Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.
Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.
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DOI：
10.1371/journal.pcbi.1008878
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发表时间：
2021-04
影响因子：
4.3
通讯作者：
Stuart JM
中科院分区：
文献类型：
--
作者：
Uzunangelov V;Wong CK;Stuart JM
测序的进步导致了人类细胞在不同条件和扰动下的多组谱的增殖。此外，许多数据库已经积累了有关途径和基因“特征”的信息-与特定细胞和表型背景相关的基因表达模式。系统生物学当前面临的一个重要挑战是利用这些关于基因协调的知识来最大化应用于高通量数据集的模型的预测能力和泛化。然而，很少有这样的综合方法存在，也提供可解释的结果，量化单个基因和途径对模型准确性的重要性。我们介绍了AKLIMATE，这是第一个基于核的堆叠学习器，它以回归或分类任务的路径形式无缝地将多组学特征数据与先验信息结合在一起。AKLIMATE使用一种新颖的多核学习框架，其中单个核捕获随机森林中记录的预测倾向，每个核都建立在一个特定的途径基因集上，该基因集集成了其成员基因的所有组学数据。AKLIMATE在各种表型学习任务中具有与最先进的方法相当或改进的性能，包括预测子宫内膜和结直肠癌的微卫星不稳定性，乳腺癌的存活，以及细胞系对基因敲低的反应。我们展示了AKLIMATE如何能够通过它们的共同途径连接数据平台上的特征数据，以确定几种已知和新的癌症和合成致命性贡献者的例子。我们描述了一种结合多模态分子测量和基因通路信息的分类和回归预测任务的新方法。该方法结合了强大的机器学习方法，如集成、核和堆叠学习。一个关键的新贡献是通过样本预测的成对相似性和特定基因模块的随机森林树的样本路径创建经验核。我们证明该方法在三种非常不同的癌症基因组学应用中表现得与顶级方法一样好，甚至更好。
Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In addition, many databases have amassed information about pathways and gene “signatures”—patterns of gene expression associated with specific cellular and phenotypic contexts. An important current challenge in systems biology is to leverage such knowledge about gene coordination to maximize the predictive power and generalization of models applied to high-throughput datasets. However, few such integrative approaches exist that also provide interpretable results quantifying the importance of individual genes and pathways to model accuracy. We introduce AKLIMATE, a first kernel-based stacked learner that seamlessly incorporates multi-omics feature data with prior information in the form of pathways for either regression or classification tasks. AKLIMATE uses a novel multiple-kernel learning framework where individual kernels capture the prediction propensities recorded in random forests, each built from a specific pathway gene set that integrates all omics data for its member genes. AKLIMATE has comparable or improved performance relative to state-of-the-art methods on diverse phenotype learning tasks, including predicting microsatellite instability in endometrial and colorectal cancer, survival in breast cancer, and cell line response to gene knockdowns. We show how AKLIMATE is able to connect feature data across data platforms through their common pathways to identify examples of several known and novel contributors of cancer and synthetic lethality. We describe a new method that incorporates multimodal molecular measurements with gene pathway information for classification and regression prediction tasks. The method combines powerful machine learning methodologies such as ensemble, kernel and stacked learning. A key new contribution is the creation of empirical kernels from pairwise similarities of sample predictions and sample paths along the trees of a random forest specific to a particular gene module. We demonstrate the method performs as well as, or better than, top approaches on three very different cancer genomics applications.
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推断因果分子网络：通过基于社区的努力进行的经验评估。
影响因子：
48
作者：
Hill SM;Heiser LM;Cokelaer T;Unger M;Nesser NK;Carlin DE;Zhang Y;Sokolov A;Paull EO;Wong CK;Graim K;Bivol A;Wang H;Zhu F;Afsari B;Danilova LV;Favorov AV;Lee WS;Taylor D;Hu CW;Long BL;Noren DP;Bisberg AJ;HPN-DREAM Consortium;Mills GB;Gray JW;Kellen M;Norman T;Friend S;Qutub AA;Fertig EJ;Guan Y;Song M;Stuart JM;Spellman PT;Koeppl H;Stolovitzky G;Saez-Rodriguez J;Mukherjee S
通讯作者：
Mukherjee S
DOI：
10.1090/s0002-9947-1950-0051437-7
发表时间：
1950-01-01
影响因子：
1.3
作者：
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影响因子：
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Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
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影响因子：
14.9
作者：
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通讯作者：
Quackenbush J