Name
Title | ||
|---|---|---|
Convolutional Neural Network Approach to Lung Cancer Classification Integrating Protein Interaction Network and Gene Expression Profiles. |
Abstract | ||
|---|---|---|
Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to "omics" data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a CNN approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression pro files to classify lung cancer. The performed computational experiments suggest that in terms of accuracy the predictive performance of our proposed method was better than those of other machine learning methods such as SVM or Random Forest. Moreover, the computational results also indicate that the underlying protein network structure assists to enhance the predictions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1142/S0219720019400079 | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
Deep learning,spectral clustering,convolutional neural network,gene expression,protein network,SVM | Spectral clustering,Information processing,Convolutional neural network,Computer science,Systems biology,Interaction network,Artificial intelligence,Complex network,Deep learning,Artificial neural network,Machine learning | Conference |
Volume | Issue | ISSN |
17 | SP3 | 0219-7200 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teppei Matsubara | 1 | 1 | 0.35 |
| Tomoshiro Ochiai | 2 | 2 | 0.70 |
| Morihiro Hayashida | 3 | 154 | 21.88 |
| Tatsuya Akutsu | 4 | 2169 | 216.05 |
| Jose C Nacher | 5 | 33 | 6.67 |