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An integrated method for the identification of novel genes related to oral cancer

View Article: PubMed Central - PubMed

ABSTRACT

Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes. To validate the utility of the integrated method, data for oral cancer were used, on which the RWR-based and SP-based methods were trained, thereby building two optimal methods. The integrated method combining these optimal methods was further adopted to identify the novel genes of oral cancer. As a result, 85 novel genes were inferred, among which eleven genes (e.g., MYD88, FGFR2, NF-κBIA) were identified by both the RWR-based and SP-based methods, 70 genes (e.g., BMP4, IFNG, KITLG) were discovered only by the RWR-based method and four genes (L1R1, MCM6, NOG and CXCR3) were predicted only by the SP-based method. Extensive analyses indicate that several novel genes have strong associations with cancers, indicating the effectiveness of the integrated method for identifying disease genes.

No MeSH data available.


The performance of the SP-based method under different combinations of parameters.There are three lines in this figure, which represent the performance of the SP-based method with different thresholds of maximum interaction score. In detail, the full line represents the performance of the SP-based method with the threshold of maximum interaction score 900, the dot line represents the performance of the SP-based method with the threshold of maximum interaction score 700, the dash line represents the performance of the SP-based method with the threshold of maximum interaction score 400.
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pone.0175185.g002: The performance of the SP-based method under different combinations of parameters.There are three lines in this figure, which represent the performance of the SP-based method with different thresholds of maximum interaction score. In detail, the full line represents the performance of the SP-based method with the threshold of maximum interaction score 900, the dot line represents the performance of the SP-based method with the threshold of maximum interaction score 700, the dash line represents the performance of the SP-based method with the threshold of maximum interaction score 400.

Mentions: The SP-based method was also trained to extract the optimal parameters for pMIS and pMFS. We tested three values for pMIS, as mentioned in the above paragraph, and various values ranging from 0 to 0.9 for pMFS. For the results obtained using the SP-based method with different combinations of parameters, the measurements mentioned in the Section “Evaluation methods” were counted and are provided in S3 Table. Additionally, three curves are plotted in Fig 2 to show the values of F1-measure-R obtained using the SP-based method with different values of pMFS and a fixed value of pMIS. When the values of pMFS were small, the values of F1-measure-R were proportional to the value of pMIS. However, the values of F1-measure-R were almost the same when pMFS were large. The maximum F1-measure-R was 2.693E-04 when the parameters were set to be pMFS = 0.8 and pMIS = 400 or 700 or 900. Similarly, we used these values to build the optimal SP-based method for the identification of novel OC-related genes.


An integrated method for the identification of novel genes related to oral cancer
The performance of the SP-based method under different combinations of parameters.There are three lines in this figure, which represent the performance of the SP-based method with different thresholds of maximum interaction score. In detail, the full line represents the performance of the SP-based method with the threshold of maximum interaction score 900, the dot line represents the performance of the SP-based method with the threshold of maximum interaction score 700, the dash line represents the performance of the SP-based method with the threshold of maximum interaction score 400.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5383255&req=5

pone.0175185.g002: The performance of the SP-based method under different combinations of parameters.There are three lines in this figure, which represent the performance of the SP-based method with different thresholds of maximum interaction score. In detail, the full line represents the performance of the SP-based method with the threshold of maximum interaction score 900, the dot line represents the performance of the SP-based method with the threshold of maximum interaction score 700, the dash line represents the performance of the SP-based method with the threshold of maximum interaction score 400.
Mentions: The SP-based method was also trained to extract the optimal parameters for pMIS and pMFS. We tested three values for pMIS, as mentioned in the above paragraph, and various values ranging from 0 to 0.9 for pMFS. For the results obtained using the SP-based method with different combinations of parameters, the measurements mentioned in the Section “Evaluation methods” were counted and are provided in S3 Table. Additionally, three curves are plotted in Fig 2 to show the values of F1-measure-R obtained using the SP-based method with different values of pMFS and a fixed value of pMIS. When the values of pMFS were small, the values of F1-measure-R were proportional to the value of pMIS. However, the values of F1-measure-R were almost the same when pMFS were large. The maximum F1-measure-R was 2.693E-04 when the parameters were set to be pMFS = 0.8 and pMIS = 400 or 700 or 900. Similarly, we used these values to build the optimal SP-based method for the identification of novel OC-related genes.

View Article: PubMed Central - PubMed

ABSTRACT

Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes. To validate the utility of the integrated method, data for oral cancer were used, on which the RWR-based and SP-based methods were trained, thereby building two optimal methods. The integrated method combining these optimal methods was further adopted to identify the novel genes of oral cancer. As a result, 85 novel genes were inferred, among which eleven genes (e.g., MYD88, FGFR2, NF-κBIA) were identified by both the RWR-based and SP-based methods, 70 genes (e.g., BMP4, IFNG, KITLG) were discovered only by the RWR-based method and four genes (L1R1, MCM6, NOG and CXCR3) were predicted only by the SP-based method. Extensive analyses indicate that several novel genes have strong associations with cancers, indicating the effectiveness of the integrated method for identifying disease genes.

No MeSH data available.