@inproceedings{fbe88cddafdd494c90dd99bbca9e8814,
title = "Net2Image: A network representation method for identifying cancer-related genes",
abstract = "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.",
keywords = "Biomolecular network, Cancer-related gene, Deep learning, Multiple data integration",
author = "Bolin Chen and Yuqiong Jin and Xuequn Shang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 13th International Symposium on Bioinformatics Research and Applications, ISBRA 2017 ; Conference date: 29-05-2017 Through 02-06-2017",
year = "2017",
doi = "10.1007/978-3-319-59575-7_31",
language = "英语",
isbn = "9783319595740",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "337--343",
editor = "Zhipeng Cai and Ovidiu Daescu and Min Li",
booktitle = "Bioinformatics Research and Applications - 13th International Symposium, ISBRA 2017, Proceedings",
}