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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...

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Autores principales: Lee, Jieun, Kim, Youngju, Jin, Seonghee, Yoo, Heeseung, Jeong, Sumin, Jeong, Euna, Yoon, Sukjoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society for Molecular and Cellular Biology 2021
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.
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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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