Translational Bioinformatics Curricula in Graduate Biomedical Informatics Programs

Translational Bioinformatics and Clinical Research (Biomedical) Informatics

Surgical Pathology Clinics ◽

10.1016/j.path.2015.02.015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 269-288 ◽

Cited By ~ 4

Author(s):

S. Joseph Sirintrapun ◽

Ahmet Zehir ◽

Aijazuddin Syed ◽

JianJiong Gao ◽

Nikolaus Schultz ◽

...

Keyword(s):

Clinical Research ◽

Biomedical Informatics ◽

Translational Bioinformatics

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Data Analysis and Data Mining: Current Issues in Biomedical Informatics

Methods of Information in Medicine ◽

10.3414/me11-06-0002 ◽

2011 ◽

Vol 50 (06) ◽

pp. 536-544 ◽

Cited By ~ 37

Author(s):

M. Diomidous ◽

I. N. Sarkar ◽

K. Takabayashi ◽

A. Ziegler ◽

A. T. McCray ◽

...

Keyword(s):

Data Mining ◽

Decision Making ◽

Data Analysis ◽

Knowledge Integration ◽

Biomedical Informatics ◽

Data Driven ◽

Biomedical Data ◽

Translational Bioinformatics ◽

Biomedical Sciences ◽

Mining Methods

SummaryBackground: Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research.Objectives: To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. Methods: On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field.Results: The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology.Conclusions: Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.

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Translational Bioinformatics and Clinical Research (Biomedical) Informatics

Clinics in Laboratory Medicine ◽

10.1016/j.cll.2015.09.013 ◽

2016 ◽

Vol 36 (1) ◽

pp. 153-181 ◽

Cited By ~ 4

Author(s):

S. Joseph Sirintrapun ◽

Ahmet Zehir ◽

Aijazuddin Syed ◽

JianJiong Gao ◽

Nikolaus Schultz ◽

...

Keyword(s):

Clinical Research ◽

Biomedical Informatics ◽

Translational Bioinformatics

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Big Data and Biomedical Informatics: A Challenging Opportunity

Yearbook of Medical Informatics ◽

10.15265/iy-2014-0024 ◽

2014 ◽

Vol 23 (01) ◽

pp. 08-13 ◽

Cited By ~ 60

Author(s):

Riccardo Bellazzi

Keyword(s):

Big Data ◽

Biomedical Informatics ◽

Concept Drift ◽

Clinical Care ◽

Healthcare Delivery ◽

Data Access ◽

Machine Learning Algorithms ◽

Translational Bioinformatics ◽

Proper Actions ◽

Research Studies

SummaryBig data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations.

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