scholarly journals Genetic Data Aren't So Special: Causes and Implications of Reidentification

2020 ◽  
Vol 50 (5) ◽  
pp. 30-39
Author(s):  
T.J. Kasperbauer ◽  
Peter H. Schwartz
Keyword(s):  
Genetic Data ◽  
Special Causes

Genetic Data and the Law

2012 ◽  
Author(s):  
Mark Taylor
Keyword(s):  
Genetic Data ◽  
The Law

Don’t ignore genetic data from minority populations

Nature ◽  
2020 ◽  
Vol 585 (7824) ◽  
pp. 184-186 ◽  
Author(s):  
Chief Ben-Eghan ◽  
Rosie Sun ◽  
Jose Sergio Hleap ◽  
Alex Diaz-Papkovich ◽  
Hans Markus Munter ◽  
...  
Keyword(s):  
Genetic Data ◽  
Minority Populations

UMD-MEN1 database: analysis of clinical and genetic data from 1,676 patients by the TENGEN network

2019 ◽  
Author(s):  
Pauline Romanet ◽  
Sophie Giraud ◽  
Marie-francoise Odou ◽  
Marie-Odile North ◽  
Amira Mohamed ◽  
...  
Keyword(s):  
Genetic Data ◽  
Database Analysis

ANALYSIS OF GENETIC DATA FROM THE UKPID REGISTRY 2017

2017 ◽  
Author(s):  
Bangs Catherine
Keyword(s):  
Genetic Data

Maintenance of a Genetic Data Set on Multiple Data Base and Statistical Analysis Systems

1982 ◽  
Vol 61 (s109) ◽  
pp. 34-34
Author(s):  
Samuel J. Agronow ◽  
Federico C. Mariona ◽  
Frederick C. Koppitch ◽  
Kazutoshi Mayeda
Keyword(s):  
Statistical Analysis ◽  
Data Base ◽  
Genetic Data ◽  
Data Set ◽  
Multiple Data

Probability models for design and analysis of genetic data

2003 ◽  
Author(s):  
Hongmei Zhang
Keyword(s):  
Genetic Data ◽  
Probability Models

Protecting the Genetic Data of Unborn Children:

2018 ◽  
Vol 4 (1) ◽  
pp. 53-64 ◽  
Author(s):  
K. Pormeister ◽  
Ů Drożdżowski
Keyword(s):  
Genetic Data

The Power of Matrix Factorization: Methods for Deconvoluting Genetic Heterogeneous Data at Expression Level

2021 ◽  
Vol 15 (8) ◽  
pp. 841-853
Author(s):  
Yuan Liu ◽  
Zhining Wen ◽  
Menglong Li
Keyword(s):  
Artificial Intelligence ◽  
Matrix Factorization ◽  
In Silico ◽  
Genetic Data ◽  
Heterogeneous Data ◽  
Expression Level ◽  
Mathematical Methods ◽  
Challenges And Opportunities ◽  
Factorization Methods ◽  
Genetic Profiles

Background:: The utilization of genetic data to investigate biological problems has recently become a vital approach. However, it is undeniable that the heterogeneity of original samples at the biological level is usually ignored when utilizing genetic data. Different cell-constitutions of a sample could differentiate the expression profile, and set considerable biases for downstream research. Matrix factorization (MF) which originated as a set of mathematical methods, has contributed massively to deconvoluting genetic profiles in silico, especially at the expression level. Objective: With the development of artificial intelligence algorithms and machine learning, the number of computational methods for solving heterogeneous problems is also rapidly abundant. However, a structural view from the angle of using MF to deconvolute genetic data is quite limited. This study was conducted to review the usages of MF methods on heterogeneous problems of genetic data on expression level. Methods: MF methods involved in deconvolution were reviewed according to their individual strengths. The demonstration is presented separately into three sections: application scenarios, method categories and summarization for tools. Specifically, application scenarios defined deconvoluting problem with applying scenarios. Method categories summarized MF algorithms contributed to different scenarios. Summarization for tools listed functions and developed web-servers over the latest decade. Additionally, challenges and opportunities of relative fields are discussed. Results and Conclusion: Based on the investigation, this study aims to present a relatively global picture to assist researchers to achieve a quicker access of deconvoluting genetic data in silico, further to help researchers in selecting suitable MF methods based on the different scenarios.

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