Pub Date : 2024-03-11DOI: 10.2174/0115748936280392240219054047
Medard Edmund Mswahili, Goodwill Erasmo Ndomba, Young Jin Kim, Kyuri Jo, Young-Seob Jeong
Background: The potential of graph neural networks (GNNs) to revolutionize the analysis of non-Euclidean data has gained attention recently, making them attractive models for deep machine learning. However, insufficient compound or moleculargraphs and feature representations might significantly impair and jeopardize their full potential. Despite the devastating impacts of ongoing COVID-19 across the globe, for which there is no drug with proven efficacy that has been shown tobe effective. As various stages of drug discovery and repositioning require the accurate prediction of drugtarget interactions(DTI), here, we propose a relational graph convolution network using multi-features based on the developed drug chemicalcompound-coronavirus target graph representation and combination of features. During the implementation of the model, we further introduced the use of not only the feature module to understand the topological structure of drugs but also the structure of the proven drug target (i.e., 3CLpro) for SARS-Cov-2 that shares a genome sequence similar to that of other members of the beta-coronavirus group such as SARS-Cov, MERS-CoV, bat coronavirus. Our feature comprises topologicalinformation in molecular SMILES and local chemical context in the SMILES sequence for the drug chemical compound and drug target. Our proposed method prevailed with high and compelling performance accuracy of 97.30% which could beprioritized as the potential and promising prediction route for the development of novel oral antiviral medicine for COVID-19 drugs. Objective: Forecasting DTI stands as a pivotal aspect of drug discovery. The focus on computational methods in DTI prediction has intensified due to the considerable expense and time investment associated with conducting extensive in vitro and in vivo experiments. Machine learning techniques, particularly deep learning, have found broad applications in DTI prediction. We are convinced that this study could be prioritized and utilized as the promising predictive route for the development of novel oral antiviral treatments for COVID-19 and other variants of coronaviruses. Methods: This study addressed the problem of COVID-19 drugs using proposed RGCN with multifeatures as an attractive and potential route. This study focused mainly on the prediction of novel antiviral drugs against coronaviruses using graph-based methodology, namely RGCN. This research further utilized the features of both drugs and common potential drug targets found in betacoronaviruses group to deepen understanding of their underlying relation. Results: Our suggested approach prevailed with a high and convincing performance accuracy of 97.30%, which may be utilizedas a top priority to support and advance this field in the prediction and development of novel antiviral treatments against coronaviruses and their variants. Conclusion: We recursively performed experiments using the proposed method on our constructed DCCCvT graph dataset from our c
{"title":"Relational Graph Convolution Network with Multi Features for AntiCOVID-19 Drugs Discovery using 3CLpro Potential Target","authors":"Medard Edmund Mswahili, Goodwill Erasmo Ndomba, Young Jin Kim, Kyuri Jo, Young-Seob Jeong","doi":"10.2174/0115748936280392240219054047","DOIUrl":"https://doi.org/10.2174/0115748936280392240219054047","url":null,"abstract":"Background: The potential of graph neural networks (GNNs) to revolutionize the analysis of non-Euclidean data has gained attention recently, making them attractive models for deep machine learning. However, insufficient compound or moleculargraphs and feature representations might significantly impair and jeopardize their full potential. Despite the devastating impacts of ongoing COVID-19 across the globe, for which there is no drug with proven efficacy that has been shown tobe effective. As various stages of drug discovery and repositioning require the accurate prediction of drugtarget interactions(DTI), here, we propose a relational graph convolution network using multi-features based on the developed drug chemicalcompound-coronavirus target graph representation and combination of features. During the implementation of the model, we further introduced the use of not only the feature module to understand the topological structure of drugs but also the structure of the proven drug target (i.e., 3CLpro) for SARS-Cov-2 that shares a genome sequence similar to that of other members of the beta-coronavirus group such as SARS-Cov, MERS-CoV, bat coronavirus. Our feature comprises topologicalinformation in molecular SMILES and local chemical context in the SMILES sequence for the drug chemical compound and drug target. Our proposed method prevailed with high and compelling performance accuracy of 97.30% which could beprioritized as the potential and promising prediction route for the development of novel oral antiviral medicine for COVID-19 drugs. Objective: Forecasting DTI stands as a pivotal aspect of drug discovery. The focus on computational methods in DTI prediction has intensified due to the considerable expense and time investment associated with conducting extensive in vitro and in vivo experiments. Machine learning techniques, particularly deep learning, have found broad applications in DTI prediction. We are convinced that this study could be prioritized and utilized as the promising predictive route for the development of novel oral antiviral treatments for COVID-19 and other variants of coronaviruses. Methods: This study addressed the problem of COVID-19 drugs using proposed RGCN with multifeatures as an attractive and potential route. This study focused mainly on the prediction of novel antiviral drugs against coronaviruses using graph-based methodology, namely RGCN. This research further utilized the features of both drugs and common potential drug targets found in betacoronaviruses group to deepen understanding of their underlying relation. Results: Our suggested approach prevailed with a high and convincing performance accuracy of 97.30%, which may be utilizedas a top priority to support and advance this field in the prediction and development of novel antiviral treatments against coronaviruses and their variants. Conclusion: We recursively performed experiments using the proposed method on our constructed DCCCvT graph dataset from our c","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"43 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.2174/0115748936288616240212073805
Guanghui Li, Ziyan Cao, Cheng Liang, Qiu Xiao, Jiawei Luo
Background: Complex and diverse microbial communities play a pivotal role in human health and have become a new drug target. Exploring the connections between drugs and microbes not only provides profound insights into their mechanisms but also drives progress in drug discovery and repurposing. The use of wet lab experiments to identify associations is time-consuming and laborious. Hence, the advancement of precise and efficient computational methods can effectively improve the efficiency of association identification between microorganisms and drugs. Objective: In this experiment, we propose a new deep learning model, a new multiview comparative hypergraph attention network (MCHAN) method for human microbe–drug association prediction. Methods: First, we fuse multiple similarity matrices to obtain a fused microbial and drug similarity network. By combining graph convolutional networks with attention mechanisms, we extract key information from multiple perspectives. Then, we construct two network topologies based on the above fused data. One topology incorporates the concept of hypernodes to capture implicit relationships between microbes and drugs using virtual nodes to construct a hyperheterogeneous graph. Next, we propose a cross-contrastive learning task that facilitates the simultaneous guidance of graph embeddings from both perspectives, without the need for any labels. This approach allows us to bring nodes with similar features and network topologies closer while pushing away other nodes. Finally, we employ attention mechanisms to merge the outputs of the GCN and predict the associations between drugs and microbes. Results: To confirm the effectiveness of this method, we conduct experiments on three distinct datasets. The results demonstrate that the MCHAN model surpasses other methods in terms of performance. Furthermore, case studies provide additional evidence confirming the consistent predictive accuracy of the MCHAN model. Conclusion: MCHAN is expected to become a valuable tool for predicting potential associations between microbiota and drugs in the future.
{"title":"MCHAN: Prediction of Human Microbe-drug Associations Based on Multiview Contrastive Hypergraph Attention Network","authors":"Guanghui Li, Ziyan Cao, Cheng Liang, Qiu Xiao, Jiawei Luo","doi":"10.2174/0115748936288616240212073805","DOIUrl":"https://doi.org/10.2174/0115748936288616240212073805","url":null,"abstract":"Background: Complex and diverse microbial communities play a pivotal role in human health and have become a new drug target. Exploring the connections between drugs and microbes not only provides profound insights into their mechanisms but also drives progress in drug discovery and repurposing. The use of wet lab experiments to identify associations is time-consuming and laborious. Hence, the advancement of precise and efficient computational methods can effectively improve the efficiency of association identification between microorganisms and drugs. Objective: In this experiment, we propose a new deep learning model, a new multiview comparative hypergraph attention network (MCHAN) method for human microbe–drug association prediction. Methods: First, we fuse multiple similarity matrices to obtain a fused microbial and drug similarity network. By combining graph convolutional networks with attention mechanisms, we extract key information from multiple perspectives. Then, we construct two network topologies based on the above fused data. One topology incorporates the concept of hypernodes to capture implicit relationships between microbes and drugs using virtual nodes to construct a hyperheterogeneous graph. Next, we propose a cross-contrastive learning task that facilitates the simultaneous guidance of graph embeddings from both perspectives, without the need for any labels. This approach allows us to bring nodes with similar features and network topologies closer while pushing away other nodes. Finally, we employ attention mechanisms to merge the outputs of the GCN and predict the associations between drugs and microbes. Results: To confirm the effectiveness of this method, we conduct experiments on three distinct datasets. The results demonstrate that the MCHAN model surpasses other methods in terms of performance. Furthermore, case studies provide additional evidence confirming the consistent predictive accuracy of the MCHAN model. Conclusion: MCHAN is expected to become a valuable tool for predicting potential associations between microbiota and drugs in the future.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"226 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-22DOI: 10.2174/0115748936285057240126062220
Efendi Zaenudin, Ezra B. Wijaya, Venugopala Reddy Mekala, Ka-Lok Ng
Objective: We propose a novel method to compare directed networks by decomposing the network into small modules, the so-called network subgraph approach, which is distinct from the network motif approach because it does not depend on null model assumptions. Method: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS). Method: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS). Results:: We identified and examined the biological meaning of 5-node regulatory modules and pairs of cancer networks with the smallest HJS values. The two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma and (ii) breast cancer and pathways in cancer. Some studies have provided experimental data supporting the 5-node regulatory modules. result: We identify and examine the biological meaning of 5-node regulatory modules and pairs of cancer networks which have the smallest HJS values. These two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma, and (ii) breast cancer and pathways in cancer. Some literature studies provide experimental data to support the 5-node regulatory modules. Conclusion: Our method is an alignment-free approach that measures the topological similarity of 5-node regulatory modules and aligns two directed networks based on their topology. These modules capture complex interactions among multiple genes that cannot be detected using existing methods that only consider single-gene relations. We analyzed the biological relevance of the regulatory modules and used the subgraph method to identify the modules that shared the same topology across 2 cancer networks out of 17 cancer networks. We validated our findings using evidence from the literature.
{"title":"Network Subgraph-based Method: Alignment-free Technique for Molecular Network Analysis","authors":"Efendi Zaenudin, Ezra B. Wijaya, Venugopala Reddy Mekala, Ka-Lok Ng","doi":"10.2174/0115748936285057240126062220","DOIUrl":"https://doi.org/10.2174/0115748936285057240126062220","url":null,"abstract":"Objective: We propose a novel method to compare directed networks by decomposing the network into small modules, the so-called network subgraph approach, which is distinct from the network motif approach because it does not depend on null model assumptions. Method: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS). Method: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS). Results:: We identified and examined the biological meaning of 5-node regulatory modules and pairs of cancer networks with the smallest HJS values. The two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma and (ii) breast cancer and pathways in cancer. Some studies have provided experimental data supporting the 5-node regulatory modules. result: We identify and examine the biological meaning of 5-node regulatory modules and pairs of cancer networks which have the smallest HJS values. These two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma, and (ii) breast cancer and pathways in cancer. Some literature studies provide experimental data to support the 5-node regulatory modules. Conclusion: Our method is an alignment-free approach that measures the topological similarity of 5-node regulatory modules and aligns two directed networks based on their topology. These modules capture complex interactions among multiple genes that cannot be detected using existing methods that only consider single-gene relations. We analyzed the biological relevance of the regulatory modules and used the subgraph method to identify the modules that shared the same topology across 2 cancer networks out of 17 cancer networks. We validated our findings using evidence from the literature.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-20DOI: 10.2174/0115748936258790240101062642
Chuqi Lei, Senbiao Fang, Yaohang Li, Fei Guo, Min Li
background: computational molecular docking plays an important role in determining the precise receptor-ligand conformation, which becomes a powerful tool for drug discovery. In the past 30 years, most computational docking methods treat the receptor structure as a rigid body, although flexible docking often yields higher accuracy. The main disadvantage of flexible docking is its significantly higher computational cost. Due to the fact that different protein pock-et residues exhibit different degrees of flexibility, semi-flexible docking methods, balancing rigid docking and flexible docking, have demonstrated success in predicting highly accurate conformations with a relatively low computational cost. method: In our study, the number of flexible pocket residues was assessed by quantitative analysis, and a novel adaptive residue flexibility prediction method, named A-RFP, was proposed to improve the docking performance. Based on the homologous information, a joint strategy is used to predict the pocket residue flexibility by combining RMSD, the distance between the residue sidechain and the ligand, and the sidechain orientation. For each receptor-ligand pair, A-RFP provides a docking conformation with the optimal affinity. result: By analyzing the docking affinities of 3507 target-ligand pairs in 5 different values ranging from 0 to 10, we found there is a general trend that the larger number of flexible residues inevitably improves the docking results by using Autodock Vina. However, a certain number of counterexamples still exist. To validate the effectiveness of A-RFP, the experimental assessment was tested in a small-scale virtual screening on 5 proteins, which confirmed that A-RFP could enhance the docking performance. And the flexible-receptor virtual screening on a low-similarity dataset with 85 receptors validates the accuracy of residue flexibility comprehensive evaluation. Moreover, we studied three receptors with FDA-approved drugs, which further proved A-RFP can play a suitable role in ligand discovery. conclusion: Our analysis confirms that the screening performance of the various number of flexible residues varies wildly across receptors. It suggests that a fine-grained docking method would offset the aforementioned deficiency. Thus, we presented A-RFP, an adaptive pocket residue flexibility prediction method based on homologous information. Without considering computational resources and time costs, A-RFP provides the optimal docking result.
{"title":"A-RFP: An Adaptive Residue Flexibility Prediction Method Improving Protein-ligand Docking Based on Homologous Proteins","authors":"Chuqi Lei, Senbiao Fang, Yaohang Li, Fei Guo, Min Li","doi":"10.2174/0115748936258790240101062642","DOIUrl":"https://doi.org/10.2174/0115748936258790240101062642","url":null,"abstract":"background: computational molecular docking plays an important role in determining the precise receptor-ligand conformation, which becomes a powerful tool for drug discovery. In the past 30 years, most computational docking methods treat the receptor structure as a rigid body, although flexible docking often yields higher accuracy. The main disadvantage of flexible docking is its significantly higher computational cost. Due to the fact that different protein pock-et residues exhibit different degrees of flexibility, semi-flexible docking methods, balancing rigid docking and flexible docking, have demonstrated success in predicting highly accurate conformations with a relatively low computational cost. method: In our study, the number of flexible pocket residues was assessed by quantitative analysis, and a novel adaptive residue flexibility prediction method, named A-RFP, was proposed to improve the docking performance. Based on the homologous information, a joint strategy is used to predict the pocket residue flexibility by combining RMSD, the distance between the residue sidechain and the ligand, and the sidechain orientation. For each receptor-ligand pair, A-RFP provides a docking conformation with the optimal affinity. result: By analyzing the docking affinities of 3507 target-ligand pairs in 5 different values ranging from 0 to 10, we found there is a general trend that the larger number of flexible residues inevitably improves the docking results by using Autodock Vina. However, a certain number of counterexamples still exist. To validate the effectiveness of A-RFP, the experimental assessment was tested in a small-scale virtual screening on 5 proteins, which confirmed that A-RFP could enhance the docking performance. And the flexible-receptor virtual screening on a low-similarity dataset with 85 receptors validates the accuracy of residue flexibility comprehensive evaluation. Moreover, we studied three receptors with FDA-approved drugs, which further proved A-RFP can play a suitable role in ligand discovery. conclusion: Our analysis confirms that the screening performance of the various number of flexible residues varies wildly across receptors. It suggests that a fine-grained docking method would offset the aforementioned deficiency. Thus, we presented A-RFP, an adaptive pocket residue flexibility prediction method based on homologous information. Without considering computational resources and time costs, A-RFP provides the optimal docking result.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"93 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139926820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-09DOI: 10.2174/0115748936285540240116065719
Jia Zheng, Yetong Zhou
Background: The chemical modification of RNA plays a crucial role in many biological processes. N7-methylguanosine (m7G), being one of the most important epigenetic modifications, plays an important role in gene expression, processing metabolism, and protein synthesis. Detecting the exact location of m7G sites in the transcriptome is key to understanding their relevant mechanism in gene expression. On the basis of experimentally validated data, several machine learning or deep learning tools have been designed to identify internal m7G sites and have shown advantages over traditional experimental methods in terms of speed, cost-effectiveness and robustness. Aims: In this study, we aim to develop a computational model to help predict the exact location of m7G sites in humans. Objective: Simple and advanced encoding methods and deep learning networks are designed to achieve excellent m7G prediction efficiently. Methods: Three types of feature extractions and six classification algorithms were tested to identify m7G sites. Our final model, named Sia-m7G, adopts one-hot encoding and a delicate Siamese neural network with an attention mechanism. In addition, multiple 10-fold cross-validation tests were conducted to evaluate our predictor. Results: Sia-m7G achieved the highest sensitivity, specificity and accuracy on 10-fold crossvalidation tests compared with the other six m7G predictors. Nucleotide preference and model visualization analyses were conducted to strengthen the interpretability of Sia-m7G and provide a further understanding of m7G site fragments in genomic sequences. Conclusion: Sia-m7G has significant advantages over other classifiers and predictors, which proves the superiority of the Siamese neural network algorithm in identifying m7G sites.
{"title":"Sia-m7G: Predicting m7G Sites through the Siamese Neural Network with an Attention Mechanism","authors":"Jia Zheng, Yetong Zhou","doi":"10.2174/0115748936285540240116065719","DOIUrl":"https://doi.org/10.2174/0115748936285540240116065719","url":null,"abstract":"Background: The chemical modification of RNA plays a crucial role in many biological processes. N7-methylguanosine (m7G), being one of the most important epigenetic modifications, plays an important role in gene expression, processing metabolism, and protein synthesis. Detecting the exact location of m7G sites in the transcriptome is key to understanding their relevant mechanism in gene expression. On the basis of experimentally validated data, several machine learning or deep learning tools have been designed to identify internal m7G sites and have shown advantages over traditional experimental methods in terms of speed, cost-effectiveness and robustness. Aims: In this study, we aim to develop a computational model to help predict the exact location of m7G sites in humans. Objective: Simple and advanced encoding methods and deep learning networks are designed to achieve excellent m7G prediction efficiently. Methods: Three types of feature extractions and six classification algorithms were tested to identify m7G sites. Our final model, named Sia-m7G, adopts one-hot encoding and a delicate Siamese neural network with an attention mechanism. In addition, multiple 10-fold cross-validation tests were conducted to evaluate our predictor. Results: Sia-m7G achieved the highest sensitivity, specificity and accuracy on 10-fold crossvalidation tests compared with the other six m7G predictors. Nucleotide preference and model visualization analyses were conducted to strengthen the interpretability of Sia-m7G and provide a further understanding of m7G site fragments in genomic sequences. Conclusion: Sia-m7G has significant advantages over other classifiers and predictors, which proves the superiority of the Siamese neural network algorithm in identifying m7G sites.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"12 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: When using clinical data for multi-omics analysis, there are issues such as the insufficient number of omics data types and relatively small sample size due to the protection of patients' privacy, the requirements of data management by various institutions, and the relatively large number of features of each omics data. This paper describes the analysis of multi-omics pathway relationships using statistical data in the absence of clinical data. Methods: We proposed a novel approach to exploit easily accessible statistics in public databases. This approach introduces phenotypic associations that are not included in the clinical data and uses these data to build a three-layer heterogeneous network. To simplify the analysis, we decomposed the three-layer network into double two-layer networks to predict the weights of the inter-layer associations. By adding a hyperparameter β, the weights of the two layers of the network were merged, and then k-fold cross-validation was used to evaluate the accuracy of this method. In calculating the weights of the two-layer networks, the RWR with fixed restart probability was combined with PBMDA and CIPHER to generate the PCRWR with biased weights and improved accuracy. Results: The area under the receiver operating characteristic curve was increased by approximately 7% in the case of the RWR with initial weights. Conclusion: Multi-omics statistical data were used to establish genotype and phenotype correlation networks for analysis, which was similar to the effect of clinical multi-omics analysis.
{"title":"Genotype and Phenotype Association Analysis Based on Multi-omics Statistical Data","authors":"Xinpeng Guo, Yafei Song, Dongyan Xu, Xueping Jin, Xuequn Shang","doi":"10.2174/0115748936276861240109045208","DOIUrl":"https://doi.org/10.2174/0115748936276861240109045208","url":null,"abstract":"Background: When using clinical data for multi-omics analysis, there are issues such as the insufficient number of omics data types and relatively small sample size due to the protection of patients' privacy, the requirements of data management by various institutions, and the relatively large number of features of each omics data. This paper describes the analysis of multi-omics pathway relationships using statistical data in the absence of clinical data. Methods: We proposed a novel approach to exploit easily accessible statistics in public databases. This approach introduces phenotypic associations that are not included in the clinical data and uses these data to build a three-layer heterogeneous network. To simplify the analysis, we decomposed the three-layer network into double two-layer networks to predict the weights of the inter-layer associations. By adding a hyperparameter β, the weights of the two layers of the network were merged, and then k-fold cross-validation was used to evaluate the accuracy of this method. In calculating the weights of the two-layer networks, the RWR with fixed restart probability was combined with PBMDA and CIPHER to generate the PCRWR with biased weights and improved accuracy. Results: The area under the receiver operating characteristic curve was increased by approximately 7% in the case of the RWR with initial weights. Conclusion: Multi-omics statistical data were used to establish genotype and phenotype correlation networks for analysis, which was similar to the effect of clinical multi-omics analysis.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"12 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139760088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.2174/0115748936288453240124082031
Yuanyuan He, Haodong Wei, Siqing Liao, Ruiming Ou, Yuqiang Xiong, Yongchun Zuo, Lei Yang
Background: Bladder cancer is a prevalent malignancy globally, characterized by rising incidence and mortality rates. Stratifying bladder cancer patients into different subtypes is crucial for the effective treatment of this form of cancer. Therefore, there is a need to develop a stratification model specific to bladder cancer. Purpose: This study aims to establish a prognostic prediction model for bladder cancer, with the primary goal of accurately predicting prognosis and treatment outcomes. objective: This study aims to establish a prognostic prediction model for bladder cancer, with the primary goal of accurately predicting prognosis and treatment outcomes. Methods: We collected datasets from 10 bladder cancer samples sourced from the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA) databases, and IMvigor210 dataset. The machine learning based algorithms were used to generate 96 models for establishing the risk score for each patient. Based on the risk score, all the patients was classified into two different risk score groups. Results: The two groups of bladder cancer patients exhibited significant differences in prognosis, biological functions, and drug sensitivity. Nomogram model demonstrated that the risk score had a robust predictive effect with good clinical utility. Conclusion: The risk score constructed in this study can be utilized to predict the prognosis, response to drug treatment, and immunotherapy of bladder cancer patients, providing assistance for personalized clinical treatment of bladder cancer. other: None
{"title":"Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer","authors":"Yuanyuan He, Haodong Wei, Siqing Liao, Ruiming Ou, Yuqiang Xiong, Yongchun Zuo, Lei Yang","doi":"10.2174/0115748936288453240124082031","DOIUrl":"https://doi.org/10.2174/0115748936288453240124082031","url":null,"abstract":"Background: Bladder cancer is a prevalent malignancy globally, characterized by rising incidence and mortality rates. Stratifying bladder cancer patients into different subtypes is crucial for the effective treatment of this form of cancer. Therefore, there is a need to develop a stratification model specific to bladder cancer. Purpose: This study aims to establish a prognostic prediction model for bladder cancer, with the primary goal of accurately predicting prognosis and treatment outcomes. objective: This study aims to establish a prognostic prediction model for bladder cancer, with the primary goal of accurately predicting prognosis and treatment outcomes. Methods: We collected datasets from 10 bladder cancer samples sourced from the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA) databases, and IMvigor210 dataset. The machine learning based algorithms were used to generate 96 models for establishing the risk score for each patient. Based on the risk score, all the patients was classified into two different risk score groups. Results: The two groups of bladder cancer patients exhibited significant differences in prognosis, biological functions, and drug sensitivity. Nomogram model demonstrated that the risk score had a robust predictive effect with good clinical utility. Conclusion: The risk score constructed in this study can be utilized to predict the prognosis, response to drug treatment, and immunotherapy of bladder cancer patients, providing assistance for personalized clinical treatment of bladder cancer. other: None","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"58 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139760199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Conventional approaches to drug discovery are often characterized by lengthy and costly processes. To expedite the discovery of new drugs, the integration of artificial intelligence (AI) in predicting drug-target binding affinity (DTA) has emerged as a crucial approach. Despite the proliferation of deep learning methods for DTA prediction, many of these methods primarily concentrate on the amino acid sequence of proteins. Yet, the interactions between drug compounds and targets occur within distinct segments within the protein structures, whereas the primary sequence primarily captures global protein features. Consequently, it falls short of fully elucidating the intricate relationship between drugs and their respective targets. Objective: This study aims to employ advanced deep-learning techniques to forecast DTA while incorporating information about the secondary structure of proteins. Methods: In our research, both the primary sequence of protein and the secondary structure of protein were leveraged for protein representation. While the primary sequence played the role of the overarching feature, the secondary structure was employed as the localized feature. Convolutional neural networks and graph neural networks were utilized to independently model the intricate features of target proteins and drug compounds. This approach enhanced our ability to capture drugtarget interactions more effectively Results: We have introduced a novel method for predicting DTA. In comparison to DeepDTA, our approach demonstrates significant enhancements, achieving a 3.9% increase in the Concordance Index (CI) and a remarkable 34% reduction in Mean Squared Error (MSE) when evaluated on the KIBA dataset. Conclusion: In conclusion, our results unequivocally demonstrate that augmenting DTA prediction with the inclusion of the protein's secondary structure as a localized feature yields significantly improved accuracy compared to relying solely on the primary structure.
{"title":"Enhancing Drug-Target Binding Affinity Prediction through Deep Learning and Protein Secondary Structure Integration","authors":"Runhua Zhang, Baozhong Zhu, Tengsheng Jiang, Zhiming Cui, Hongjie Wu","doi":"10.2174/0115748936285519240110070209","DOIUrl":"https://doi.org/10.2174/0115748936285519240110070209","url":null,"abstract":"Background: Conventional approaches to drug discovery are often characterized by lengthy and costly processes. To expedite the discovery of new drugs, the integration of artificial intelligence (AI) in predicting drug-target binding affinity (DTA) has emerged as a crucial approach. Despite the proliferation of deep learning methods for DTA prediction, many of these methods primarily concentrate on the amino acid sequence of proteins. Yet, the interactions between drug compounds and targets occur within distinct segments within the protein structures, whereas the primary sequence primarily captures global protein features. Consequently, it falls short of fully elucidating the intricate relationship between drugs and their respective targets. Objective: This study aims to employ advanced deep-learning techniques to forecast DTA while incorporating information about the secondary structure of proteins. Methods: In our research, both the primary sequence of protein and the secondary structure of protein were leveraged for protein representation. While the primary sequence played the role of the overarching feature, the secondary structure was employed as the localized feature. Convolutional neural networks and graph neural networks were utilized to independently model the intricate features of target proteins and drug compounds. This approach enhanced our ability to capture drugtarget interactions more effectively Results: We have introduced a novel method for predicting DTA. In comparison to DeepDTA, our approach demonstrates significant enhancements, achieving a 3.9% increase in the Concordance Index (CI) and a remarkable 34% reduction in Mean Squared Error (MSE) when evaluated on the KIBA dataset. Conclusion: In conclusion, our results unequivocally demonstrate that augmenting DTA prediction with the inclusion of the protein's secondary structure as a localized feature yields significantly improved accuracy compared to relying solely on the primary structure.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"24 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-04DOI: 10.2174/0115748936282613231211112920
Dayu Tan, Jing Wang, Zhaolong Cheng, Yansen Su, Chunhou Zheng
Objective: This work aims to infer causal relationships between genes and construct dynamic gene regulatory networks using time-course scRNA-seq data. Methods: We propose an analytical method for inferring GRNs from single-cell time-course data based on temporal convolutional networks (scTGRN), which provides a supervised learning approach to infer causal relationships among genes. scTGRN constructs a 4D tensor representing gene expression features for each gene pair, then inputs the constructed 4D tensor into the temporal convolutional network to train and infer the causal relationship between genes. Results: We validate the performance of scTGRN on five real datasets and four simulated datasets, and the experimental results show that scTGRN outperforms existing models in constructing GRNs. In addition, we test the performance of scTGRN on gene function assignment, and scTGRN outperforms other models. Conclusion: The analysis shows that scTGRN can not only accurately identify the causal relationship between genes, but also can be used to achieve gene function assignment.
{"title":"Inferring Gene Regulatory Networks from Single-Cell Time-Course Data Based on Temporal Convolutional Networks","authors":"Dayu Tan, Jing Wang, Zhaolong Cheng, Yansen Su, Chunhou Zheng","doi":"10.2174/0115748936282613231211112920","DOIUrl":"https://doi.org/10.2174/0115748936282613231211112920","url":null,"abstract":"Objective: This work aims to infer causal relationships between genes and construct dynamic gene regulatory networks using time-course scRNA-seq data. Methods: We propose an analytical method for inferring GRNs from single-cell time-course data based on temporal convolutional networks (scTGRN), which provides a supervised learning approach to infer causal relationships among genes. scTGRN constructs a 4D tensor representing gene expression features for each gene pair, then inputs the constructed 4D tensor into the temporal convolutional network to train and infer the causal relationship between genes. Results: We validate the performance of scTGRN on five real datasets and four simulated datasets, and the experimental results show that scTGRN outperforms existing models in constructing GRNs. In addition, we test the performance of scTGRN on gene function assignment, and scTGRN outperforms other models. Conclusion: The analysis shows that scTGRN can not only accurately identify the causal relationship between genes, but also can be used to achieve gene function assignment.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"40 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139689088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: With increasing rates of polypharmacy, the vigilant surveillance of clinical drug toxicity has emerged as an important concern. Named Entity Recognition (NER) stands as an indispensable undertaking, essential for the extraction of valuable insights regarding drug safety from the biomedical literature. In recent years, significant advancements have been achieved in the deep learning models on NER tasks. Nonetheless, the effectiveness of these NER techniques relies on the availability of substantial volumes of annotated data, which is labor-intensive and inefficient. background: With increasing rates of polypharmacy, clinical drug toxicity has been closely monitored. Named Entity Recognition (NER) is a vital task for extracting valuable drug safety information from biomedical literature. Recently, many deep learning models in biomedical domain have made great progress for NER, especially pre-trained language models. However, these NER methods require large amounts of high-quality manually annotated data with named entities, which is labor intensive and inefficient. Methods: This study introduces a novel approach that diverges from the conventional reliance on manually annotated data. It employs a transformer-based technique known as Positive-Unlabeled Learning (PULearning), which incorporates adaptive learning and is applied to the clinical cancer drug toxicity corpus. To improve the precision of prediction, we employ relative position embeddings within the transformer encoder. Additionally, we formulate a composite loss function that integrates two Kullback-Leibler (KL) regularizers to align with PULearning assumptions. The outcomes demonstrate that our approach attains the targeted performance for NER tasks, solely relying on unlabeled data and named entity dictionaries. objective: To improve the performance of prediction Conclusion: Our model achieves an overall NER performance with an F1 of 0.819. Specifically, it attains F1 of 0.841, 0.801 and 0.815 for DRUG, CANCER, and TOXI entities, respectively. A comprehensive analysis of the results validates the effectiveness of our approach in comparison to existing PULearning methods on biomedical NER tasks. Additionally, a visualization of the associations among three identified entities is provided, offering a valuable reference for querying their interrelationships. method: In this work, instead of relying on the manually labeled data, a transformer-based Positive-Unlabeled Learning (PULearning) is proposed with adaptive learning and applied on the clinical cancer drug toxicity corpus. To improve the precision of prediction, relative position embeddings are used in transformer encoder. And then, a mixed loss is designed with two Kullback-Leibler (KL) regularizers for PULearning assumptions. Through adaptive sampling, our approach meets the expected performance for NER task only using unlabeled data and named entity dictionaries. result: The overall NER performance of our model obtains 0
背景：随着多种药物使用率的增加，对临床药物毒性的警惕性监测已成为一个重要的关注点。命名实体识别（NER）是一项不可或缺的工作，对于从生物医学文献中提取有关药物安全性的宝贵见解至关重要。近年来，深度学习模型在 NER 任务方面取得了重大进展。然而，这些核糖核酸（NER）技术的有效性依赖于大量注释数据的可用性，而这是一项劳动密集型且效率低下的工作：随着多种药物的使用率越来越高，临床药物毒性受到了密切关注。命名实体识别（NER）是从生物医学文献中提取有价值的药物安全性信息的重要任务。最近，生物医学领域的许多深度学习模型在 NER 方面取得了很大进展，尤其是预训练语言模型。然而，这些 NER 方法需要大量高质量的人工标注命名实体的数据，劳动强度大且效率低。方法本研究引入了一种新方法，与传统的依赖人工标注数据的方法不同。它采用了一种被称为正向无标注学习（PULearning）的基于转换器的技术，该技术结合了自适应学习，并应用于临床癌症药物毒性语料库。为了提高预测精度，我们在变换器编码器中采用了相对位置嵌入技术。此外，我们还制定了一个复合损失函数，其中整合了两个库尔巴克-莱伯勒（KL）正则，以符合 PULearning 假设。结果表明，我们的方法仅依靠未标注数据和命名实体字典就能实现 NER 任务的目标性能：提高预测性能 结论：我们的模型实现了整体 NER 性能的提高：我们的模型实现了整体 NER 性能，F1 为 0.819。具体来说，它对 DRUG、CANCER 和 TOXI 实体的 F1 分别为 0.841、0.801 和 0.815。对结果的综合分析验证了我们的方法与现有的 PULearning 方法相比在生物医学 NER 任务中的有效性。此外，我们还提供了三个已识别实体之间关联的可视化方法，为查询它们之间的相互关系提供了有价值的参考：在这项工作中，我们提出了一种基于转换器的正向无标注学习（PULearning）方法，并将其应用于临床癌症药物毒性语料库。为了提高预测精度，在变换器编码器中使用了相对位置嵌入。然后，针对 PULearning 假设，设计了带有两个 Kullback-Leibler (KL) 正则的混合损失。通过自适应采样，我们的方法仅在使用未标记数据和命名实体词典的情况下就达到了 NER 任务的预期性能：我们模型的总体 NER 性能获得了 0.819 的 F1 分数，而在 DRUG、CANCER 和 TOXI 上的 F1 分数分别为 0.841、0.801 和 0.815：无
{"title":"Transformer-based Named Entity Recognition for Clinical Cancer Drug Toxicity by Positive-unlabeled Learning and KL Regularizers","authors":"Weixin Xie, Jiayu Xu, Chengkui Zhao, Jin Li, Shuangze Han, Tianyu Shao, Limei Wang, Weixing Feng","doi":"10.2174/0115748936278299231213045441","DOIUrl":"https://doi.org/10.2174/0115748936278299231213045441","url":null,"abstract":"Background: With increasing rates of polypharmacy, the vigilant surveillance of clinical drug toxicity has emerged as an important concern. Named Entity Recognition (NER) stands as an indispensable undertaking, essential for the extraction of valuable insights regarding drug safety from the biomedical literature. In recent years, significant advancements have been achieved in the deep learning models on NER tasks. Nonetheless, the effectiveness of these NER techniques relies on the availability of substantial volumes of annotated data, which is labor-intensive and inefficient. background: With increasing rates of polypharmacy, clinical drug toxicity has been closely monitored. Named Entity Recognition (NER) is a vital task for extracting valuable drug safety information from biomedical literature. Recently, many deep learning models in biomedical domain have made great progress for NER, especially pre-trained language models. However, these NER methods require large amounts of high-quality manually annotated data with named entities, which is labor intensive and inefficient. Methods: This study introduces a novel approach that diverges from the conventional reliance on manually annotated data. It employs a transformer-based technique known as Positive-Unlabeled Learning (PULearning), which incorporates adaptive learning and is applied to the clinical cancer drug toxicity corpus. To improve the precision of prediction, we employ relative position embeddings within the transformer encoder. Additionally, we formulate a composite loss function that integrates two Kullback-Leibler (KL) regularizers to align with PULearning assumptions. The outcomes demonstrate that our approach attains the targeted performance for NER tasks, solely relying on unlabeled data and named entity dictionaries. objective: To improve the performance of prediction Conclusion: Our model achieves an overall NER performance with an F1 of 0.819. Specifically, it attains F1 of 0.841, 0.801 and 0.815 for DRUG, CANCER, and TOXI entities, respectively. A comprehensive analysis of the results validates the effectiveness of our approach in comparison to existing PULearning methods on biomedical NER tasks. Additionally, a visualization of the associations among three identified entities is provided, offering a valuable reference for querying their interrelationships. method: In this work, instead of relying on the manually labeled data, a transformer-based Positive-Unlabeled Learning (PULearning) is proposed with adaptive learning and applied on the clinical cancer drug toxicity corpus. To improve the precision of prediction, relative position embeddings are used in transformer encoder. And then, a mixed loss is designed with two Kullback-Leibler (KL) regularizers for PULearning assumptions. Through adaptive sampling, our approach meets the expected performance for NER task only using unlabeled data and named entity dictionaries. result: The overall NER performance of our model obtains 0","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"35 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139688945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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