A meta-learning approach for genomic survival analysis.
A meta-learning approach for genomic survival analysis.
复制标题
基因组生存分析的元学习方法。
DOI：
10.1038/s41467-020-20167-3
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发表时间：
2020-12-11
影响因子：
16.6
通讯作者：
Gevaert O
中科院分区：
文献类型：
--
作者：
Qiu YL;Zheng H;Devos A;Selby H;Gevaert O
随着测序数据变得更容易获取且成本更低，RNA测序已成为癌症预后中一种很有前景的方法。然而，构建良好的预测模型仍然具有挑战性，特别是当样本量有限且特征数量较多时，这在生物医学环境中是常见的情况。为了解决这些局限性，我们提出了一种基于神经网络的元学习框架用于生存分析，并在基因组癌症研究环境中对其进行评估。我们证明，与常规的迁移学习相比，元学习是一种更有效的范式，可以利用与感兴趣的问题相关但不直接相关的高维数据。具体而言，元学习从相关任务的大量数据中明确构建一个模型，以便有效地学习样本量少的新任务。对于预测癌症生存结果的应用，我们还表明，使用少量样本的元学习框架能够与使用大量样本从头学习取得有竞争力的性能。最后，我们证明元学习模型根据基因对生存预测的贡献隐性地对基因进行优先级排序，并使我们能够识别癌症中的重要通路。
来自肿瘤的RNA测序数据可用于预测患者的预后。在这里，作者表明神经网络元学习方法可用于从少量样本中预测预后。
RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and the number of features is high, which is a common situation in biomedical settings. To address these limitations, we propose a meta-learning framework based on neural networks for survival analysis and evaluate it in a genomic cancer research setting. We demonstrate that, compared to regular transfer-learning, meta-learning is a significantly more effective paradigm to leverage high-dimensional data that is relevant but not directly related to the problem of interest. Specifically, meta-learning explicitly constructs a model, from abundant data of relevant tasks, to learn a new task with few samples effectively. For the application of predicting cancer survival outcome, we also show that the meta-learning framework with a few samples is able to achieve competitive performance with learning from scratch with a significantly larger number of samples. Finally, we demonstrate that the meta-learning model implicitly prioritizes genes based on their contribution to survival prediction and allows us to identify important pathways in cancer. RNA-sequencing data from tumours can be used to predict the prognosis of patients. Here, the authors show that a neural network meta-learning approach can be useful for predicting prognosis from a small number of samples.
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影响因子：
9.8
作者：
Bakr S;Gevaert O;Echegaray S;Ayers K;Zhou M;Shafiq M;Zheng H;Benson JA;Zhang W;Leung ANC;Kadoch M;Hoang CD;Shrager J;Quon A;Rubin DL;Plevritis SK;Napel S
通讯作者：
Napel S
影响因子：
14.9
作者：
Barrett T;Troup DB;Wilhite SE;Ledoux P;Rudnev D;Evangelista C;Kim IF;Soboleva A;Tomashevsky M;Marshall KA;Phillippy KH;Sherman PM;Muertter RN;Edgar R
通讯作者：
Edgar R
影响因子：
11.1
作者：
Brennan K;Koenig JL;Gentles AJ;Sunwoo JB;Gevaert O
通讯作者：
Gevaert O
影响因子：
64.5
作者：
Ceccarelli M;Barthel FP;Malta TM;Sabedot TS;Salama SR;Murray BA;Morozova O;Newton Y;Radenbaugh A;Pagnotta SM;Anjum S;Wang J;Manyam G;Zoppoli P;Ling S;Rao AA;Grifford M;Cherniack AD;Zhang H;Poisson L;Carlotti CG Jr;Tirapelli DP;Rao A;Mikkelsen T;Lau CC;Yung WK;Rabadan R;Huse J;Brat DJ;Lehman NL;Barnholtz-Sloan JS;Zheng S;Hess K;Rao G;Meyerson M;Beroukhim R;Cooper L;Akbani R;Wrensch M;Haussler D;Aldape KD;Laird PW;Gutmann DH;TCGA Research Network;Noushmehr H;Iavarone A;Verhaak RG
通讯作者：
Verhaak RG
影响因子：
4.3
作者：
Ching T;Zhu X;Garmire LX
通讯作者：
Garmire LX