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A Novel Computational Approach for Reducing False Positives in Text Data Mining
| Content Provider | Semantic Scholar |
|---|---|
| Author | Yasarturk, Noah |
| Copyright Year | 2016 |
| Abstract | Elucidating molecular genetic mechanisms regulating complex biological processes has benefit from the advent of large-scale bioinformatics efforts that provide tools for mining genetic interactions and functions in a tissue or cell type of interest. Among these bioinformatics tools is the use of text data mining algorithms, which are computational strategies for extracting biological data on gene interactions and gene ontologies for a queried gene from the published scientific literature. One major problem with these algorithms is a high false discovery rate (FDR) whereby genetic interactions or gene functions are sometimes incorrectly reported for a given gene because most of these algorithms look at whether there is a presence of the queried genes and the functions or the interaction, but do not check the connections between the same. We hypothesize that FDR from text mining for a given query gene can be reduced by implementing algorithms that more rigorously assess data extracted from text mining. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://scholarworks.gsu.edu/cgi/viewcontent.cgi?article=1495&context=gsurc |
| Alternate Webpage(s) | https://scholarworks.gsu.edu/cgi/viewcontent.cgi?article=1495&context=gsurc |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
