.png){kind=icon}
返回
Predicting potential microbe-disease associations based on multi-source features and deep learning
.png){kind=icon}
PRE
AI
DOI：10.1093/bib/bbad255.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can affect tumorigenesis and metastasis by regulating the tumor microenvironment. However, there are still large gaps in the clinical observation of the microbiota in disease. Although biological experiments are accurate in identifying disease-associated microbes, they are also time-consuming and expensive. The computational models for effective identification of diseases related microbes can shorten this process, and reduce capital and time costs. Based on this, in the paper, a model named DSAE_RF is presented to predict latent microbe-disease associations by combining multi-source features and deep learning. DSAE_RF calculates four similarities between microbes and diseases, which are then used as feature vectors for the disease-microbe pairs. Later, reliable negative samples are screened by k-means clustering, and a deep sparse autoencoder neural network is further used to extract effective features of the disease-microbe pairs. In this foundation, a random forest classifier is presented to predict the associations between microbes and diseases. To assess the performance of the model in this paper, 10-fold cross-validation is implemented on the same dataset. As a result, the AUC and AUPR of the model are 0.9448 and 0.9431, respectively. Furthermore, we also conduct a variety of experiments, including comparison of negative sample selection methods, comparison with different models and classifiers, Kolmogorov-Smirnov test and t-test, ablation experiments, robustness analysis, and case studies on Covid-19 and colorectal cancer. The results fully demonstrate the reliability and availability of our model.
Keyword:
microbe-disease associations
k-means clustering
deep sparse autoencoder neural network
random forest
期刊
IF:
7.7
论文数:
5.8K
被引数:
2.7W
机构
引用论文
Role of Porphyromonas gingivalis in oral squamous cell carcinoma development: A systematic review
PREAI
分享收藏Disbiome database: linking the microbiome to diseaseDisbiome数据库: 将微生物组与疾病联系起来.png)
BMC MICROBIOLOGY
OAAI
BMC MICROBIOLOGYIF4.2
OAAI分享收藏分享收藏A highly accurate constant voltage (CV) and constant current (CC) primary side controller for offline applications适用于离线应用的高精度恒定电压 (CV) 和恒定电流 (CC) 一次侧控制器
PREAI
分享收藏Peryton: a manual collection of experimentally supported microbe-disease associationsNUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏
查看更多内容