Fariba Khoshghalbvash
ABSTRACT
Extracting cancer-related information from genomic data specially multi-source datasets has been an ever-growing challenge during the past years. The identification of subtype-specific genomic markers can lead to a sounder diagnosis and treatment. While several algorithms are proposed for feature extraction, to best of our knowledge, none of them consider between modality relations to discover modular disease associated biomarkers. In this paper, we represent an integrative deep learning approach to identify modular subtype-associated critical genes from three sets of input modalities for a better diagnosis of cancer subtypes. First, we train deep classifiers with different integration stages and distinct number of input modalities to predict cancer subtypes. Next, we use the optimized weight matrices of the classifier with the best performance to extract interactive top-ranked features among all input modalities. Lastly, we evaluate those ranks with other feature scoring methods according to their classification performance after feature extraction. Our results and analysis illustrate that the modular candidate biomarkers can be useful for cancer subtype detection.
- George A Calin and CarloMCroce. 2006. MicroRNA signatures in human cancers. Nature reviews cancer 6, 11 (2006), 857.Google Scholar
- S. Ceri, A. Kaitoua, M. Masseroli, P. Pinoli, and F. Venco. 2016. Data Management for Heterogeneous Genomic Datasets. IEEE/ACM Transactions on Computational Biology and Bioinformatics PP, 99 (2016), 1--1. Google ScholarDigital Library
- Kumardeep Chaudhary, Olivier B Poirion, Liangqun Lu, and Lana X Garmire. 2018. Deep learning--based multi-omics integration robustly predicts survival in liver cancer. Clinical Cancer Research 24, 6 (2018), 1248--1259.Google ScholarCross Ref
- Sean R Eddy. 2001. Non--coding RNA genes and the modern RNA world. Nature Reviews Genetics 2, 12 (2001), 919.Google ScholarCross Ref
- Ewan A Gibb, Carolyn J Brown, and Wan L Lam. 2011. The functional role of long non-coding RNA in human carcinomas. Molecular cancer 10, 1 (2011), 38.Google Scholar
- Nicolas Goossens, Shigeki Nakagawa, Xiaochen Sun, and Yujin Hoshida. 2015. Cancer biomarker discovery and validation. Translational cancer research 4, 3 (2015), 256.Google Scholar
- Isabelle Guyon, Jason Weston, Stephen Barnhill, and Vladimir Vapnik. 2002. Gene selection for cancer classification using support vector machines. Machine learning 46, 1--3 (2002), 389--422. Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In The IEEE International Conference on Computer Vision (ICCV). Google ScholarDigital Library
- Miles F Jefferson, Neil Pendleton, Sam B Lucas, and Michael A Horan. 1997. Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma. Cancer: Interdisciplinary International Journal of the American Cancer Society 79, 7 (1997), 1338--1342.Google Scholar
- Jun Li, Leng Han, Paul Roebuck, Lixia Diao, Lingxiang Liu, Yuan Yuan, John N Weinstein, and Han Liang. 2015. TANRIC: an interactive open platform to explore the function of lncRNAs in cancer. Cancer research (2015), canres--0273.Google Scholar
- Muxuan Liang, Zhizhong Li, Ting Chen, and Jianyang Zeng. 2015. Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 12, 4 (2015), 928--937. Google ScholarDigital Library
- Guanming Lu, Yueyong Li, Yanfei Ma, Jinlan Lu, Yongcheng Chen, Qiulan Jiang, Qiang Qin, Lifeng Zhao, Qianfang Huang, Zhizhai Luo, et al. 2018. Long noncoding RNA LINC00511 contributes to breast cancer tumourigenesis and stemness by inducing the miR-185--3p/E2F1/Nanog axis. Journal of Experimental & Clinical Cancer Research 37, 1 (2018), 289.Google ScholarCross Ref
- John S Mattick and Igor V Makunin. 2006. Non-coding RNA. Human molecular genetics 15, suppl_1 (2006), R17--R29.Google Scholar
- Cuong Nguyen, YongWang, and Ha Nam Nguyen. 2013. Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic. Journal of Biomedical Science and Engineering 6, 05 (2013), 551.Google ScholarCross Ref
- Brian C Ross. 2014. Mutual information between discrete and continuous data sets. PloS one 9, 2 (2014), e87357.Google ScholarCross Ref
- Ahmad Salameh, Xuejun Fan, Byung-Kwon Choi, Shu Zhang, Ningyan Zhang, and Zhiqiang An. 2017. HER3 and LINC00052 interplay promotes tumor growth in breast cancer. Oncotarget 8, 4 (2017), 6526.Google ScholarCross Ref
- Stephan C Schuster. 2008. Next-generation sequencing transforms today's biology. Nature methods 5, 1 (2008), 16.Google Scholar
- Jay Shendure and Hanlee Ji. 2008. Next-generation DNA sequencing. Nature biotechnology 26, 10 (2008), 1135--1145.Google Scholar
- Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15, 1 (2014), 1929--1958. Google ScholarDigital Library
- Nitish Srivastava and Ruslan R Salakhutdinov. 2012. Multimodal learning with deep boltzmann machines. In Advances in neural information processing systems. 2222--2230. Google ScholarDigital Library
- Dongdong Sun, Minghui Wang, and Ao Li. 2018. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multidimensional data. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018). Google ScholarDigital Library
- Erwin L van Dijk, Hélène Auger, Yan Jaszczyszyn, and Claude Thermes. 2014. Ten years of next-generation sequencing technology. Trends in genetics 30, 9 (2014), 418--426.Google Scholar
- Lin Wei, Zhilin Jin, Shengjie Yang, Yanxun Xu, Yitan Zhu, and Yuan Ji. 2017. TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data. Bioinformatics 34, 9 (2017), 1615--1617.Google ScholarCross Ref
- Xiaoyi Xu, Ya Zhang, Liang Zou, Minghui Wang, and Ao Li. 2012. A gene signature for breast cancer prognosis using support vector machine. In 2012 5th International Conference on BioMedical Engineering and Informatics. IEEE, 928--931.Google ScholarCross Ref
- Matthew D Zeiler. 2012. ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012).Google Scholar
- Yitan Zhu, Peng Qiu, and Yuan Ji. 2014. TCGA-assembler: open-source software for retrieving and processing TCGA data. Nature methods 11, 6 (2014), 599.Google Scholar
Index Terms
- Integrative Feature Ranking by Applying Deep Learning on Multi Source Genomic Data
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