications

(3 citation statements)

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“…12 NMF approaches have been successfully applied as pre-processing steps for downstream machine learning classifiers. 19 We propose a pan-cancer framework to uncover cancer type-specific molecular mechanisms and identify drugs that could be repurposed ( Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

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Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

Gaudelet

Malod-Dognin

Pržulj

2021

Network and Systems Medicine

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With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data, to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms the competing methods and can identify new associations. Furthermore, it captures the underlying biology predictive of drug response. Through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problem.

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Section: Introductionmentioning

confidence: 99%

“… 12 NMF approaches have been successfully applied as pre-processing steps for downstream machine learning classifiers. 19 …”

Section: Introductionmentioning

confidence: 99%

Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

Gaudelet

Malod-Dognin

Pržulj

2021

Network and Systems Medicine

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“…However, unsupervised approaches often underperform supervised machine learning methods in classification tasks. Hence, NMF approaches have been combined with downstream machine learning classifiers 19 .…”

Section: Introductionmentioning

confidence: 99%

Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

Gaudelet¹,

Malod-Dognin²,

Pržulj³

2020

Preprint

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With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms competing methods and can identify new associations. Furthermore, through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problems.

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Identification of important risk factors for all-cause mortality of acquired long QT syndrome patients using random survival forests and non-negative matrix factorization

Cheng

Zhou

et al. 2021

Heart Rhythm

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Assessment of non-negative matrix factorization for the preprocessing of long-term ECG

Cited by 4 publications

References 0 publications

Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

Identification of important risk factors for all-cause mortality of acquired long QT syndrome patients using random survival forests and non-negative matrix factorization

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