TCGA数据机器学习研究数据包
TCGA数据机器学习研究数据包
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选择 Cancers、Gene sets
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做肿瘤研究的过程中,生存分是一个很常见的研究分析,研究者可以根据生存分析的结果判断某个因素,比如基因表达,对患者预后生存的影响。

TCGA这个肿瘤研究的宝库包含了多个肿瘤Cancer Types Index的生存数据,一直以来都是数据挖掘的宝库。

我们本次收集了大家广泛研究的基因集(GeneSet,分析这些基因在TCGA不同肿瘤类型中与病人生存期的关系。每个基因集分别进行基因表达与生存数据的Cox回归分析KM生存分析、风险指数建模以及与病人多个临床因素的关等分析。

本分析花费6天6夜给大家带来丰富内容:

结果按照基因集和肿瘤类型组合分别存储为独立数据包,可供选择。另外也接受基因集私人定制。

两百多个与肿瘤相关的GeneSet分别在TCGA的33种肿瘤中进行分析,GeneSet使用的为GSEA的。


H: hallmark gene sets
(browse 50 gene sets)
We envision this collection as the starting point for your exploration of the MSigDB resource and GSEA. Hallmark gene sets summarize and represent specific well-defined biological states or processes and display coherent expression. These gene sets were generated by a computational methodology based on identifying gene set overlaps and retaining genes that display coordinate expression. The hallmarks reduce noise and redundancy and provide a better delineated biological space for GSEA. We refer to the original overlapping gene sets, from which a hallmark is derived, as its 'founder' sets. Hallmark gene set pages provide links to the corresponding founder sets for deeper follow up.

This collection is an initial release of 50 hallmarks which condense information from over 4,000 original overlapping gene sets from v4.0 MSigDB collections C1 through C6. We refer to the original gene sets as "founder" sets.

Hallmark gene set pages provide links to the corresponding founder sets for more in-depth exploration. In addition, hallmark gene set pages include links to microarray data that served for refining and validation of the hallmark signatures.

To cite your use of the collection, and for further information, please refer to

The Molecular Signatures Database (MSigDB) hallmark gene setcollection.

2015 Dec 23;1(6):417-425.


C6: oncogenic signatures
(browse 189 gene sets)

Gene sets represent signatures of cellular pathways which are often dis-regulated in cancer. The majority of signatures were generated directly from microarray data from NCBI GEO or from internal unpublished profiling experiments which involved perturbation of known cancer genes. In addition, a small number of oncogenic signatures were curated from scientific publications.

To cite your use of the collection, and for further information, please refer to

Emerging landscape of oncogenic signatures across humancancers.

2013Oct;45(10):1127-33. doi: 10.1038/ng.2762.


Oncogenic pathway signatures in human cancers asa guide to targeted therapies.

2006 Jan 19;439(7074):353-7.Epub 2005 Nov 6.









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ACC肾上腺皮质癌)为例,展示本次分析结果


参考文献:


1. Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

2018 Apr 3;23(1):172-180.e3. doi: 10.1016/j.celrep.2018.03.046.


2. A Multivariable Prediction Model for Pneumocystis jiroveciiPneumonia in Hematology Patients with Acute Respiratory Failure

Am J Respir Crit Care Med. 2018 Dec15;198(12):1519-1526. doi: 10.1164/rccm.201712-2452OC.


3. Plasma biomarkers of risk for death in a multicenter phase3 trial with uniform transplant characteristics post–allogeneic HCT

Blood. 2017 Jan 12;129(2):162-170. doi:10.1182/blood-2016-08-735324. Epub 2016 Nov 8.


4. Deep learning for lung cancer prognostication: Aretrospective multi-cohort radiomics study

PLoS Med. 2018 Nov 30;15(11):e1002711. doi:10.1371/journal.pmed.1002711. eCollection 2018 Nov.


5. Weekly dose-dense chemotherapy infirst-line epithelial ovarian, fallopian tube, or primary peritoneal carcinomatreatment (ICON8): primary progression free survival analysis results from aGCIG phase 3 randomised controlled trial

Lancet. 2019 Dec 7;394(10214):2084-2095. doi:10.1016/S0140-6736(19)32259-7. Epub 2019 Nov 29.


6. First-line ceritinib versusplatinum-based chemotherapy in advanced ALK-rearranged non-small-cell lungcancer (ASCEND-4): a randomised, open-label, phase 3 study

Lancet. 2017 Mar 4;389(10072):917-929. doi:10.1016/S0140-6736(17)30123-X. Epub 2017 Jan 24.


7. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

2018 Apr 5;173(2):338-354.e15. doi: 10.1016/j.cell.2018.03.034.


8. Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes

2019 Oct;7(10):1591-1604. doi: 10.1158/2326-6066.CIR-19-0155. Epub 2019 Sep 12.


If you have questions or problems using the data   please   send them to yunbios . Also lets us know if you find   it's   useful   in your work.

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