Prediction of lncRNA-Disease Associations from Heterogeneous Information Network Based on DeepWalk Embedding Model

Song, Xiaoyu; Liu, Tong; Qiu, Zeyang; You, Zhu‐Hong; Sun, Yue; Jin, Li-ting; Feng, Xiaobei; Zhu, Lin

doi:10.1007/978-3-030-60796-8_25

Cited by 5 publications

(4 citation statements)

References 52 publications

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“…By comparing the order of predictions with the order of ground truths, other measures, such as the concordance index (CI or C-index) and Spearman's correlation coefficient (ρ), can be used to measure the accuracy of rankings. CI is one of the frequently used ranking metrics in the prediction of drug-target-affinity [137,153]. The CI measures whether the predicted binding affinity values of two random drug-target combinations were in accordance with those actual values.…”

Section: Regression Evaluation Metricsmentioning

confidence: 99%

Deep Learning Based Methods for Molecular Similarity Searching: A Systematic Review

2023

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In rational drug design, the concept of molecular similarity searching is frequently used to identify molecules with similar functionalities by looking up structurally related molecules in chemical databases. Different methods have been developed to measure the similarity of molecules to a target query. Although the approaches perform effectively, particularly when dealing with molecules with homogenous active structures, they fall short when dealing with compounds that have heterogeneous structural compounds. In recent times, deep learning methods have been exploited for improving the performance of molecule searching due to their feature extraction power and generalization capabilities. However, despite numerous research studies on deep-learning-based molecular similarity searches, relatively few secondary research was carried out in the area. This research aims to provide a systematic literature review (SLR) on deep-learning-based molecular similarity searches to enable researchers and practitioners to better understand the current trends and issues in the field. The study accesses 875 distinctive papers from the selected journals and conferences, which were published over the last thirteen years (2010–2023). After the full-text eligibility analysis and careful screening of the abstract, 65 studies were selected for our SLR. The review’s findings showed that the multilayer perceptrons (MLPs) and autoencoders (AEs) are the most frequently used deep learning models for molecular similarity searching; next are the models based on convolutional neural networks (CNNs) techniques. The ChEMBL dataset and DrugBank standard dataset are the two datasets that are most frequently used for the evaluation of deep learning methods for molecular similarity searching based on the results. In addition, the results show that the most popular methods for optimizing the performance of molecular similarity searching are new representation approaches and reweighing features techniques, and, for evaluating the efficiency of deep-learning-based molecular similarity searching, the most widely used metrics are the area under the curve (AUC) and precision measures.

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Section: Regression Evaluation Metricsmentioning

confidence: 99%

Deep Learning Based Methods for Molecular Similarity Searching: A Systematic Review

2023

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“…Ji et al predicted miRNA–disease associations based on the GraRep embedding model ( Ji et al, 2020 ). Song et al predicted lncRNA–disease associations based on the DeepWalk embedding model ( Song et al, 2020 ). The third category is based on deep learning, among which the most representative method is the graph convolution network (GCN).…”

Section: Introductionmentioning

confidence: 99%

Predicting Pseudogene–miRNA Associations Based on Feature Fusion and Graph Auto-Encoder

et al. 2021

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Pseudogenes were originally regarded as non-functional components scattered in the genome during evolution. Recent studies have shown that pseudogenes can be transcribed into long non-coding RNA and play a key role at multiple functional levels in different physiological and pathological processes. microRNAs (miRNAs) are a type of non-coding RNA, which plays important regulatory roles in cells. Numerous studies have shown that pseudogenes and miRNAs have interactions and form a ceRNA network with mRNA to regulate biological processes and involve diseases. Exploring the associations of pseudogenes and miRNAs will facilitate the clinical diagnosis of some diseases. Here, we propose a prediction model PMGAE (Pseudogene–MiRNA association prediction based on the Graph Auto-Encoder), which incorporates feature fusion, graph auto-encoder (GAE), and eXtreme Gradient Boosting (XGBoost). First, we calculated three types of similarities including Jaccard similarity, cosine similarity, and Pearson similarity between nodes based on the biological characteristics of pseudogenes and miRNAs. Subsequently, we fused the above similarities to construct a similarity profile as the initial representation features for nodes. Then, we aggregated the similarity profiles and associations of nodes to obtain the low-dimensional representation vector of nodes through a GAE. In the last step, we fed these representation vectors into an XGBoost classifier to predict new pseudogene–miRNA associations (PMAs). The results of five-fold cross validation show that PMGAE achieves a mean AUC of 0.8634 and mean AUPR of 0.8966. Case studies further substantiated the reliability of PMGAE for mining PMAs and the study of endogenous RNA networks in relation to diseases.

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“…An lncRNA-disease association prediction model based on RF and feature selection, RFLDA, is proposed by Yao et al (2020). A predictive lncRNA-disease prediction model based on heterogeneous networks is proposed by Song et al (2020). The LDAMAN is proposed by Zhang et al (2020), which uses a structural deep network embedding model.…”

Section: Introductionmentioning

confidence: 99%

GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

Duan

Kuang

Wang

et al. 2021

Front. Cell Dev. Biol.

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In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA–disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNA–disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA–disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample sets are selected by using K-means clustering. The innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative negative sample set, and the use of MetaGraph2Vec can better retain the semantic and structural features in heterogeneous networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.

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Prediction of lncRNA-Disease Associations from Heterogeneous Information Network Based on DeepWalk Embedding Model

Cited by 5 publications

References 52 publications

Deep Learning Based Methods for Molecular Similarity Searching: A Systematic Review

Deep Learning Based Methods for Molecular Similarity Searching: A Systematic Review

Predicting Pseudogene–miRNA Associations Based on Feature Fusion and Graph Auto-Encoder

GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

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