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Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data

Jie Hao, Youngsoon Kim, Tejaswini Mallavarapu, Jung Hun Oh, Mingon Kang
2019 BMC Medical Genomics  
Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction  ...  However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis.  ...  In this paper, we develop a novel pathway-based sparse deep neural network, named Cox-PASNet, for survival analysis by integrating high-dimensional genomic data and clinical data.  ... 
doi:10.1186/s12920-019-0624-2 pmid:31865908 pmcid:PMC6927105 fatcat:5xvlc4xvmngofavfu2dvikjfoq

A novel deep autoencoder based survival analysis approach for microarray dataset

Hanaa Torkey, Mostafa Atlam, Nawal El-Fishawy, Hanaa Salem
2021 PeerJ Computer Science  
Finally, the biological pathways and GO molecular functions are analyzed for these significant genes.  ...  The results show that the proposed AutoCox and AutoRandom algorithms based on our feature selection autoencoder approach have better concordance index results comparing the most recent deep learning approaches  ...  Co-PASNet is a sparse pathway-based deep neural network, which consists of five layers; genes input layer, pathway layer, some hidden layers, clinical date layer, and finally Cox layer.  ... 
doi:10.7717/peerj-cs.492 pmid:33981841 pmcid:PMC8080419 fatcat:v74plgjy45erjavnoelggwl4pa

Identification and validation of stemness-related lncRNA prognostic signature for breast cancer

Xiaoying Li, Yang Li, Xinmiao Yu, Feng Jin
2020 Journal of Translational Medicine  
The risk model was further verified as a novel independent prognostic factor for breast cancer patients based on the calculated risk score.  ...  A co-expression network of BCSC-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA) was constructed.  ...  Moreover, Cox-PASNet, which is a novel pathway-based sparse deep neural network for survival analysis that integrates high-dimensional genomic data and clinical data, has been applied to identify significant  ... 
doi:10.1186/s12967-020-02497-4 pmid:32867770 fatcat:cdzzjmdpk5dxjkjsbxd57b2ks4

Identification and validation of autophagy-related lncRNA prognostic signature for breast cancer

Xiaoying Li, Feng Jin, Yang Li
2020 pre-print withdrawn
The risk model was further verified as a novel independent prognostic factor for breast cancer patients based on the calculated risk score.  ...  Methods: A coexpression network of autophagy-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA) was constructed.  ...  Moreover, Cox-PASNet, which is a novel pathway-based sparse deep neural network for survival analysis that intergrats high-dimensional genomic data and clinical data, has been applied to identify signi  ... 
doi:10.21203/rs.3.rs-47873/v1 fatcat:qgvwm5vnsjfbhlnxprpjavgvwi

Identification and validation of autophagy-related lncRNA prognostic signature for breast cancer

Xiaoying Li, Feng Jin, Yang Li
2020 pre-print withdrawn
Moreover, Cox-PASNet, which is a novel pathway-based sparse deep neural network for survival analysis that intergrats high-dimensional genomic data and clinical data, has been applied to identify signi  ...  Cox-nnet is an arti cial neural network approach that has been utilized in predicting low-dimensional survival prognosis [36] .  ... 
doi:10.21203/rs.3.rs-47873/v2 fatcat:wtxhknbcr5g75daxg6jpelos6a