Text Mining in Cancer Gene and Pathway Prioritization
Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been develo...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Libertas Academica, Ltd.
2014
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| Online Access: | http://hdl.handle.net/1721.1/92503 https://orcid.org/0000-0003-0195-7456 https://orcid.org/0000-0001-8411-6403 |
| _version_ | 1826195197802840064 |
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| author | Luo, Yuan Riedlinger, Gregory Szolovits, Peter |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Luo, Yuan Riedlinger, Gregory Szolovits, Peter |
| author_sort | Luo, Yuan |
| collection | MIT |
| description | Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes. |
| first_indexed | 2024-09-23T10:09:15Z |
| format | Article |
| id | mit-1721.1/92503 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:09:15Z |
| publishDate | 2014 |
| publisher | Libertas Academica, Ltd. |
| record_format | dspace |
| spelling | mit-1721.1/925032022-09-26T16:02:19Z Text Mining in Cancer Gene and Pathway Prioritization Luo, Yuan Riedlinger, Gregory Szolovits, Peter Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Luo, Yuan Szolovits, Peter Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes. National Library of Medicine (U.S.) (Grant U54LM008748) Scullen Family Group for Cancer Data Analysis 2014-12-24T16:54:13Z 2014-12-24T16:54:13Z 2014-10 2014-05 Article http://purl.org/eprint/type/JournalArticle 1176-9351 http://hdl.handle.net/1721.1/92503 Luo, Yuan, Gregory Riedlinger, and Peter Szolovits. “Text Mining in Cancer Gene and Pathway Prioritization.” Cancer Informatics (October 2014): 69. https://orcid.org/0000-0003-0195-7456 https://orcid.org/0000-0001-8411-6403 en_US http://dx.doi.org/10.4137/cin.s13874 Cancer Informatics Creative Commons Attribution http://creativecommons.org/licenses/by-nc/3.0/ application/pdf Libertas Academica, Ltd. Libertas Academica |
| spellingShingle | Luo, Yuan Riedlinger, Gregory Szolovits, Peter Text Mining in Cancer Gene and Pathway Prioritization |
| title | Text Mining in Cancer Gene and Pathway Prioritization |
| title_full | Text Mining in Cancer Gene and Pathway Prioritization |
| title_fullStr | Text Mining in Cancer Gene and Pathway Prioritization |
| title_full_unstemmed | Text Mining in Cancer Gene and Pathway Prioritization |
| title_short | Text Mining in Cancer Gene and Pathway Prioritization |
| title_sort | text mining in cancer gene and pathway prioritization |
| url | http://hdl.handle.net/1721.1/92503 https://orcid.org/0000-0003-0195-7456 https://orcid.org/0000-0001-8411-6403 |
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