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Translational Informatics in Smart Healthcare

Overview of attention for book
Attention for Chapter 2: Genetic Test, Risk Prediction, and Counseling
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Chapter title
Genetic Test, Risk Prediction, and Counseling
Chapter number 2
Book title
Translational Informatics in Smart Healthcare
Published in
Advances in experimental medicine and biology, January 2017
DOI 10.1007/978-981-10-5717-5_2
Pubmed ID
Book ISBNs
978-9-81-105716-8, 978-9-81-105717-5
Authors

Maggie Haitian Wang, Haoyi Weng

Abstract

Advancement in technology has nurtured the new era of genetic tests for personalized medicine. In this chapter, we will introduce the current development, challenges, and the outlook of genetic test, disease risk prediction, and genetic counseling. In the first section, we will present the success cases in the areas of molecular classification of tumors, pharmacogenomics, and Mendelian disorders, and the challenges of genetic tests implementations. In the second section, common methods for genetic risk prediction models and evaluation measures will be introduced, as well as challenges in feature reliability, risk model stability, and clinical utility. In the final section, key components of genetic counseling will be introduced, covering individual communications, psychosocial concerns, risk assessments, and follow-ups. Current evidences have shown a promising future for genetic testing and risk prediction; we expect that the advancement of analytical methods, technology, integration of omics data, and the increasing clinical implementation and regulation will continue to pave the way for precision medicine in future.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 16%
Student > Master 3 12%
Student > Bachelor 3 12%
Other 1 4%
Student > Doctoral Student 1 4%
Other 1 4%
Unknown 12 48%
Readers by discipline Count As %
Medicine and Dentistry 6 24%
Biochemistry, Genetics and Molecular Biology 3 12%
Computer Science 3 12%
Engineering 2 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 0 0%
Unknown 10 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 September 2017.
All research outputs
#20,447,499
of 23,002,898 outputs
Outputs from Advances in experimental medicine and biology
#3,986
of 4,961 outputs
Outputs of similar age
#356,155
of 421,223 outputs
Outputs of similar age from Advances in experimental medicine and biology
#414
of 490 outputs
Altmetric has tracked 23,002,898 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,961 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,223 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 490 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.