The sbv IMPROVER Systems Toxicology Computational Challenge: Identification of Human and Species-Independent Blood Response Markers as Predictors of Smoking Exposure and Cessation Status
| العنوان: | The sbv IMPROVER Systems Toxicology Computational Challenge: Identification of Human and Species-Independent Blood Response Markers as Predictors of Smoking Exposure and Cessation Status |
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| المؤلفون: | Belcastro, Vincenzo, Poussin, Carine, Xiang, Yang, Giordano, Maurizio, Tripathi, Kumar Parijat, Boda, Akash, Balci, Ali Tugrul, Bilgen, Ismail, Dhanda, Sandeep Kumar, Duan, Zhongqu, Gong, Xiaofeng, Kumar, Rahul, Romero, Roberto, Sarac, Omer Sinan, Tarca, Adi L., Wang, Peixuan, Yang, Hao, Yang, Wenxin, Zhang, Chenfang, Boué, Stéphanie, Guarracino, Mario Rosario, Martin, Florian, Peitsch, Manuel C., Hoeng, Julia |
| المصدر: | Toxicology (Amst.) (2018): 1–14. doi:10.1016/j.comtox.2017.07.004 info:cnr-pdr/source/autori:Belcastro, Vincenzo; Poussin, Carine; Xiang, Yang; Giordano, Maurizio; Tripathi, Kumar Parijat; Boda, Akash; Balci, Ali Tugrul; Bilgen, Ismail; Dhanda, Sandeep Kumar; Duan, Zhongqu; Duan, Zhongqu; Gong, Xiaofeng; Gong, Xiaofeng; Kumar, Rahul; Romero, Roberto; Romero, Roberto; Romero, Roberto; Romero, Roberto; Romero, Roberto; Sarac, Omer Sinan; Tarca, Adi L.; Tarca, Adi L.; Wang, Peixuan; Wang, Peixuan; Yang, Hao; Yang, Hao; Yang, Wenxin; Yang, Wenxin; Zhang, Chenfang; Zhang, Chenfang; Boué, Stéphanie; Guarracino, Mario Rosario; Martin, Florian; Peitsch, Manuel C.; Hoeng, Julia/titolo:The sbv IMPROVER Systems Toxicology computational challenge: Identification of human and species-independent blood response markers as predictors of smoking exposure and cessation status/doi:10.1016%2Fj.comtox.2017.07.004/rivista:Toxicology (Amst.)/anno:2018/pagina_da:1/pagina_a:14/intervallo_pagine:1–14/volume |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 0301 basic medicine, CDKN1C gene, Health, Toxicology and Mutagenesis, DSC2 gene, Toxicology, Bioinformatics, CLEC10A gene, transcriptomics, 0302 clinical medicine, SEMA6B gene, Medicine, gene expression assay, GSE1 gene, Exposure response, GPR63 gene, education.field_of_study, DNA methylation, smoking exposure, training, feasibility study, biological marker, Systems toxicology, Computational challenge, Computer Science Applications, RNA isolation, priority journal, 030220 oncology & carcinogenesis, blood sampling, RNA hybridization, genetic marker, Risk assessment, FSTL1 gene, human versus animal comparison, biological marker, Article, blood gene signature, computational fluid dynamics, computer model, consensus, gene expression, gene mapping, GUCY1A3 gene, human, immunity, nonhuman, scoring system, sequence homology, smoking, smoking cessation, support vector machine, unindexed sequence, Blood biomarkers, Gene signature, Smoking biomarker, Population, Feature selection, Article, 03 medical and health sciences, Cigarette smoking, education, business.industry, 030104 developmental biology, Tobacco exposure, business |
| الوصف: | Cigarette smoking entails chronic exposure to a mixture of harmful chemicals that trigger molecular changes over time, and is known to increase the risk of developing diseases. Risk assessment in the context of 21st century toxicology relies on the elucidation of mechanisms of toxicity and the identification of exposure response markers, usually from high-throughput data, using advanced computational methodologies. The sbv IMPROVER Systems Toxicology computational challenge (Fall 2015-Spring 2016) aimed to evaluate whether robust and sparse (≤40 genes) human (sub-challenge 1, SC1) and species-independent (sub-challenge 2, SC2) exposure response markers (so called gene signatures) could be extracted from human and mouse blood transcriptomics data of current (S), former (FS) and never (NS) smoke-exposed subjects as predictors of smoking and cessation status. Best-performing computational methods were identified by scoring anonymized participants’ predictions. Worldwide participation resulted in 12 (SC1) and six (SC2) final submissions qualified for scoring. The results showed that blood gene expression data were informative to predict smoking exposure (i.e. discriminating smoker versus never or former smokers) status in human and across species with a high level of accuracy. By contrast, the prediction of cessation status (i.e. distinguishing FS from NS) remained challenging, as reflected by lower classification performances. Participants successfully developed inductive predictive models and extracted human and species-independent gene signatures, including genes with high consensus across teams. Post-challenge analyses highlighted “feature selection” as a key step in the process of building a classifier and confirmed the importance of testing a gene signature in independent cohorts to ensure the generalized applicability of a predictive model at a population-based level. In conclusion, the Systems Toxicology challenge demonstrated the feasibility of extracting a consistent blood-based smoke exposure response gene signature and further stressed the importance of independent and unbiased data and method evaluations to provide confidence in systems toxicology-based scientific conclusions. |
| اللغة: | English |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d501cecaf711613c99feac1486c353aTest https://europepmc.org/articles/PMC6136260Test/ |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....8d501cecaf711613c99feac1486c353a |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |