Dataset Information

Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction.

ABSTRACT: MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molecule similarity network, miRNA similarity network and known small molecule-miRNA association network. This model described the complex relationship between two small molecules or between two miRNAs using graphlet interaction which consists of 28 isomers. The association score between a small molecule and a miRNA was calculated based on counting the numbers of graphlet interaction throughout the small molecule similarity network and the miRNA similarity network, respectively. Global and two types of local leave-one-out cross validation (LOOCV) as well as five-fold cross validation were implemented in two datasets to evaluate GISMMA. For Dataset 1, the AUCs are 0.9291 for global LOOCV, 0.9505, and 0.7702 for two local LOOCVs, 0.9263 ± 0.0026 for five-fold cross validation; for Dataset 2, the AUCs are 0.8203, 0.8640, 0.6591, and 0.8554 ± 0.0063, in turn. In case study for small molecules, 5-Fluorouracil, 17?-Estradiol and 5-Aza-2'-deoxycytidine, the numbers of top 50 miRNAs predicted by GISMMA and validated to be related to these three small molecules by experimental literatures are in turn 30, 29, and 25. Based on the results from cross validations and case studies, it is easy to realize the excellent performance of GISMMA.

SUBMITTER: Guan NN

PROVIDER: S-EPMC6196296 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction.

Guan Na-Na NN Sun Ya-Zhou YZ Ming Zhong Z Li Jian-Qiang JQ Chen Xing X

Frontiers in pharmacology 20181015

MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molec ...[more]

PMID: 30374302

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Project description:Different types of chemicals and products may exhibit various health risks when administered into the human body. For toxicity reasons, the number of new drugs entering the market through the conventional drug development process has been reduced over the years. However, with the advent of big data and artificial intelligence, machine learning techniques have emerged as a potential solution for predicting toxicity and ensuring efficient drug development and chemical safety. An ML model for toxicity prediction can reduce experimental costs and time while addressing ethical concerns by drastically reducing the need for animals and clinical trials. Herein, MolToxPred, an ML-based tool, has been developed using a stacked model approach to predict the potential toxicity of small molecules and metabolites. The stacked model consists of random forest, multi-layer perceptron, and LightGBM as base classifiers and Logistic Regression as the meta classifier. For training and validation purposes, a comprehensive set of toxic and non-toxic molecules is curated. Different structural and physicochemical-based features in the form of molecular descriptors and fingerprints were employed. MolToxPred utilizes a comprehensive feature selection process and optimizes its hyperparameters through Bayesian optimization with stratified 5-fold cross-validation. In the evaluation phase, MolToxPred achieved an AUROC of 87.76% on the test set and 88.84% on an external validation set. The McNemar test was used as the post-hoc test to determine if the stacked models' performance was significantly different compared to the base learners. The developed stacked model outperformed its base classifiers and an existing tool in the literature, reaffirming its better performance. The hypothesis is that the incorporation of a diverse set of data, the subsequent feature selection, and a stacked ensemble approach give MolToxPred the edge over other methods. In addition to this, an attempt has been made to identify structural alerts responsible for endpoints of the Tox21 data to determine the association of a molecule with a plausible downstream pathway of action. MolToxPred may be helpful for drug discovery and regulatory pipelines in pharmaceutical and other industries for in silico toxicity prediction of small molecule candidates.

Project description:MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.

Project description:MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model.Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations.Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks.

Dataset Information

Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction.

Publications

Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction.

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