Title |
An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era
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Published in |
Genome Biology, December 2014
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DOI | 10.1186/s13059-014-0523-y |
Pubmed ID | |
Authors |
Zhenqiang Su, Hong Fang, Huixiao Hong, Leming Shi, Wenqian Zhang, Wenwei Zhang, Yanyan Zhang, Zirui Dong, Lee J Lancashire, Marina Bessarabova, Xi Yang, Baitang Ning, Binsheng Gong, Joe Meehan, Joshua Xu, Weigong Ge, Roger Perkins, Matthias Fischer, Weida Tong |
Abstract |
Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? |
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Geographical breakdown
Country | Count | As % |
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United States | 2 | 22% |
United Kingdom | 2 | 22% |
France | 1 | 11% |
Italy | 1 | 11% |
Unknown | 3 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 44% |
Scientists | 4 | 44% |
Science communicators (journalists, bloggers, editors) | 1 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 2% |
Spain | 2 | 1% |
Chile | 1 | <1% |
Germany | 1 | <1% |
Denmark | 1 | <1% |
Unknown | 137 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 33 | 23% |
Researcher | 30 | 21% |
Student > Master | 17 | 12% |
Student > Bachelor | 11 | 8% |
Student > Doctoral Student | 7 | 5% |
Other | 18 | 12% |
Unknown | 29 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 35 | 24% |
Biochemistry, Genetics and Molecular Biology | 30 | 21% |
Medicine and Dentistry | 12 | 8% |
Computer Science | 12 | 8% |
Engineering | 6 | 4% |
Other | 14 | 10% |
Unknown | 36 | 25% |