Correspondence
Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach
-
* Corresponding author: Vasant Honavar honavar@cs.iastate.edu
1 Artificial Intelligence Laboratory, Department of Computer Science, Iowa State University, Ames, Iowa, 50011, USA
2 Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa, 50011, USA
3 Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, Iowa, 50011, USA
4 Center for Computational Intelligence, Learning, and Discovery, Iowa State University, Ames, Iowa, 50011, USA
BMC Bioinformatics 2007, 8:284 doi:10.1186/1471-2105-8-284
Published: 3 August 2007Additional files
Additional file 1:
Supplementary Table 1: Evidence Codes for AmiGO annotations. A table displaying the Evidence Codes for AmiGO annotations of the mouse protein kinases used in this study.
Format: PDF Size: 14KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 2:
Supplementary Table 2: AmiGO annotations versus UniProt annotations (with UniProt Evidence). A table comparing the annotations found in the AmiGO server with the annotations found in UniProt.
Format: PDF Size: 18KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 3:
Supplementary Table 3: AmiGO labels, UniProt labels, and Predicted Labels for each mouse kinase protein. A table comparing the predicted annotations from our three machine learning classifiers with the annotations of AmiGO and UniProt.
Format: PDF Size: 15KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 4:
Supplementary Data: Machine learning approaches to predict Gene Ontology and/or UniProt Functional labels. The data provided represent the results and performance of all the machine learning approaches used in this study.
Format: PDF Size: 48KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 5:
Supplementary Table 4: Mouse kinases having a human ortholog. A table displaying the human orthologs for the mouse kinases used in this study. The table also displays the identity between these orthologs.
Format: PDF Size: 19KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 6:
Supplementary Table 5: Number of mouse kinases having a specified level of sequence identity with their human orthologs. A table displaying the summary statistics of Supplementary Table 4.
Format: PDF Size: 5KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 7:
Supplementary Note. Because there is only a non-curated reference to the work done on "Rat ISS GO annotations from MGI's mouse gene data," we provide the abstract and a link to the original reference report in this file.
Format: PDF Size: 5KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 8:
Supplementary Table 6: The UniProt and AmiGO annotations for the rat kinase proteins with mouse orthologs. This table displays the UniProt and AmiGO annotations for rat kinase proteins that were annotated based on a mouse ortholog.
Format: PDF Size: 23KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 9:
Supplementary Table 7: Distribution of protein classes for human and mouse proteins annotated by AmiGO, UniProt, and HDTree. This table is a representation of the data used in Figure 1 which is a pie chart showing the distribution of human and mouse protein classes based on annotations found in AmiGO, UniProt, and predicted by HDTree.
Format: PDF Size: 6KB Download file
This file can be viewed with: Adobe Acrobat Reader