Background Metabolic chemical reaction is one of the main types of fundamental processes to maintain life. Generally, each reaction needs an enzyme. The metabolic pathway collects a series of chemical reactions at the...
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Background Metabolic chemical reaction is one of the main types of fundamental processes to maintain life. Generally, each reaction needs an enzyme. The metabolic pathway collects a series of chemical reactions at the system level. As compounds and enzymes are two important components in each metabolic pathway, identification of metabolic pathways that a given compound or enzyme can participate is the first important step for understanding the mechanism of metabolic *** The purpose of this study was to build efficient computational methods to predict the metabolic pathways of compounds and *** Novel multi-label classifiers were proposed to identify metabolic pathway types, reported in KEGG, of compounds and enzymes. Three heterogeneous networks defining compounds and enzymes as nodes were constructed. To extract more informative features of compounds and enzymes, we generalized the powerful network embedding algorithm, Mashup, to its heterogeneous network version, named MashupH. RAndom k-labELsets (RAKEL) was employed to build the classifiers and support vector machine or random forest was selected as the base classification *** The 10-fold cross-validation results indicated the good performance of the proposed classifiers and such performance was superior to the previous classifier that adopted features yielded by Mashup. Furthermore, some key parameters of MashupH that might contribute to or influence the classifiers were *** The features yielded by MashupH were more informative than those produced by Mashup on heterogeneous networks. This was the main reason the new classifiers were superior to those using features yielded by Mashup.
Background: The Anatomical Therapeutic Chemicals (ATC) classification system is a widely accepted drug classification system. It classifies drugs according to the organ or system in which they can operate and their th...
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Background: The Anatomical Therapeutic Chemicals (ATC) classification system is a widely accepted drug classification system. It classifies drugs according to the organ or system in which they can operate and their therapeutic, pharmacological, and chemical properties. Assigning drugs into 14 classes in the first level of the system is an essential step to understanding drug properties. Several multi-label classifiers have been proposed to identify drug classes. Although their performance was good, most classifiers directly only adopted drug relationships or the features derived from these relationships, but the essential properties of drugs were not directly employed. Thus, classifiers still have a space for improvement. Objective: The aim of this study was to build a novel and powerful multilabel classifier for identifying classes in the first level of the ATC classification system for given drugs. Methods: A powerful multi-label classifier, namely, iATC-NFMLP, was proposed. Two feature types were adopted to encode each drug. The first type was derived from drug relationships via a network embedding algorithm, whereas the second one represented the fingerprints of drugs. Multilayer perceptron using sigmoid as the activating function was used to learn these features for the construction of the classifier. Results: The 10-fold cross-validation results indicated that a combination of the two feature types could improve the performance of the classifier. The jackknife test on the benchmark dataset with 3883 drugs showed that the accuracy and absolute true were 82.76% and 79.27%, respectively. Conclusion: The performance of iATC-NFMLP was best compared with all previous classifiers.
Increasing amounts of experimental studies have shown that circular RNAs (circRNAs) play important regulatory roles in human diseases through interactions with related microRNAs (miRNAs). CircRNAs have become new pote...
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Increasing amounts of experimental studies have shown that circular RNAs (circRNAs) play important regulatory roles in human diseases through interactions with related microRNAs (miRNAs). CircRNAs have become new potential disease biomarkers and therapeutic targets. Predicting circRNA-disease association (CDA) is of great significance for exploring the pathogenesis of complex diseases, which can improve the diagnosis level of diseases and promote the targeted therapy of diseases. However, determination of CDAs through traditional clinical trials is usually time-consuming and expensive. Computational methods are now alternative ways to predict CDAs. In this study, a new computational method, named PCDA-HNMP, was designed. For obtaining informative features of circRNAs and diseases, a heterogeneous network was first constructed, which defined circRNAs, mRNAs, miRNAs and diseases as nodes and associations between them as edges. Then, a deep analysis was conducted on the heterogeneous network by extracting meta-paths connecting to circRNAs (diseases), thereby mining hidden associations between various circRNAs (diseases). These associations constituted the meta-path-induced networks for circRNAs and diseases. The features of circRNAs and diseases were derived from the aforementioned networks via mashup. On the other hand, miRNA-disease associations (mDAs) were employed to improve the model's performance. miRNA features were yielded from the meta-path-induced networks on miRNAs and circRNAs, which were constructed from the meta-paths connecting miRNAs and circRNAs in the heterogeneous network. A concatenation operation was adopted to build the features of CDAs and mDAs. Such representations of CDAs and mDAs were fed into XGBoost to set up the model. The five-fold cross-validation yielded an area under the curve (AUC) of 0.9846, which was better than those of some existing state-of-the-art methods. The employment of mDAs can really enhance the model's performance and the
Background: Protein function is closely related to its location within the cell. Determination of protein subcellular location is helpful in uncovering its functions. However, traditional biological experiments to det...
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Background: Protein function is closely related to its location within the cell. Determination of protein subcellular location is helpful in uncovering its functions. However, traditional biological experiments to determine the subcellular location are of high cost and low efficiency, which cannot meet today's needs. In recent years, many computational models have been set up to identify the subcellular location of proteins. Most models use features derived from protein sequences. Recently, features extracted from the protein-protein interaction (PPI) network have become popular in studying various protein-related problems. Objective: A novel model with features derived from multiple PPI networks was proposed to predict protein subcellular location. Methods: Protein features were obtained by a newly designed network embedding algorithm, Mnode2vec, which is a generalized version of the classic Node2vec algorithm. Two classic classification algorithms: support vector machine and random forest, were employed to build the model. Results: Such model provided good performance and was superior to the model with features extracted by Node2vec. Also, this model outperformed some classic models. Furthermore, Mnode2vec was found to produce powerful features when the path length was small. Conclusion: The proposed model can be a powerful tool to determine protein subcellular location, and Mnode2vec can efficiently extract informative features from multiple networks.
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