Biology subjects

You, Z.-H.

Publications and source records attributed to You, Z.-H..

10 recordsLinked to original sources

bioRxiv2022

An improved graph representation learning method for drug-target interaction prediction over heterogeneous biological information graph

The prediction task of the relationships between drugs and targets plays a significant role in the process of new drug discovery. Computational-based strategies for predicting drug-target interactions (DTIs) are regarded as a high-efficiency way. Moreover, recent studies adopted a graph neural network (GNN) to discover underlying DTIs and achieved better performance. Although these inductive methods can straightway learn biomolecules latent representations, they have an over-smoothing phenomenon in the course of obtaining the rich neighborhood information of each node in the biological information network, which further leads to a consistent feature representation of each node. To address the above issues, a novel model, called iGRLDTI, is proposed to precisely identify new DTIs based on an improved graph representation learning strategy. Specifically, iGRLDTI first constructs a biological information graph (BIG) by calculating the biological knowledge of drugs and targets with the relationships between them. Then, an improved graph representation learning strategy is designed to capture the enriched feature representations of drugs and targets. Finally, the Gradient Boosting Decision Tree classifier is applied to predict potential DTIs. Experimental results demonstrate that iGRLDTI yields better performance by comparing it with other state-of-the-art models on the benchmark dataset. Besides, our case studies denote that iGRLDTI can successfully identify unknown DTIs according to the improved feature representations of drugs and targets.

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bioRxiv2020

SPRDA: a matrix completion approach based on the structural perturbation to infer disease-associated Piwi-Interacting RNAs

Emerging evidence suggests that PIWI-interacting RNAs (piRNAs) are one of the most influential small non-coding RNAs (ncRNAs) that regulate RNA silencing. piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. The main motivation of this work is tantamount to fill the gap in research on large-scale prediction of disease-related piRNAs. In this study, a novel computational model based on the structural perturbation method is proposed, called SPRDA. In detail, the duplex network is constructed based on the piRNA similarity network and disease similarity network extracted from piRNA sequence information, Gaussian interaction profile kernel similarity information and gene-disease association information. The structural perturbation method is then used to predict the potential associations on the duplex network, which is more predictive than other network structures in terms of structural consistency. In the five-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.Competing Interest StatementThe authors have declared no competing interest.View Full Text

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bioRxiv2020

iMDA-BN: Identification of miRNA-Disease Associations based on the Biological Network and Graph Embedding Algorithm

Benefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively.

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bioRxiv2020

Inferring Disease-Associated Piwi-Interacting RNAs via Graph Attention Networks

MotivationPIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlates with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes out gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. This study aims to make preliminary attempts on bionetwork based graph mining. ResultsIn this study, we present a method based on graph attention network to identify potential and biologically significant piRNA-disease associations (PDAs), called GAPDA. The attention mechanism can calculate a hidden representation of an association in the network based on neighbor nodes and assign weights to the input to make decisions. In particular, we introduced the attention-based Graph Neural Networks to the field of bio-association prediction for the first time, and proposed an abstract network topology suitable for small samples. Specifically, we combined piRNA sequence information and disease semantic similarity with piRNA-disease association network to construct a new attribute network. In the experiment, GAPDA performed excellently in five-fold cross-validation with the AUC of 0.9038. Not only that, but it still has superior performance compared to methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research. Contactzhuhongyou@ms.xjb.ac.cn;leiwang@ms.xjb.ac.cn Supplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2020

RFDTI: Using Rotation Forest with Feature Weighted for Drug-Target Interaction Prediction from Drug Molecular Structure and Protein Sequence

The identification and prediction of Drug-Target Interactions (DTIs) is the basis for screening drug candidates, which plays a vital role in the development of innovative drugs. However, due to the time-consuming and high cost constraints of biological experimental methods, traditional drug target identification technologies are often difficult to develop on a large scale. Therefore, in silico methods are urgently needed to predict drug-target interactions in a genome-wide manner. In this article, we design a new in silico approach, named RFDTI to predict the DTIs combine Feature weighted Rotation Forest (FwRF) classifier with protein amino acids information. This model has two outstanding advantages: a) using the fusion data of protein sequence and drug molecular fingerprint, which can fully carry information; b) using the classifier with feature selection ability, which can effectively remove noise information and improve prediction performance. More specifically, we first use Position-Specific Score Matrix (PSSM) to numerically convert protein sequences and utilize Pseudo Position-Specific Score Matrix (PsePSSM) to extract their features. Then a unified digital descriptor is formed by combining molecular fingerprints representing drug information. Finally, the FwRF is applied to implement on Enzyme, Ion Channel, GPCR, and Nuclear Receptor data sets. The results of the five-fold cross-validation experiment show that the prediction accuracy of this approach reaches 91.68%, 88.11%, 84.72% and 78.33% on four benchmark data sets, respectively. To further validate the performance of the RFDTI, we compare it with other excellent methods and Support Vector Machine (SVM) model. In addition, 7 of the 10 highest predictive scores in predicting novel DTIs were validated by relevant databases. The experimental results of cross-validation indicated that RFDTI is feasible in predicting the relationship among drugs and target, and can provide help for the discovery of new candidate drugs.

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bioRxiv2019

GCNCDA: A New Method for Predicting CircRNA-Disease Associations Based on Graph Convolutional Network Algorithm

Numerous evidences indicate that Circular RNAs (circRNAs) are widely involved in the occurrence and development of diseases. Identifying the association between circRNAs and diseases plays a crucial role in exploring the pathogenesis of complex diseases and improving the diagnosis and treatment of diseases. However, due to the complex mechanisms between circRNAs and diseases, it is expensive and time-consuming to discover the new circRNA-disease associations by biological experiment. Therefore, there is increasingly urgent need for utilizing the computational methods to predict novel circRNA-disease associations. In this study, we propose a computational method called GCNCDA based on the deep learning Fast learning with Graph Convolutional Networks (FastGCN) algorithm to predict the potential disease-associated circRNAs. Specifically, the method first forms the unified descriptor by fusing disease semantic similarity information, disease and circRNA Gaussian Interaction Profile (GIP) kernel similarity information based on known circRNA-disease associations. The FastGCN algorithm is then used to objectively extract the high-level features contained in the fusion descriptor. Finally, the new circRNA-disease associations are accurately predicted by the Forest by Penalizing Attributes (Forest PA) classifier. The 5-fold cross-validation experiment of GCNCDA achieved 91.2% accuracy with 92.78% sensitivity at the AUC of 90.90% on circR2Disease benchmark dataset. In comparison with different classifier models, feature extraction models and other state-of-the-art methods, GCNCDA shows strong competitiveness. Furthermore, 10 of the top 15 circRNA-disease association candidates with the highest prediction scores were confirmed by recently published literature. These results suggest that GCNCDA can effectively predict potential circRNA-disease associations and provide highly credible candidates for biological experiments. Author SummaryThe recognition of circRNA-disease association is the key of disease diagnosis and treatment, and it is of great significance for exploring the pathogenesis of complex diseases. Computational methods can predicte the potential disease-related circRNAs quickly and accurately. Based on the hypothesis that circRNA with similar function tends to associate with similar disease, GCNCDA model is proposed to effectively predict the potential association between circRNAs and diseases by combining FastGCN algorithm. The performance of the model was verified by cross-validation experiments, different feature extraction algorithm and classifier models comparison experiments. Furthermore, 10 of the top 15 disease-associated circRNAs with the highest prediction scores were confirmed by recently published literature. It is anticipated that GCNCDA model can give priority to the most promising circRNA-disease associations on a large scale to provide reliable candidates for further biological experiment.

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bioRxiv2019

Biomarker2vec: Attribute- and Behavior-driven Representation for Multi-type Relationship Prediction between Various Biomarkers

The explosive growth of genomic, chemical and pathological data provides new opportunities and challenges to re-recognize life activities within human cells. However, there exist few computational models that aggregate various biomarkers to comprehensively reveal the physical and functional landscape of the biology system. Here, we construct a graph called Molecular Association Network (MAN) and a representation method called Biomarker2vec. Specifically, MAN is a heterogeneous attribute network consists of 18 kinds of edges (relationships) among 8 kinds of nodes (biomarkers). Biomarker2vec is an algorithm that represents the nodes as vectors by integrating biomarker attribute and behavior. After the biomarkers are described as vectors, random forest classifier is applied to carry out the prediction task. Our approach achieved promising performance on 18 relationships, with AUC of 0.9608 and AUPR of 0.9572. We also empirically explored the contribution of attribute and behavior feature of biomarkers to the results. In addition, a drug-disease association prediction case study was performed to validate our methods ability on a specific object. These results strongly prove that MAN is a network with rich topological and biological information and Biomarker2vec can indeed adequately characterize biomarkers. Generally, our method can achieve simultaneous prediction of both single-type and multi-type relationships, which bring beneficial inspiration to relevant scholars and expand the medical research paradigm.

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bioRxiv2019

MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm

MotivationEffectively representing the MeSH headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify. ResultsIn this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships) which can be constructed by the rule of tree num. Then, five graph embedding algorithms including DeepWalk (DW), LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed method, we carried out the node classification and relationship prediction tasks. The experimental results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the distinguishable ability of vectors. Thus, it can act as input and continue to play a significant role in any disease-, drug-, microbe- and etc.-related computational models. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective. Contactzhuhongyou@ms.xjb.ac.cn

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bioRxiv2019

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been involved in various biological processes. Emerging evidence suggests that the interactions between lncRNAs and miRNAs play an important role in regulating of genes and the development of many diseases. Due to the limited scale of known lncRNA-miRNA interactions, and expensive time and labor costs for identifying them by biological experiments, more accurate and efficient lncRNA-miRNA interactions computational prediction approach urgently need to be developed. In this work, we proposed a novel computational method, GNMFLMI, to predict lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization. More specifically, the similarities both lncRNA and miRNA are calculated based on known interaction information and their sequence information. Then, the affinity graphs for lncRNAs and miRNAs are constructed using the p-nearest neighbors, respectively. Finally, a graph regularized nonnegative matrix factorization model is developed to accurately identify potential interactions between lncRNAs and miRNAs. To evaluate the performance of GNMFLMI, five-fold cross validation experiments are carried out. GNMFLMI achieves the AUC value of 0.9769 which outperforms the compared methods NMF and CNMF. In the case studies for lncRNA nonhsat159254.1 and miRNA hsa-mir-544a, 20 and 16 of the top-20 associations predicted by GNMFLMI are confirmed, respectively. Rigorous experimental results demonstrate that GNMFLMI can effectively predict novel lncRNA-miRNA interactions, which can provide guidance for relevant biomedical research.

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bioRxiv2019

Construct a molecular associations network to systematically understand intermolecular associations in Human cells

A key aim of post-genomic biomedical research is to systematically integrate and model all molecules and their interactions in living cells. Existing research usually only focusing on the associations between individual or very limited type of molecules. But the interactions between molecules shouldnt be isolated but interconnected and influenced. In this study, we revealed, constructed and analyzed a large-scale molecular association network of multiple biomolecules in human cells by modeling all associations among lncRNA, miRNA, protein, circRNA, microbe, drug, and disease, in which various associations are interconnected and any type of associations can be predicted. More specifically, we defined the molecular associations network and constructed a molecular associations dataset containing 105546 associations. Then, each node is represented by its attribute feature and network embedding learned by Structural Deep Network Embedding. Moreover, Random Forest is trained to predict any kind of associations. And we compared the features and classifiers under five-fold cross-validation. Our method achieves a remarkable performance on entire molecular associations network with an AUC of 0.9552 and an AUPR of 0.9338. To further evaluate the performance of our method, a case study for predicting lncRNA-protein interactions was executed. The experimental results demonstrate that the systematic insight for understanding the synergistic interactions between various molecules and complex diseases. It is anticipated that this work can bring beneficial inspiration and advance related systems biology and biomedical research.\n\nAuthor SummaryThe interactions between the various biomolecules in the cells should not be isolated, but interconnected and influenced. There have been many valuable studies on the interactions between two individual molecules. Based on a systematic and holistic perspective, we revealed and constructed a large-scale molecular associations network by combining various associations in human living cells, including miRNA-lncRNA association, miRNA-disease association, miRNA-protein interaction, lncRNA-disease association, protein-protein interaction, protein-disease association, drug-protein interaction, drug-disease interaction, and lncRNA-protein interaction. To model and analyze this molecular associations network, we employed the network representation learning model to learn how to represent the node. Each node in the network can be represented by network embedding and its own attribute information. Any node can be classified. And any type of the associations in this network can be predicted, which can be considered as link prediction task. Our work provides a new systematic view and conceptual framework to understand complex diseases and life activities. It is anticipated that our study can advance related biological macromolecules, systems biology and biomedical research, and bring some meaningful inspiration.

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