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- Tran, K. B., et al.
(författare)
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The global burden of cancer attributable to risk factors, 2010-19: a systematic analysis for the Global Burden of Disease Study 2019
- 2022
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Ingår i: Lancet. - 0140-6736. ; 400:10352, s. 563-591
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Tidskriftsartikel (refereegranskat)abstract
- Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
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| 3. |
- MotieGhader, Habib, et al.
(författare)
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mRNA and microRNA selection for breast cancer molecular subtype stratification using meta-heuristic based algorithms
- 2020
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Ingår i: Genomics. - : Elsevier BV. - 0888-7543 .- 1089-8646. ; 112:5, s. 3207-3217
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Tidskriftsartikel (refereegranskat)abstract
- Cancer subtype stratification, which may help to make a better decision in treating cancerous patients, is one of the most crucial and challenging problems in cancer studies. To this end, various computational methods such as Feature selection, which enhances the accuracy of the classification and is an NP-Hard problem, have been proposed. However, the performance of the applied methods is still low and can be increased by the state-of-the-art and efficient methods. We used 11 efficient and popular meta-heuristic algorithms including WCC, LCA, GA, PSO, ACO, ICA, LA, HTS, FOA, DSOS and CUK along with SVM classifier to stratify human breast cancer molecular subtypes using mRNA and micro-RNA expression data. The applied algorithms select 186 mRNAs and 116 miRNAs out of 9692 mRNAs and 489 miRNAs, respectively. Although some of the selected mRNAs and miRNAs are common in different algorithms results, six miRNAs including miR-190b, miR-18a, miR-301a, miR-34c-5p, miR-18b, and miR-129-5p were selected by equal or more than three different algorithms. Further, six mRNAs, including HAUS6, LAMA2, TSPAN33, PLEKHM3, GFRA3, and DCBLD2, were chosen through two different algorithms. We have reported these miRNAs and mRNAs as important diagnostic biomarkers to the stratification of breast cancer subtypes. By investigating the literature, it is also observed that most of our reported mRNAs and miRNAs have been proposed and introduced as biomarkers in cancer subtypes stratification.
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- Kashani, Zahra RM, et al.
(författare)
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Kavosh: a new algorithm for finding network motifs
- 2009
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Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 10:318
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundComplex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.ResultsWe present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae, and also on non-biological networks: a social and an electronic network.ConclusionThe efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/.
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