| 篇名/題名： | An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate |
| 並列篇名： | 基於資訊融合系統的深度神經網路及其在癌症死亡風險評估中的應用 |
| 摘要： | Abstract— Next-generation sequencing (NGS) genomic data
offer valuable high-throughput genomic information for computational applications in medicine. Using genomic data to
identify disease-associated genes to estimate cancer mortality
risk remains challenging regarding to computational efficiency
and risk integration. For determining mortality-related genes,
we propose an information fusion system based on a fuzzy system
to fuse the numerous deep-learning-based risk scores, consider
the significance of features related to time-varying effects and
risk stratifications, and interpret the directional relationship and
interaction between outcome and predictors. Fuzzy rules were
implemented to integrate the considerations mentioned above
by merging all the risk score models to achieve advanced risk
estimation. The genomic data of head and neck squamous cell
carcinoma (HNSCC) were used to evaluate the performance
of the proposed computational approach. The results indicated
that the proposed computational approach exhibited optimal
ability to identify mortality risk-related genes in HNSCC patients.
The results also suggest that HNSCC mortality is associated
with cancer inflammatory response, the interleukin-17A signaling
pathway, stellate cell activation, and the extracellular-regulated
protein kinase five signaling pathway, which might offer new
therapeutic targets HNSCC through immunologic or antiangiogenic mechanisms. The proposed information fusion system can
promote the determination of high-risk genes related to cancermortality. This study contributes a valid cancer mortality risk
estimate that can identify mortality-related genes. |
| 類型： | 期刊論文 |
| 關鍵詞： | Deep learning (DL)
；
fuzzy theorem
；
head and neck squamous cell carcinoma (HNSCC)
；
regression analysis |
| 作者： | 楊正宏
Sin-Hua Moi
Li-Yeh Chuang
Yu-Da Lin
|
| 學校系所： | 資訊管理系 |
| 卷期： | Institute of Electrical and Electronics Engineers；vol. 36 卷；period 2 期 |
| 頁碼： | 2905-2916 |
