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Open Access Highly Accessed Methodology article

Computational prediction of associations between long non-coding RNAs and proteins

Qiongshi Lu125, Sijin Ren13, Ming Lu1, Yong Zhang4, Dahai Zhu4, Xuegong Zhang2 and Tingting Li13*

Author Affiliations

1 Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China

2 MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China

3 Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China

4 National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China

5 Current address: Department of Biostatistics, Yale University, New Haven, CT 06511, USA

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BMC Genomics 2013, 14:651  doi:10.1186/1471-2164-14-651

Published: 24 September 2013

Additional files

Additional file 1: Table S1:

The number of sequences contained in 44 complexes.

Format: XLSX Size: 10KB Download file

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Additional file 2: Table S2:

The number of sequences in the remaining 18 complexes.

Format: XLSX Size: 9KB Download file

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Additional file 3: Figure S1:

Distribution of Interaction Score. The distribution of predicted interaction scores for the shuffled set. The shuffled set was got by randomizing all pairs in the non-redundant negative training set.

Format: DOCX Size: 123KB Download file

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Additional file 4: Table S3:

ID list of proteins.

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Additional file 5: Table S4:

Test result of nuclear proteins.

Format: XLSX Size: 12KB Download file

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Additional file 6: Table S5:

Test result of RNA-binding proteins.

Format: XLSX Size: 12KB Download file

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