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Mendeley readers
Title |
A novel neural response algorithm for protein function prediction
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Published in |
BMC Systems Biology, July 2012
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DOI | 10.1186/1752-0509-6-s1-s19 |
Pubmed ID | |
Authors |
Hari Krishna Yalamanchili, Quan-Wu Xiao, Junwen Wang |
Abstract |
Large amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOFigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction. |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 5% |
Hong Kong | 1 | 5% |
Unknown | 18 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 7 | 35% |
Student > Ph. D. Student | 3 | 15% |
Researcher | 3 | 15% |
Student > Bachelor | 2 | 10% |
Student > Postgraduate | 1 | 5% |
Other | 0 | 0% |
Unknown | 4 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 7 | 35% |
Agricultural and Biological Sciences | 4 | 20% |
Biochemistry, Genetics and Molecular Biology | 3 | 15% |
Decision Sciences | 1 | 5% |
Medicine and Dentistry | 1 | 5% |
Other | 0 | 0% |
Unknown | 4 | 20% |