LEARNING DAGS USING MULTICLASS SUPPORT VECTOR MACHINES

被引：0

作者：

Nikolay, Fabio ^{[1
]}

Pesavento, Marius ^{[1
]}

机构：

[1] Tech Univ Darmstadt, Commun Syst Grp, Darmstadt, Germany

来源：

2018 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2018年

关键词：

Large scale gene networks; machine learning; big data; graph learning; INFERENCE;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper we consider the problem of learning the genetic-interaction-network that is underlying the measured double knockout (DK) data. Based on the biological system model of [3], we propose a multiclass-SVM approach that yields a high prediction accuracy of the genetic-interaction-network underlying the DK data while being able to estimate the network topology for large sets of genes. We demonstrate the performance of our proposed multiclass-SVM approach by synthetic data simulations where we use the recently proposed GENIE method of [3] as a benchmark.

引用

页码：75 / 79

页数：5

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