Net2Image: A network representation method for identifying cancer-related genes

Bolin Chen, Yuqiong Jin, Xuequn Shang

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Although many machine learning algorithms have been proposed to identify cancer-related genes, their prediction accuracy is still limited due to the complex relationship between cancers and genes. To improve the prediction accuracy, many deep learning based tools have been developed, and they have shown their efficiency to handle complex relationships. To use those tools, a deliberate data representation method is indispensable, since majority tools only take those image-like data as inputs. In this study, we propose a novel network representation method, called Net2Image, to transfer topological networks into image-like datasets. The local topological information of individual vertices from six biomolecular networks and one DNA methylation dataset are encoded as 80 ∗ 6 matrices. They are then employed as inputs to train the model for identifying cancer-related genes using TensorFlow. The numerical experiments show that the proposed method can achieve very high prediction accuracy, which outperforms many existing methods.

源语言英语
主期刊名Bioinformatics Research and Applications - 13th International Symposium, ISBRA 2017, Proceedings
编辑Zhipeng Cai, Ovidiu Daescu, Min Li
出版商Springer Verlag
337-343
页数7
ISBN(印刷版)9783319595740
DOI
出版状态已出版 - 2017
活动13th International Symposium on Bioinformatics Research and Applications, ISBRA 2017 - Honolulu, 美国
期限: 29 5月 20172 6月 2017

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10330 LNBI
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议13th International Symposium on Bioinformatics Research and Applications, ISBRA 2017
国家/地区美国
Honolulu
时期29/05/172/06/17

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