Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification-Reference-Cited by-同舟云学术

Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification

Published:2016-09 Issue:1 Volume:6 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Borgmann Daniela M.,Mayr Sandra,Polin Helene,Schaller Susanne,Dorfer Viktoria,Obritzberger Lisa,Endmayr Tanja,Gabriel Christian,Winkler Stephan M.,Jacak Jaroslaw

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/srep32317.pdf

Reference58 articles.

1. Landsteiner, K. & Wiener, A. S. An agglutinable factor in human blood recognized by immune sera for rhesus blood. Proc. Soc. Exp. Biol. Med. 43, 41–42 (1940).

2. Avent, N. D. & Reid, M. E. The Rh blood group system: a review. Blood 95, 375–387 (2000).

3. Tippett, P. A speculative model for the Rh blood groups. Ann. Hum. Genet. 50, 241–247 (1986).

4. Patnaik, S. K., Helmberg, W. & Blumenfeld, O. BGMUT: NCBI dbRBC database of allelic variations of genes encoding antigens of blood group systems. Nucleic Acids Res. 40, D1023–1029 (2012).

5. Ostgård, P., Fevang, F. & Kornstad, L. Anti-D in a ‘D positive’ mother giving rise to severe haemolytic disease of the newborn. A dilemma in antenatal immunohaematological testing. Acta. Paediatr. Scand. 75, 175–178 (1986).

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