RNA sequencing identifies dysregulated circular RNAs in early-stage breast cancer

Arvinden, Vittal Rangan; Kuha, D. M. R. Arinagiri; Meenakumari, Balaiah; Ramanathan, Priya; Sundersingh, Shirley; Velusami, Sri Devi; Rajkumar, Thangarajan; Herceg, Zdenko; Mani, Samson

doi:10.1101/506246

Cited by 1 publication

(1 citation statement)

References 48 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, increasing numbers of evidences and studies have shown that circRNAs play significant roles in disease diagnosis and treatment 13. Especially for various cancers 14, 15, cardiovascular disease 16, diabetes 17, etc. Current researches of the associations between circRNAs and diseases are relatively advanced works, which might be based on the pathway circRNA-miRNA-mRNA to explore potential knowledge.…”

Section: Introductionmentioning

confidence: 99%

GBDTCDA: Predicting circRNA-disease Associations Based on Gradient Boosting Decision Tree with Multiple Biological Data Fusion

Lei

Fang

2019

Int. J. Biol. Sci.

View full text Add to dashboard Cite

Circular RNA (circRNA) is a closed-loop structural non-coding RNA molecule which plays a significant role during the gene regulation processes. There are many previous studies shown that circRNAs can be regarded as the sponges of miRNAs. Thus, circRNA is also a key point for disease diagnosing, treating and inferring. However, traditional experimental approaches to verify the associations between the circRNA and disease are time-consuming and money-consuming. There are few computational models to predict potential circRNA-disease associations, which become our motivation to propose a new computational model. In this study, we propose a machine learning based computational model named Gradient Boosting Decision Tree with multiple biological data to predict circRNA-disease associations (GBDTCDA). The known circRNA-disease associations' data are downloaded from cricR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). The feature vector of each circRNA-disease association pair is composed of four parts, which are the statistics information of different biological networks, the graph theory information of different biological networks, circRNA-disease associations' network information and circRNA nucleotide sequence information, respectively. Therefore, we use those feature vectors to train the gradient boosting decision tree regression model. Then, the leave one out cross validation (LOOCV) is adopted to evaluate the performance of our computational model. As for predicting some common diseases related circRNAs, our method GBDTCDA also obtains the better results. The Area under the ROC Curve (AUC) values of Basal cell carcinoma, Non-small cell lung cancer and cervical cancer are 95.8%, 88.3% and 93.5%, respectively. For further illustrating the performance of GBDTCDA, a case study of breast cancer is also supplemented in this study. Thus, our proposed method GBDTCDA is a powerful tool to predict potential circRNA-disease associations based on experimental results and analyses.

show abstract