Manuscript Details - Revision history

Priti at 19:49, 11 July 2011

2011-07-11T19:49:10Z

Priti at 19:44, 11 July 2011

2011-07-11T19:44:12Z

Priti at 19:39, 11 July 2011

2011-07-11T19:39:23Z

Priti at 19:37, 11 July 2011

2011-07-11T19:37:21Z

Priti at 19:35, 11 July 2011

2011-07-11T19:35:50Z

Priti at 19:33, 11 July 2011

2011-07-11T19:33:49Z

Priti at 19:30, 11 July 2011

2011-07-11T19:30:53Z

Priti at 19:18, 11 July 2011

2011-07-11T19:18:00Z

Matthan at 15:21, 10 June 2011

2011-06-10T15:21:01Z

Matthan at 15:20, 10 June 2011

2011-06-10T15:20:44Z

@@ Line 8: / Line 8: @@
    |title_background=#b0e2ff
    |content=
-Research in the parasite domain requires analyzing experimental lab data along with relevant public data resources.  This task is difficult for biologists who may not possess adequate computational skills to process all the data that are in different format and stored at different locations.  We, therefore, developed an intuitive and easy to use semantic problem solving environment (SPSE) for parasite research where researchers may query their lab data that is integrated with public data resources using ontologies. Other features of SPSE include integrated support for capturing and querying provenance information, and a visual query-processing tool that allows biologists to formulate complex queries without learning the query language syntax.  We demonstrate the significance of SPSE by using it to query integrated data to identify gene knockout and/or other intervention (i.e., drug or vaccination) targets for ''T. cruzi''.  These queries help parasite researchers discover new or existing knowledge, which is implicitly present in the data.  The evaluation results of the SPSE demonstrate improved usability than existing systems/approaches and support for design of complex queries, which was not present earlier.  This work is currently under review for the publication in the PLoS Neglected Tropical Diseases journal.
+Research in the parasite domain requires analyzing experimental lab data along with relevant public data resources.  This task is difficult for biologists who may not possess adequate computational skills to process all the data that are in different format and stored at different locations.  We, therefore, developed an intuitive and easy to use semantic problem solving environment (SPSE) for parasite research where researchers may query their lab data that is integrated with public data resources using ontologies. Other features of SPSE include integrated support for capturing and querying provenance information, and a visual query-processing tool that allows biologists to formulate complex queries without learning the query language syntax.  We demonstrate the significance of SPSE by using it to query integrated data to identify gene knockout and/or other intervention (i.e., drug or vaccination) targets for ''T. cruzi''.  These queries help parasite researchers discover new or existing knowledge, which is implicitly present in the data.  The evaluation results of the SPSE demonstrate improved usability than existing systems/approaches and support for design of complex queries, which was not present earlier.  This work was completed in collaboration with [http://www.ctegd.uga.edu Center for Tropical and Emerging Global Diseases(CTEGD), University of Georgia] and [http://thinc.cs.uga.edu Intelligent Thought and Action Lab, THINC Lab, University of Georgia], and is currently under review for the publication in the PLoS Neglected Tropical Diseases journal.
 Below we mention all three queries along with Cuebee demo that shows how complex SPARQL queries can be formulated easily with minimal skills and ontology background.

@@ Line 8: / Line 8: @@
    |title_background=#b0e2ff
    |content=
-Research in the parasite domain requires analyzing experimental lab data along with relevant public data resources.  This task is difficult for biologists who may not possess adequate computational skills to process all the data that are in different format and stored at different locations.  We, therefore, developed an intuitive and easy to use semantic problem solving environment (SPSE) for parasite research where researchers may query their lab data that is integrated with public data resources using ontologies. Other features of SPSE include integrated support for capturing and querying provenance information, and a visual query-processing tool that allows biologists to formulate complex queries without learning the query language syntax.  We demonstrate the significance of SPSE by using it to query integrated data to identify gene knockout and/or other intervention (i.e., drug or vaccination) targets for ''T. cruzi''.  These queries help parasite researchers discover new or existing knowledge, which is implicitly present in the data.  The evaluation results of the SPSE demonstrate improved usability than existing systems/approaches and support for design of complex queries, which was not present earlier.
+Research in the parasite domain requires analyzing experimental lab data along with relevant public data resources.  This task is difficult for biologists who may not possess adequate computational skills to process all the data that are in different format and stored at different locations.  We, therefore, developed an intuitive and easy to use semantic problem solving environment (SPSE) for parasite research where researchers may query their lab data that is integrated with public data resources using ontologies. Other features of SPSE include integrated support for capturing and querying provenance information, and a visual query-processing tool that allows biologists to formulate complex queries without learning the query language syntax.  We demonstrate the significance of SPSE by using it to query integrated data to identify gene knockout and/or other intervention (i.e., drug or vaccination) targets for ''T. cruzi''.  These queries help parasite researchers discover new or existing knowledge, which is implicitly present in the data.  The evaluation results of the SPSE demonstrate improved usability than existing systems/approaches and support for design of complex queries, which was not present earlier.  This work is currently under review for the publication in the PLoS Neglected Tropical Diseases journal.
 Below we mention all three queries along with Cuebee demo that shows how complex SPARQL queries can be formulated easily with minimal skills and ontology background.

@@ Line 16: / Line 16: @@
    |title_background=#ffffcc
    |content=
-[http://jade.cs.uga.edu:8080/download/query1.html Query 1 Demo] <br />
+[http://jade.cs.uga.edu:8080/download/query1.html Query 1, Cuebee Demo] <br />
 [http://jade.cs.uga.edu:8080/download/query1.avi Download High Quality Video]
@@ Line 75: / Line 75: @@
    |title_background=#ffffcc
    |content=
-[http://jade.cs.uga.edu:8080/download/query2.html Query 2] <br />
+[http://jade.cs.uga.edu:8080/download/query2.html Query 2, Cuebee demo] <br />
 [http://jade.cs.uga.edu:8080/download/query2.avi Download High Quality Video]
@@ Line 136: / Line 136: @@
    |title_background=#ffffcc
    |content=
-[http://jade.cs.uga.edu:8080/download/query3.html Query 3] <br />
+[http://jade.cs.uga.edu:8080/download/query3.html Query 3, Cuebee demo] <br />
 [http://jade.cs.uga.edu:8080/download/query3.avi Download High Quality Video]

@@ Line 16: / Line 16: @@
    |title_background=#ffffcc
    |content=
-Cuebee Query: <br />
+[http://jade.cs.uga.edu:8080/download/query1.html Query 1 Demo] <br />
 [http://jade.cs.uga.edu:8080/download/query1.html Query 1] <br />
 [http://jade.cs.uga.edu:8080/download/query1.avi Download High Quality Video]
@@ Line 76: / Line 75: @@
    |title_background=#ffffcc
    |content=
 [http://jade.cs.uga.edu:8080/download/query2.html Query 2] <br />
 [http://jade.cs.uga.edu:8080/download/query2.avi Download High Quality Video]
@@ Line 138: / Line 136: @@
    |title_background=#ffffcc
    |content=
 [http://jade.cs.uga.edu:8080/download/query3.html Query 3] <br />
 [http://jade.cs.uga.edu:8080/download/query3.avi Download High Quality Video]

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 {| style="width:100%; vertical-align:top;"
 {{block
-   |title=A Semantic Problem Solving Environment for Integrative Parasite Research: Identification of Intervention Targets for Trypanosoma cruzi.
+   |title=A Semantic Problem Solving Environment for Integrative Parasite Research: Identification of Intervention Targets for ''Trypanosoma cruzi''.
    |title_background=#b0e2ff
    |content=
-Research in the parasite domain requires analyzing experimental lab data along with relevant public data resources.  This task is difficult for biologists who may not possess adequate computational skills to process all the data that are in different format and stored at different locations.  We, therefore, developed an intuitive and easy to use semantic problem solving environment (SPSE) for parasite research where researchers may query their lab data that is integrated with public data resources using ontologies. Other features of SPSE include integrated support for capturing and querying provenance information, and a visual query-processing tool that allows biologists to formulate complex queries without learning the query language syntax.  We demonstrate the significance of SPSE by using it to query integrated data to identify gene knockout and/or other intervention (i.e., drug or vaccination) targets for T. cruzi.  These queries help parasite researchers discover new or existing knowledge, which is implicitly present in the data.  The evaluation results of the SPSE demonstrate improved usability than existing systems/approaches and support for design of complex queries, which was not present earlier.
+Research in the parasite domain requires analyzing experimental lab data along with relevant public data resources.  This task is difficult for biologists who may not possess adequate computational skills to process all the data that are in different format and stored at different locations.  We, therefore, developed an intuitive and easy to use semantic problem solving environment (SPSE) for parasite research where researchers may query their lab data that is integrated with public data resources using ontologies. Other features of SPSE include integrated support for capturing and querying provenance information, and a visual query-processing tool that allows biologists to formulate complex queries without learning the query language syntax.  We demonstrate the significance of SPSE by using it to query integrated data to identify gene knockout and/or other intervention (i.e., drug or vaccination) targets for ''T. cruzi''.  These queries help parasite researchers discover new or existing knowledge, which is implicitly present in the data.  The evaluation results of the SPSE demonstrate improved usability than existing systems/approaches and support for design of complex queries, which was not present earlier.
-Below we mention all three queries along with Cuebee demo that shows how complex SPARQL queries can be formulated with a little knowledge about the ontology.
+Below we mention all three queries along with Cuebee demo that shows how complex SPARQL queries can be formulated easily with minimal skills and ontology background.
 }}
 {{block

@@ Line 4: / Line 4: @@
 | style="width:60%; border:1px solid #cef2e0; background:#cef2e0; vertical-align:top; color:#000;" |
 {| style="width:100%; vertical-align:top;"
 {{block
    |title=Query 1: Show proteins that are downregulated in the epimastigote stage and exist in a single metabolic pathway.