Back to the future - The integration of big data with machine learning is re-establishing the importance of predictive correlations in ovarian cancer diagnostics and therapeutics

2018 ◽  
Vol 149 (2) ◽  
pp. 230-231 ◽  
Author(s):  
John F. McDonald
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Big Data ◽  
Cancer Diagnostics ◽  
Ovarian Cancer Diagnostics ◽  
The Future

scholarly journals Ovarian Cancer Biomarkers: Current Options and Future Promise

2008 ◽  
Vol 6 (8) ◽  
pp. 795-802 ◽  
Author(s):  
Christine M. Coticchia ◽  
Jiang Yang ◽  
Marsha A. Moses
Keyword(s):  
Ovarian Cancer ◽  
Disease Progression ◽  
Cancer Detection ◽  
Therapeutic Efficacy ◽  
Cancer Biomarkers ◽  
Cancer Diagnostics ◽  
Early Disease ◽  
Molecular Approaches ◽  
Ovarian Cancer Diagnostics ◽  
Potential Biomarkers

As more effective, less toxic cancer drugs reach patients, the need for accurate and reliable cancer diagnostics and prognostics has become widely appreciated. Nowhere is this need more dire than in ovarian cancer; here most women are diagnosed late in disease progression. The ability to sensitively and specifically predict the presence of early disease and its status, stage, and associated therapeutic efficacy has the potential to revolutionize ovarian cancer detection and treatment. This article reviews current ovarian cancer diagnostics and prognostics and potential biomarkers that are being studied and validated. Some of the most recent molecular approaches being used to identify genes and proteins are presented, which may represent the next generation of ovarian cancer diagnostics and prognostics.

Using Big Data and Machine Learning in Personality Measurement: Opportunities and Challenges

2020 ◽  
Vol 34 (5) ◽  
pp. 632-648
Author(s):  
Leo Alexander ◽  
Evan Mulfinger ◽  
Frederick L. Oswald
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
European Association ◽  
Reliability And Validity ◽  
Personality Measurement ◽  
Personality Psychology ◽  
Personality Research ◽  
The Future ◽  
Challenges And Opportunities ◽  
Conceptual Paper

This conceptual paper examines the promises and critical challenges posed by contemporary personality measurement using big data. More specifically, the paper provides (i) an introduction to the type of technologies that give rise to big data, (ii) an overview of how big data is used in personality research and how it might be used in the future, (iii) a framework for approaching big data in personality science, (iv) an exploration of ideas that connect psychometric reliability and validity, as well as principles of fairness and privacy, to measures of personality that use big data, (v) a discussion emphasizing the importance of collaboration with other disciplines for personality psychologists seeking to adopt big data methods, and finally, (vi) a list of practical considerations for researchers seeking to move forward with big data personality measurement and research. It is expected that this paper will provide insights, guidance, and inspiration that helps personality researchers navigate the challenges and opportunities posed by using big data methods in personality measurement. © 2020 European Association of Personality Psychology

Digital Epidemiology of Innovation (Preprint)

2021 ◽  
Author(s):  
Ivan Triana ◽  
LUIS PINO ◽  
Dennise Rubio
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Management ◽  
Medical Practice ◽  
Data Science ◽  
Medical Literature ◽  
The Future ◽  
Digital Epidemiology ◽  
Definition Of ◽  
And Control

UNSTRUCTURED Bio and infotech revolution including data management are global tendencies that have a relevant impact on healthcare. Concepts such as Big Data, Data Science and Machine Learning are now topics of interest within medical literature. All of them are encompassed in what recently is named as digital epidemiology. The purpose of this article is to propose our definition of digital epidemiology with the inclusion of a further aspect: Innovation. It means Digital Epidemiology of Innovation (DEI) and show the importance of this new branch of epidemiology for the management and control of diseases. In this sense, we will describe all characteristics concerning to the topic, current uses within medical practice, application for the future and applicability of DEI as conclusion.

scholarly journals Apolipoprotein A1 and Transferrin as Biomarkers in Ovarian Cancer Diagnostics

2010 ◽  
Vol 10 (2) ◽  
Author(s):  
Ronalds Macuks ◽  
Ieva Baidekalna ◽  
Julia Gritcina ◽  
Arina Avdejeva ◽  
Simona Donina
Keyword(s):  
Ovarian Cancer ◽  
Cancer Diagnostics ◽  
Apolipoprotein A1 ◽  
Ovarian Cancer Diagnostics

Modern Statistical Modeling in Machine Learning and Big Data Analytics

2020 ◽  
pp. 135-151
Author(s):  
Niloofar Ramezani
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Statistical Modeling ◽  
Statistical Models ◽  
Data Analytics ◽  
High Dimensional Data ◽  
Big Data Analytics ◽  
High Dimensional ◽  
Statistical Techniques ◽  
The Future

Machine learning, big data, and high dimensional data are the topics we hear about frequently these days, and some even call them the wave of the future. Therefore, it is important to use appropriate statistical models, which have been established for many years, and their efficiency has already been evaluated to contribute into advancing machine learning, which is a relatively newer field of study. Different algorithms that can be used within machine learning, depending on the nature of the variables, are discussed, and appropriate statistical techniques for modeling them are presented in this chapter.

scholarly journals Programmable Bio-Nano-Chip Systems for Serum CA125 Quantification: Toward Ovarian Cancer Diagnostics at the Point-of-Care

2012 ◽  
Vol 5 (5) ◽  
pp. 706-716 ◽  
Author(s):  
Archana Raamanathan ◽  
Glennon W. Simmons ◽  
Nicolaos Christodoulides ◽  
Pierre N. Floriano ◽  
Wieslaw B. Furmaga ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Point Of Care ◽  
Cancer Diagnostics ◽  
Ovarian Cancer Diagnostics ◽  
Serum Ca125
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