■微生物与人类疾病有着紧密的联系。平衡的微生物保护人体免受生理紊乱,而不平衡的微生物可能导致疾病。因此,识别微生物与疾病之间的潜在关联可以有助于各种复杂疾病的诊断和治疗。微生物-疾病关联(MDA)预测的生物学实验是昂贵的,耗时,和劳动密集型。
■我们通过结合图注意力自动编码器开发了一种称为GPUDMDA的计算MDA预测方法,积极的无标签学习,和深度神经网络。首先,GPUDMDA通过整合疾病相似度和微生物相似度矩阵的功能相似度和高斯关联谱核相似度来计算疾病相似度和微生物相似度矩阵,分别。接下来,它根据获得的疾病相似度和微生物相似度矩阵,使用图注意力自动编码器学习每个微生物-疾病对的特征表示。第三,它选择了一些可靠的负MDAs基于积极的无标记的学习。最后,它将学习到的MDA特征和选定的负MDA作为输入,并设计了一个深度神经网络来预测潜在的MDA。
■将GPUDMDA与四种最先进的MDA识别模型进行了比较(即,MNNMDA,GATMDA,LRLSHMDA,和NTSHMDA)在HMDAD和Disbiome数据库上,对微生物进行了五次交叉验证,疾病,和微生物-疾病对。在三个五折交叉验证下,GPUDMDA计算出HMDAD数据库上的最佳AUC为0.7121、0.9454和0.9501,以及Disbiome数据库上的0.8372、0.8908和0.8948。分别,优于其他四种MDA预测方法。哮喘是最常见的慢性呼吸系统疾病,影响全球约3.39亿人。炎症性肠病是一类广泛存在于患者肠道、胃肠道和肠外器官的全球性慢性肠道疾病。特别是,炎症性肠病严重影响儿童的生长发育。我们使用了拟议的GPUDMDA方法,发现肠杆菌与哮喘和炎症性肠病都有潜在的关联,需要进一步的生物学实验验证。
■提出的GPUMDA展示了强大的MDA预测能力。我们预计GPUDMDA有助于筛选微生物相关疾病的治疗线索。
UNASSIGNED: Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe-disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.
UNASSIGNED: We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe-disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.
UNASSIGNED: GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.
UNASSIGNED: The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.