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Q-omics: Smart Software for Assisting Oncology and Cancer Research
The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computationa...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society for Molecular and Cellular Biology
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627836/ https://www.ncbi.nlm.nih.gov/pubmed/34819397 http://dx.doi.org/10.14348/molcells.2021.0169 |
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author | Lee, Jieun Kim, Youngju Jin, Seonghee Yoo, Heeseung Jeong, Sumin Jeong, Euna Yoon, Sukjoon |
author_facet | Lee, Jieun Kim, Youngju Jin, Seonghee Yoo, Heeseung Jeong, Sumin Jeong, Euna Yoon, Sukjoon |
author_sort | Lee, Jieun |
collection | PubMed |
description | The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr. |
format | Online Article Text |
id | pubmed-8627836 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Korean Society for Molecular and Cellular Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-86278362021-12-08 Q-omics: Smart Software for Assisting Oncology and Cancer Research Lee, Jieun Kim, Youngju Jin, Seonghee Yoo, Heeseung Jeong, Sumin Jeong, Euna Yoon, Sukjoon Mol Cells Research Article The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr. Korean Society for Molecular and Cellular Biology 2021-11-30 2021-11-17 /pmc/articles/PMC8627836/ /pubmed/34819397 http://dx.doi.org/10.14348/molcells.2021.0169 Text en © The Korean Society for Molecular and Cellular Biology. All rights reserved. https://creativecommons.org/licenses/by-nc-sa/3.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ (https://creativecommons.org/licenses/by-nc-sa/3.0/) |
spellingShingle | Research Article Lee, Jieun Kim, Youngju Jin, Seonghee Yoo, Heeseung Jeong, Sumin Jeong, Euna Yoon, Sukjoon Q-omics: Smart Software for Assisting Oncology and Cancer Research |
title | Q-omics: Smart Software for Assisting Oncology and Cancer Research |
title_full | Q-omics: Smart Software for Assisting Oncology and Cancer Research |
title_fullStr | Q-omics: Smart Software for Assisting Oncology and Cancer Research |
title_full_unstemmed | Q-omics: Smart Software for Assisting Oncology and Cancer Research |
title_short | Q-omics: Smart Software for Assisting Oncology and Cancer Research |
title_sort | q-omics: smart software for assisting oncology and cancer research |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627836/ https://www.ncbi.nlm.nih.gov/pubmed/34819397 http://dx.doi.org/10.14348/molcells.2021.0169 |
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