@article{
 title = {An adaptation of particle swarm clustering applied in basal cell carcinoma, squamous cell carcinoma of the skin and actinic keratosis},
 type = {article},
 year = {2017},
 keywords = {Bioinformatics,Metastasis,Particle swarm clustering,Potential malignant lesion,Skin cancers},
 pages = {72-77},
 volume = {12},
 id = {3bb2c5ea-6b3e-31cf-bee2-a3daf863eb01},
 created = {2018-01-16T17:29:07.913Z},
 file_attached = {false},
 profile_id = {a1b2ded6-b257-3e56-ac7c-9d438762d170},
 last_modified = {2021-10-19T17:32:18.599Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Introduction This study used the comparison of basal cell carcinoma (BCC), squamous cell carcinoma of the skin (SCC) and actinic keratosis (AK) to test a new method for data set clustering in the leader gene approach. Methods Genes related to BCC, SCC and AK, were identified in the databases: OMIM, Genecards and NCBI Gene. A network was built for BCC, SCC and AK using STRING. For each gene, a weighted number of links (WNL) was calculated based on the combined STRING scores. The genes were then clustered according to their WNL and TIS, using an adaptation of particle swarm clustering (PSC) or K-means clustering. Results A disagreement between K-means clustering and PSC was observed for both BCC and SCC. PSC suggested completed different leader genes to BCC and SCC. While K-means clustering indicated that CTNNB1 and TP53 were associated with BCC and SCC. In contrast, no differences in methods were observed to AK, which had the shorter network. TP53 was the only leader gene for AK. Conclusion In conclusion, the current study suggests that PSC is an interesting tool for clustering genes in bioinformatics analyses of prevalent diseases. K-means clustering should be used in the small network. The current study also suggests TP53 may play a central role for AK. Additionally, CTNNB1 seems to be related to BCC, while CTNNA1 is related to SCC},
 bibtype = {article},
 author = {Poswar, Fabiano de Oliveira and Santos, Laércio Ives and Farias, Lucyana Conceição and Guimarães, Talita Antunes and Santos, Sérgio Henrique Souza and Jones, Kimberly Marie and de Paula, Alfredo Maurício Batista and Palhares, Reinaldo Martinez and D'Angelo, Marcos Flávio Silveira Vasconcelos and Guimarães, André Luiz Sena},
 doi = {10.1016/j.mgene.2017.01.007},
 journal = {Meta Gene}
}

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Methods Genes related to BCC, SCC and AK, were identified in the databases: OMIM, Genecards and NCBI Gene. A network was built for BCC, SCC and AK using STRING. For each gene, a weighted number of links (WNL) was calculated based on the combined STRING scores. The genes were then clustered according to their WNL and TIS, using an adaptation of particle swarm clustering (PSC) or K-means clustering. Results A disagreement between K-means clustering and PSC was observed for both BCC and SCC. PSC suggested completed different leader genes to BCC and SCC. While K-means clustering indicated that CTNNB1 and TP53 were associated with BCC and SCC. In contrast, no differences in methods were observed to AK, which had the shorter network. TP53 was the only leader gene for AK. Conclusion In conclusion, the current study suggests that PSC is an interesting tool for clustering genes in bioinformatics analyses of prevalent diseases. K-means clustering should be used in the small network. The current study also suggests TP53 may play a central role for AK. Additionally, CTNNB1 seems to be related to BCC, while CTNNA1 is related to SCC","bibtype":"article","author":"Poswar, Fabiano de Oliveira and Santos, Laércio Ives and Farias, Lucyana Conceição and Guimarães, Talita Antunes and Santos, Sérgio Henrique Souza and Jones, Kimberly Marie and de Paula, Alfredo Maurício Batista and Palhares, Reinaldo Martinez and D'Angelo, Marcos Flávio Silveira Vasconcelos and Guimarães, André Luiz Sena","doi":"10.1016/j.mgene.2017.01.007","journal":"Meta Gene","bibtex":"@article{\n title = {An adaptation of particle swarm clustering applied in basal cell carcinoma, squamous cell carcinoma of the skin and actinic keratosis},\n type = {article},\n year = {2017},\n keywords = {Bioinformatics,Metastasis,Particle swarm clustering,Potential malignant lesion,Skin cancers},\n pages = {72-77},\n volume = {12},\n id = {3bb2c5ea-6b3e-31cf-bee2-a3daf863eb01},\n created = {2018-01-16T17:29:07.913Z},\n file_attached = {false},\n profile_id = {a1b2ded6-b257-3e56-ac7c-9d438762d170},\n last_modified = {2021-10-19T17:32:18.599Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Introduction This study used the comparison of basal cell carcinoma (BCC), squamous cell carcinoma of the skin (SCC) and actinic keratosis (AK) to test a new method for data set clustering in the leader gene approach. Methods Genes related to BCC, SCC and AK, were identified in the databases: OMIM, Genecards and NCBI Gene. A network was built for BCC, SCC and AK using STRING. For each gene, a weighted number of links (WNL) was calculated based on the combined STRING scores. The genes were then clustered according to their WNL and TIS, using an adaptation of particle swarm clustering (PSC) or K-means clustering. Results A disagreement between K-means clustering and PSC was observed for both BCC and SCC. PSC suggested completed different leader genes to BCC and SCC. While K-means clustering indicated that CTNNB1 and TP53 were associated with BCC and SCC. In contrast, no differences in methods were observed to AK, which had the shorter network. TP53 was the only leader gene for AK. Conclusion In conclusion, the current study suggests that PSC is an interesting tool for clustering genes in bioinformatics analyses of prevalent diseases. K-means clustering should be used in the small network. The current study also suggests TP53 may play a central role for AK. Additionally, CTNNB1 seems to be related to BCC, while CTNNA1 is related to SCC},\n bibtype = {article},\n author = {Poswar, Fabiano de Oliveira and Santos, Laércio Ives and Farias, Lucyana Conceição and Guimarães, Talita Antunes and Santos, Sérgio Henrique Souza and Jones, Kimberly Marie and de Paula, Alfredo Maurício Batista and Palhares, Reinaldo Martinez and D'Angelo, Marcos Flávio Silveira Vasconcelos and Guimarães, André Luiz Sena},\n doi = {10.1016/j.mgene.2017.01.007},\n journal = {Meta Gene}\n}","author_short":["Poswar, F., d., O.","Santos, L., I.","Farias, L., C.","Guimarães, T., A.","Santos, S., H., S.","Jones, K., M.","de Paula, A., M., B.","Palhares, R., M.","D'Angelo, M., F., S., V.","Guimarães, A., L., S."],"biburl":"https://bibbase.org/service/mendeley/a1b2ded6-b257-3e56-ac7c-9d438762d170","bibbaseid":"poswar-santos-farias-guimares-santos-jones-depaula-palhares-etal-anadaptationofparticleswarmclusteringappliedinbasalcellcarcinomasquamouscellcarcinomaoftheskinandactinickeratosis-2017","role":"author","urls":{},"keyword":["Bioinformatics","Metastasis","Particle swarm clustering","Potential malignant lesion","Skin cancers"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"article","creationDate":"2020-02-05T21:53:19.720Z","downloads":0,"keywords":["bioinformatics","metastasis","particle swarm clustering","potential malignant lesion","skin cancers"],"search_terms":["adaptation","particle","swarm","clustering","applied","basal","cell","carcinoma","squamous","cell","carcinoma","skin","actinic","keratosis","poswar","santos","farias","guimarães","santos","jones","de paula","palhares","d'angelo","guimarães"],"title":"An adaptation of particle swarm clustering applied in basal cell carcinoma, squamous cell carcinoma of the skin and actinic keratosis","year":2017,"biburl":"https://bibbase.org/service/mendeley/a1b2ded6-b257-3e56-ac7c-9d438762d170","dataSources":["KeLrgK49soyCLiQgv","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}