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Open Access Research

Research prioritization through prediction of future impact on biomedical science: a position paper on inference-analytics

Madhavi K Ganapathiraju12* and Naoki Orii12

Author Affiliations

1 Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15206, USA

2 Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA

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GigaScience 2013, 2:11  doi:10.1186/2047-217X-2-11

Published: 30 August 2013

Additional files

Additional file 1:

100 most-impactful interactions as predicted by the model, as well as predicted impact scores for all interactions sorted in descending order by predicted impact.

Format: TXT Size: 1.5MB Download file

Open Data

Additional file 2:

Enriched Gene Ontology terms (biological process) for the top 50 high-impact interactions.

Format: TXT Size: 5KB Download file

Open Data

Additional file 3:

Enriched Gene Ontology terms (molecular function) for the top 50 high-impact interactions.

Format: TXT Size: 1KB Download file

Open Data

Additional file 4:

Enriched Gene Ontology terms (cellular component) for the top 50 high-impact interactions.

Format: TXT Size: 1KB Download file

Open Data

Additional file 5:

Cytoscape session file showing enriched Gene Ontology biological process terms, output by BiNGO.

Format: ZIP Size: 189KB Download file

Open Data

Additional file 6:

Cytoscape session file showing enriched Gene Ontology molecular function terms, output by BiNGO.

Format: ZIP Size: 169KB Download file

Open Data

Additional file 7:

Cytoscape session file showing enriched Gene Ontology cellular component terms, output by BiNGO.

Format: ZIP Size: 187KB Download file

Open Data

Additional file 8:

Of the top 50 high-impact interactions, those in which one or both proteins are among the GWAS-genes are shown.

Format: XLSX Size: 12KB Download file

Open Data