Pub Date : 2024-01-21DOI: 10.2174/0115748936278884240102094058
Yaxuan Cui, Leyi Wei, Ruheng Wang, Xiucai Ye, Tetsuya Sakurai
Introduction: Transcriptional gene expressions and their corresponding spatial information are critical for understanding the biological function, mutual regulation, and identification of various cell types. Materials and Methods: Recently, several computational methods have been proposed for clustering using spatial transcriptional expression. Although these algorithms have certain practicability, they cannot utilize spatial information effectively and are highly sensitive to noise and outliers. In this study, we propose ACSpot, an autoencoder-based fuzzy clustering algorithm, as a solution to tackle these problems. Specifically, we employed a self-supervised autoencoder to reduce feature dimensionality, mitigate nonlinear noise, and learn high-quality representations. Additionally, a commonly used clustering method, Fuzzy c-means, is used to achieve improved clustering results. In particular, we utilize spatial neighbor information to optimize the clustering process and to fine-tune each spot to its associated cluster category using probabilistic and statistical methods. Result and Discussion: The comparative analysis on the 10x Visium human dorsolateral prefrontal cortex (DLPFC) dataset demonstrates that ACSpot outperforms other clustering algorithms. Subsequently, spatially variable genes were identified based on the clustering outcomes, revealing a striking similarity between their spatial distribution and the subcluster spatial distribution from the clustering results. Notably, these spatially variable genes include APP, PSEN1, APOE, SORL1, BIN1, and PICALM, all of which are well-known Alzheimer's disease-associated genes. Conclusion: In addition, we applied our model to explore some potential Alzheimer's disease correlated genes within the dataset and performed Gene Ontology (GO) enrichment and gene-pathway analyses for validation, illustrating the capability of our model to pinpoint genes linked to Alzheimer’s disease.
{"title":"Identification of Spatial Domains, Spatially Variable Genes, and Genetic Association Studies of Alzheimer Disease with an Autoencoder-based Fuzzy Clustering Algorithm","authors":"Yaxuan Cui, Leyi Wei, Ruheng Wang, Xiucai Ye, Tetsuya Sakurai","doi":"10.2174/0115748936278884240102094058","DOIUrl":"https://doi.org/10.2174/0115748936278884240102094058","url":null,"abstract":"Introduction: Transcriptional gene expressions and their corresponding spatial information are critical for understanding the biological function, mutual regulation, and identification of various cell types. Materials and Methods: Recently, several computational methods have been proposed for clustering using spatial transcriptional expression. Although these algorithms have certain practicability, they cannot utilize spatial information effectively and are highly sensitive to noise and outliers. In this study, we propose ACSpot, an autoencoder-based fuzzy clustering algorithm, as a solution to tackle these problems. Specifically, we employed a self-supervised autoencoder to reduce feature dimensionality, mitigate nonlinear noise, and learn high-quality representations. Additionally, a commonly used clustering method, Fuzzy c-means, is used to achieve improved clustering results. In particular, we utilize spatial neighbor information to optimize the clustering process and to fine-tune each spot to its associated cluster category using probabilistic and statistical methods. Result and Discussion: The comparative analysis on the 10x Visium human dorsolateral prefrontal cortex (DLPFC) dataset demonstrates that ACSpot outperforms other clustering algorithms. Subsequently, spatially variable genes were identified based on the clustering outcomes, revealing a striking similarity between their spatial distribution and the subcluster spatial distribution from the clustering results. Notably, these spatially variable genes include APP, PSEN1, APOE, SORL1, BIN1, and PICALM, all of which are well-known Alzheimer's disease-associated genes. Conclusion: In addition, we applied our model to explore some potential Alzheimer's disease correlated genes within the dataset and performed Gene Ontology (GO) enrichment and gene-pathway analyses for validation, illustrating the capability of our model to pinpoint genes linked to Alzheimer’s disease.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"240 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139518523","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 : 2024-01-05DOI: 10.2174/0115748936266722231116050255
Xiao-Yan Zeng, Min Zhang, Si-Jing Liao, Yong Wang, Ying-Bo Ren, Run Li, Tian-Mei Li, An-Qiong Mao, Guang-Zhen Li, Ying Zhang
Background: Numerous studies have shown that mitochondrial damage induces inflammation and activates inflammatory cells, leading to sepsis, while sepsis, a systemic inflammatory response syndrome, also exacerbates mitochondrial damage and hyperactivation. Mitochondrial autophagy eliminates aged, abnormal or damaged mitochondria to reduce intracellular mitochondrial stress and the release of mitochondria-associated molecules, thereby reducing the inflammatory response and cellular damage caused by sepsis. In addition, mitochondrial autophagy may also influence the onset and progression of sepsis, but the exact mechanisms are unclear. background: Sepsis is a critical systemic infection, a syndrome of severe inflammatory response of the organism to various pathogenic microorganisms. Methods: In this study, we mined the available publicly available microarray data in the GEO database (Home - GEO - NCBI (nih.gov)) with the aim of identifying key genes associated with mitochondrial autophagy in sepsis. objective: In this study, we used a bioinformatics approach to integrate multiple microarray data to screen for mitochondrial autophagy-related hub genes associated with sepsis onset and progression in a more scientific and systematic manner. Results: We identified four mitophagy-related genes in sepsis, TOMM20, TOMM22, TOMM40, and MFN1. method: Robust rank aggregation (RRA) Conclusion: This study provides preliminary evidence for the treatment of sepsis and may provide a solid foundation for subsequent biological studies. result: we constructed a PPI network combined with RRA analysis method to finally identify 4 key genes, namely TOMM20, TOMM22, TOMM40, and MFN1. conclusion: In this study, we used a bioinformatics analysis method, RRA, to integrate five gene microarray datasets to identify pivotal genes associated with mitochondrial autophagy in sepsis. Gene ontology (GO) functional annotation results show that these hub genes are mainly enriched in mitochondrial transport and establishment of protein localization to mitochondrion. Finally, we constructed the PPI network with the top 100 genes obtained from the rra method analysis. Based on the RRA results, the PPI results and the mitochondrial autophagy-related genes we found in the Reactome Pathway Database, we finally identified four key genes as TOMM20, TOMM22, TOMM40, and MFN1, respectively.
{"title":"Identification of Mitophagy-Related Genes in Sepsis","authors":"Xiao-Yan Zeng, Min Zhang, Si-Jing Liao, Yong Wang, Ying-Bo Ren, Run Li, Tian-Mei Li, An-Qiong Mao, Guang-Zhen Li, Ying Zhang","doi":"10.2174/0115748936266722231116050255","DOIUrl":"https://doi.org/10.2174/0115748936266722231116050255","url":null,"abstract":"Background: Numerous studies have shown that mitochondrial damage induces inflammation and activates inflammatory cells, leading to sepsis, while sepsis, a systemic inflammatory response syndrome, also exacerbates mitochondrial damage and hyperactivation. Mitochondrial autophagy eliminates aged, abnormal or damaged mitochondria to reduce intracellular mitochondrial stress and the release of mitochondria-associated molecules, thereby reducing the inflammatory response and cellular damage caused by sepsis. In addition, mitochondrial autophagy may also influence the onset and progression of sepsis, but the exact mechanisms are unclear. background: Sepsis is a critical systemic infection, a syndrome of severe inflammatory response of the organism to various pathogenic microorganisms. Methods: In this study, we mined the available publicly available microarray data in the GEO database (Home - GEO - NCBI (nih.gov)) with the aim of identifying key genes associated with mitochondrial autophagy in sepsis. objective: In this study, we used a bioinformatics approach to integrate multiple microarray data to screen for mitochondrial autophagy-related hub genes associated with sepsis onset and progression in a more scientific and systematic manner. Results: We identified four mitophagy-related genes in sepsis, TOMM20, TOMM22, TOMM40, and MFN1. method: Robust rank aggregation (RRA) Conclusion: This study provides preliminary evidence for the treatment of sepsis and may provide a solid foundation for subsequent biological studies. result: we constructed a PPI network combined with RRA analysis method to finally identify 4 key genes, namely TOMM20, TOMM22, TOMM40, and MFN1. conclusion: In this study, we used a bioinformatics analysis method, RRA, to integrate five gene microarray datasets to identify pivotal genes associated with mitochondrial autophagy in sepsis. Gene ontology (GO) functional annotation results show that these hub genes are mainly enriched in mitochondrial transport and establishment of protein localization to mitochondrion. Finally, we constructed the PPI network with the top 100 genes obtained from the rra method analysis. Based on the RRA results, the PPI results and the mitochondrial autophagy-related genes we found in the Reactome Pathway Database, we finally identified four key genes as TOMM20, TOMM22, TOMM40, and MFN1, respectively.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"21 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139376298","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 : 2023-12-15DOI: 10.2174/0115748936269106231025064143
Harris Song, Nan Sun, Wenping Yu, Stephen Yau
Background: This study addresses the need for analyzing viral genome sequences and understanding their genetic relationships. The focus is on introducing a novel natural graph approach as a solution. Objective: The objective of this study is to demonstrate the effectiveness and advantages of the proposed natural graph approach in clustering viral genome sequences into distinct clades, subtypes, or districts. Additionally, the aim is to explore its interpretability, potential applications, and implications for pandemic control and public health interventions. Methods: The study utilizes the proposed natural graph algorithm to cluster viral genome sequences. The results are compared with existing methods and multidimensional scaling to evaluate the performance and effectiveness of the approach. Results: The natural graph approach successfully clusters viral genome sequences, providing valuable insights into viral evolution and transmission dynamics. The ability to generate directed connections between nodes enhances the interpretability of the results, facilitating the investigation of transmission pathways and viral fitness. Conclusion: The findings highlight the potential applications of the natural graph algorithm in pandemic control, transmission tracing, and vaccine design. Future research directions may involve scaling up the analysis to larger datasets and incorporating additional genetic features for improved resolution. The natural graph approach presents a promising tool for viral genomics research with implications for public health interventions.
{"title":"A Novel Natural Graph for Efficient Clustering of Virus Genome Sequences","authors":"Harris Song, Nan Sun, Wenping Yu, Stephen Yau","doi":"10.2174/0115748936269106231025064143","DOIUrl":"https://doi.org/10.2174/0115748936269106231025064143","url":null,"abstract":"Background: This study addresses the need for analyzing viral genome sequences and understanding their genetic relationships. The focus is on introducing a novel natural graph approach as a solution. Objective: The objective of this study is to demonstrate the effectiveness and advantages of the proposed natural graph approach in clustering viral genome sequences into distinct clades, subtypes, or districts. Additionally, the aim is to explore its interpretability, potential applications, and implications for pandemic control and public health interventions. Methods: The study utilizes the proposed natural graph algorithm to cluster viral genome sequences. The results are compared with existing methods and multidimensional scaling to evaluate the performance and effectiveness of the approach. Results: The natural graph approach successfully clusters viral genome sequences, providing valuable insights into viral evolution and transmission dynamics. The ability to generate directed connections between nodes enhances the interpretability of the results, facilitating the investigation of transmission pathways and viral fitness. Conclusion: The findings highlight the potential applications of the natural graph algorithm in pandemic control, transmission tracing, and vaccine design. Future research directions may involve scaling up the analysis to larger datasets and incorporating additional genetic features for improved resolution. The natural graph approach presents a promising tool for viral genomics research with implications for public health interventions.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"33 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138687256","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}
Aims: Bulk and single-cell RNA sequencing data were analyzed to explore the association of stemness phenotype with dysfunctional anti-tumor immunity and its impact on clinical outcomes of primary and relapse HCC. Background: The stemness phenotype is gradually acquired during cancer progression; however, it remains unclear the effect of stemness phenotype on recurrence and clinical outcomes in hepatocellular carcinoma (HCC). Methods: The stemness index (mRNAsi) calculated by a one-class logistic regression algorithm in multiple HCC cohorts was defined as the stemness phenotype of the patient. Using single-cell profiling in primary or early-relapse HCC, cell stemness phenotypes were evaluated by developmental potential. Differential analysis of stemness phenotype, gene expression and interactions between primary and recurrent samples revealed the underlying immune evasion mechanisms. Results: A significant mRNAsi association with HCC patient clinical outcomes was found. The high and low mRNAsi groups had distinct tumor immune microenvironments. Cellular stemness phenotype varied by cell type. Moreover, compared with primary tumors, early-relapse tumors had increased stemness of dendritic cells and tumor cells and reduced stemness of T cells and B cells. Moreover, in relapse tumors, CD8+ T cells displayed a low stemness state, with a high exhausted state, unlike the high stemness state observed in primary HCC. Conclusions: The comprehensive characterization of the HCC stemness phenotype provides insights into the clinical outcomes and immune escape mechanisms associated with recurrence.
{"title":"Integrating Single-cell and Bulk RNA Sequencing Reveals Stemness Phenotype Associated with Clinical Outcomes and Potential Immune Evasion Mechanisms in Hepatocellular Carcinoma","authors":"Xiaojing Zhu, Jiaxing Zhang, Zixin Zhang, Hongyan Yuan, Aimin xie, Nan Zhang, Mingwei wang, Minghui jiang, Yanqi Xiao, Hao Wang, Xing Wang, Yan Xu","doi":"10.2174/0115748936268168231114103440","DOIUrl":"https://doi.org/10.2174/0115748936268168231114103440","url":null,"abstract":"Aims: Bulk and single-cell RNA sequencing data were analyzed to explore the association of stemness phenotype with dysfunctional anti-tumor immunity and its impact on clinical outcomes of primary and relapse HCC. Background: The stemness phenotype is gradually acquired during cancer progression; however, it remains unclear the effect of stemness phenotype on recurrence and clinical outcomes in hepatocellular carcinoma (HCC). Methods: The stemness index (mRNAsi) calculated by a one-class logistic regression algorithm in multiple HCC cohorts was defined as the stemness phenotype of the patient. Using single-cell profiling in primary or early-relapse HCC, cell stemness phenotypes were evaluated by developmental potential. Differential analysis of stemness phenotype, gene expression and interactions between primary and recurrent samples revealed the underlying immune evasion mechanisms. Results: A significant mRNAsi association with HCC patient clinical outcomes was found. The high and low mRNAsi groups had distinct tumor immune microenvironments. Cellular stemness phenotype varied by cell type. Moreover, compared with primary tumors, early-relapse tumors had increased stemness of dendritic cells and tumor cells and reduced stemness of T cells and B cells. Moreover, in relapse tumors, CD8+ T cells displayed a low stemness state, with a high exhausted state, unlike the high stemness state observed in primary HCC. Conclusions: The comprehensive characterization of the HCC stemness phenotype provides insights into the clinical outcomes and immune escape mechanisms associated with recurrence.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"10 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138686838","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 : 2023-12-07DOI: 10.2174/0115748936274103231114105340
Farnoosh Barneh, Ahmad Nazarian, Rezvan Mousavi-nadushan, Kamran Pooshang Bagheri
Background:: Antibacterial resistance has been one of the most important causes of death in the last few decades, necessitating the need to discover new antibiotics. Antimicrobial peptides (AMPs) are among the best candidates due to their broad-spectrum and potent activity against bacteria and low probability of developing resistance against them. Objective:: In this study, we proposed a novel filtration method using knowledge-based approaches to discover encrypted AMPs within a protein sequence Methods:: The encrypted AMPs were selected from a protein sequence, in this case, lactoferrin, based on hydrophobicity, cationicity, alpha-helix structure, helical wheel projection, and binding affinities to gram-negative and positive bacterial membranes. Results:: Six out of 20 potential encrypted AMPs were ultimately selected for further assays. Molecular docking of the selected AMPs with outer and inner membranes of gram-negative bacteria and also gram-positive bacterial membranes showed reasonable binding affinity ranging from ‘-6.7 to -7.5’ and ‘- 4.5 to -5.7’ and ‘-4.6 to -5.7’ kcal/mol, respectively. No toxicity was shown in the candidate AMPs. Conclusion:: According to in silico results, our method succeeded to discover six new encrypted AMPs from human lactoferrin, designated as lactoferrin-derived peptides (LDPs). Further in silico and experimental assays should also be performed to prove the efficiency of our knowledge-based filtration method.
{"title":"A Novel In silico Filtration Method for Discovery of Encrypted Antimicrobial Peptides","authors":"Farnoosh Barneh, Ahmad Nazarian, Rezvan Mousavi-nadushan, Kamran Pooshang Bagheri","doi":"10.2174/0115748936274103231114105340","DOIUrl":"https://doi.org/10.2174/0115748936274103231114105340","url":null,"abstract":"Background:: Antibacterial resistance has been one of the most important causes of death in the last few decades, necessitating the need to discover new antibiotics. Antimicrobial peptides (AMPs) are among the best candidates due to their broad-spectrum and potent activity against bacteria and low probability of developing resistance against them. Objective:: In this study, we proposed a novel filtration method using knowledge-based approaches to discover encrypted AMPs within a protein sequence Methods:: The encrypted AMPs were selected from a protein sequence, in this case, lactoferrin, based on hydrophobicity, cationicity, alpha-helix structure, helical wheel projection, and binding affinities to gram-negative and positive bacterial membranes. Results:: Six out of 20 potential encrypted AMPs were ultimately selected for further assays. Molecular docking of the selected AMPs with outer and inner membranes of gram-negative bacteria and also gram-positive bacterial membranes showed reasonable binding affinity ranging from ‘-6.7 to -7.5’ and ‘- 4.5 to -5.7’ and ‘-4.6 to -5.7’ kcal/mol, respectively. No toxicity was shown in the candidate AMPs. Conclusion:: According to in silico results, our method succeeded to discover six new encrypted AMPs from human lactoferrin, designated as lactoferrin-derived peptides (LDPs). Further in silico and experimental assays should also be performed to prove the efficiency of our knowledge-based filtration method.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"11 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138555680","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 new variants of COVID-19 causing challenges, we need to focus on integrating multiple deep-learning frameworks to develop intelligent healthcare systems for early detection and diagnosis. Objective:: This article suggests three hybrid deep learning models, namely CNN-LSTM, CNN-Bi- LSTM, and CNN-GRU, to address the pressing need for an intelligent healthcare system. These models are designed to capture spatial and temporal patterns in COVID-19 data, thereby improving the accuracy and timeliness of predictions. An output forecasting framework integrates these models, and an optimization algorithm automatically selects the hyperparameters for the 13 baselines and the three proposed hybrid models. Methods:: Real-time time series data from the five most affected countries were used to test the effectiveness of the proposed models. Baseline models were compared, and optimization algorithms were employed to improve forecasting capabilities. Results:: CNN-GRU and CNN-LSTM are the top short- and long-term forecasting models. CNNGRU had the best performance with the lowest SMAPE and MAPE values for long-term forecasting in India at 3.07% and 3.17%, respectively, and impressive results for short-term forecasting with SMAPE and MAPE values of 1.46% and 1.47%. Conclusion:: Hybrid deep learning models, like CNN-GRU, can aid in early COVID-19 assessment and diagnosis. They detect patterns in data for effective governmental strategies and forecasting. This helps manage and mitigate the pandemic faster and more accurately.
{"title":"Optimized Hybrid Deep Learning for Real-Time Pandemic Data Forecasting: Long and Short-Term Perspectives","authors":"Sujata Dash, Sourav Kumar Giri, Subhendu Kumar Pani, Saurav Mallik, Mingqiang Wang, Hong Qin","doi":"10.2174/0115748936257412231120113648","DOIUrl":"https://doi.org/10.2174/0115748936257412231120113648","url":null,"abstract":"Background:: With new variants of COVID-19 causing challenges, we need to focus on integrating multiple deep-learning frameworks to develop intelligent healthcare systems for early detection and diagnosis. Objective:: This article suggests three hybrid deep learning models, namely CNN-LSTM, CNN-Bi- LSTM, and CNN-GRU, to address the pressing need for an intelligent healthcare system. These models are designed to capture spatial and temporal patterns in COVID-19 data, thereby improving the accuracy and timeliness of predictions. An output forecasting framework integrates these models, and an optimization algorithm automatically selects the hyperparameters for the 13 baselines and the three proposed hybrid models. Methods:: Real-time time series data from the five most affected countries were used to test the effectiveness of the proposed models. Baseline models were compared, and optimization algorithms were employed to improve forecasting capabilities. Results:: CNN-GRU and CNN-LSTM are the top short- and long-term forecasting models. CNNGRU had the best performance with the lowest SMAPE and MAPE values for long-term forecasting in India at 3.07% and 3.17%, respectively, and impressive results for short-term forecasting with SMAPE and MAPE values of 1.46% and 1.47%. Conclusion:: Hybrid deep learning models, like CNN-GRU, can aid in early COVID-19 assessment and diagnosis. They detect patterns in data for effective governmental strategies and forecasting. This helps manage and mitigate the pandemic faster and more accurately.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"92 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138556751","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 : 2023-12-07DOI: 10.2174/0115748936268218231114070754
Jiyu Zhang, Tao Huang, Qiao Sun, Jian Zhang
Background:: Pathological myopia, a severe variant of myopia, extends beyond the typical refractive error associated with nearsightedness. While the condition has a strong genetic component, the intricate mechanisms of inheritance remain elusive. Some genes have been associated with the development of pathological myopia, but their exact roles are not fully understood. Objective:: This study aimed to identify novel genes associated with pathological myopia Methods:: Our study leveraged DisGeNET to identify 184 genes linked with high myopia and 39 genes related to degenerative myopia. To uncover additional pathological myopia-associated genes, we employed the random walk with restart algorithm to investigate the protein-protein interactions network. We used the previously identified 184 high myopia and 39 degenerative myopia genes as seed nodes. Results:: Through subsequent screening tests, we discarded genes with weak associations, yielding 103 new genes for high myopia and 33 for degenerative myopia. Conclusion:: We confirmed the association of certain genes, including six genes that were confirmed to be associated with both high and degenerative myopia. The newly discovered genes are helpful to uncover and understand the pathogenesis of myopia.
{"title":"Identifying Pathological Myopia Associated Genes with A Random Walk- Based Method in Protein-Protein Interaction Network","authors":"Jiyu Zhang, Tao Huang, Qiao Sun, Jian Zhang","doi":"10.2174/0115748936268218231114070754","DOIUrl":"https://doi.org/10.2174/0115748936268218231114070754","url":null,"abstract":"Background:: Pathological myopia, a severe variant of myopia, extends beyond the typical refractive error associated with nearsightedness. While the condition has a strong genetic component, the intricate mechanisms of inheritance remain elusive. Some genes have been associated with the development of pathological myopia, but their exact roles are not fully understood. Objective:: This study aimed to identify novel genes associated with pathological myopia Methods:: Our study leveraged DisGeNET to identify 184 genes linked with high myopia and 39 genes related to degenerative myopia. To uncover additional pathological myopia-associated genes, we employed the random walk with restart algorithm to investigate the protein-protein interactions network. We used the previously identified 184 high myopia and 39 degenerative myopia genes as seed nodes. Results:: Through subsequent screening tests, we discarded genes with weak associations, yielding 103 new genes for high myopia and 33 for degenerative myopia. Conclusion:: We confirmed the association of certain genes, including six genes that were confirmed to be associated with both high and degenerative myopia. The newly discovered genes are helpful to uncover and understand the pathogenesis of myopia.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"82 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138555910","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 : 2023-12-04DOI: 10.2174/0115748936270441231116093650
Jieqi Xing, Yu Shi, Xiaoquan Su, Shunyao Wu
Background:: Microbe-disease associations are integral to understanding complex dis-eases and their screening procedures. Objective:: While numerous computational methods have been developed to detect these associa-tions, their performance remains limited due to inadequate utilization of weighted inherent similari-ties and microbial taxonomy hierarchy. To address this limitation, we have introduced WTHMDA (weighted taxonomic heterogeneous network-based microbe-disease association), a novel deep learning framework. Methods:: WTHMDA combines a weighted graph convolution network and the microbial taxono-my common tree to predict microbe-disease associations effectively. The framework extracts mul-tiple microbe similarities from the taxonomy common tree, facilitating the construction of a mi-crobe-disease heterogeneous interaction network. Utilizing a weighted DeepWalk algorithm, node embeddings in the network incorporate weight information from the similarities. Subsequently, a deep neural network (DNN) model accurately predicts microbe-disease associations based on this interaction network. Results:: Extensive experiments on multiple datasets and case studies demonstrate WTHMDA's su-periority over existing approaches, particularly in predicting unknown associations. Conclusion:: Our proposed method offers a new strategy for discovering microbe-disease linkages, showcasing remarkable performance and enhancing the feasibility of identifying disease risk.
{"title":"Discovering Microbe-disease Associations with Weighted Graph Convolution Networks and Taxonomy Common Tree","authors":"Jieqi Xing, Yu Shi, Xiaoquan Su, Shunyao Wu","doi":"10.2174/0115748936270441231116093650","DOIUrl":"https://doi.org/10.2174/0115748936270441231116093650","url":null,"abstract":"Background:: Microbe-disease associations are integral to understanding complex dis-eases and their screening procedures. Objective:: While numerous computational methods have been developed to detect these associa-tions, their performance remains limited due to inadequate utilization of weighted inherent similari-ties and microbial taxonomy hierarchy. To address this limitation, we have introduced WTHMDA (weighted taxonomic heterogeneous network-based microbe-disease association), a novel deep learning framework. Methods:: WTHMDA combines a weighted graph convolution network and the microbial taxono-my common tree to predict microbe-disease associations effectively. The framework extracts mul-tiple microbe similarities from the taxonomy common tree, facilitating the construction of a mi-crobe-disease heterogeneous interaction network. Utilizing a weighted DeepWalk algorithm, node embeddings in the network incorporate weight information from the similarities. Subsequently, a deep neural network (DNN) model accurately predicts microbe-disease associations based on this interaction network. Results:: Extensive experiments on multiple datasets and case studies demonstrate WTHMDA's su-periority over existing approaches, particularly in predicting unknown associations. Conclusion:: Our proposed method offers a new strategy for discovering microbe-disease linkages, showcasing remarkable performance and enhancing the feasibility of identifying disease risk.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"2 2 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514996","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 : 2023-12-04DOI: 10.2174/0115748936270744231115110329
Shweta Sharma, Garima Singh, Mymoona Akhter
: In the era of genomics, fueled by advanced technologies and analytical tools, metabo-lomics has become a vital component in biomedical research. Its significance spans various do-mains, encompassing biomarker identification, uncovering underlying mechanisms and pathways, as well as the exploration of new drug targets and precision medicine. This article presents a com-prehensive overview of the latest developments in metabolomics techniques, emphasizing their wide-ranging applications across diverse research fields and underscoring their immense potential for future advancements.
{"title":"Metabolomics: Recent Advances and Future Prospects Unveiled","authors":"Shweta Sharma, Garima Singh, Mymoona Akhter","doi":"10.2174/0115748936270744231115110329","DOIUrl":"https://doi.org/10.2174/0115748936270744231115110329","url":null,"abstract":": In the era of genomics, fueled by advanced technologies and analytical tools, metabo-lomics has become a vital component in biomedical research. Its significance spans various do-mains, encompassing biomarker identification, uncovering underlying mechanisms and pathways, as well as the exploration of new drug targets and precision medicine. This article presents a com-prehensive overview of the latest developments in metabolomics techniques, emphasizing their wide-ranging applications across diverse research fields and underscoring their immense potential for future advancements.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514997","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 : 2023-12-04DOI: 10.2174/0115748936272040231117114252
Ying Liang, Suhui Li, Xiya You, You Guo, Jianjun Tang
Background:: Protein lysine crotonylation (Kcr), a newly discovered important post-translational modification (PTM), is typically localized at the transcription start site and regulates gene expression, which is associated with a variety of pathological conditions such as developmen-tal defects and malignant transformation. Objective:: Identifying Kcr sites is advantageous for the discovery of its biological mechanism and the development of new drugs for related diseases. However, traditional experimental methods for identifying Kcr sites are expensive and inefficient, necessitating the development of new computa-tional techniques. Methods:: In this work, to accurately identify Kcr sites, we propose a model for ensemble learning called Stacking-Kcr. Firstly, extract features from sequence information, physicochemical proper-ties, and sequence fragment similarity. Then, the two characteristics of sequence information and physicochemical properties are fused using automatic encoder and serial, respectively. Finally, the fused two features and sequence fragment similarity features are then respectively input into the four base classifiers, a meta classifier is constructed using the first level prediction results, and the final forecasting results are obtained. method: In this work, to accurately identify Kcr sites, we propose a model for ensemble learning called Stacking-Kcr. Firstly, extract features from sequence information, physicochemical properties, and sequence fragment similarity. Then, the two characteristics of sequence information and physicochemical properties are fused using automatic encoder and serial, respectively. Finally, the fused two features and sequence fragment similarity features are then respectively input into the four base classifiers, a meta classifier is constructed using the first level prediction results, and the final forecasting results are obtained. Results:: The five-fold cross-validation of this model has achieved an accuracy of 0.828 and an AUC of 0.910. This shows that the Stacking-Kcr method has obvious advantages over traditional machine learning methods. On independent test sets, Stacking-Kcr achieved an accuracy of 84.89% and an AUC of 92.21%, which was higher than 1.7% and 0.8% of other state-of-the-art tools. Addi-tionally, we trained Stacking-Kcr on the phosphorylation site, and the result is superior to the cur-rent model. Conclusion:: These outcomes are additional evidence that Stacking-Kcr has strong application po-tential and generalization performance.
{"title":"Stacking-Kcr: A Stacking Model for Predicting the Crotonylation Sites of Lysine by Fusing Serial and Automatic Encoder","authors":"Ying Liang, Suhui Li, Xiya You, You Guo, Jianjun Tang","doi":"10.2174/0115748936272040231117114252","DOIUrl":"https://doi.org/10.2174/0115748936272040231117114252","url":null,"abstract":"Background:: Protein lysine crotonylation (Kcr), a newly discovered important post-translational modification (PTM), is typically localized at the transcription start site and regulates gene expression, which is associated with a variety of pathological conditions such as developmen-tal defects and malignant transformation. Objective:: Identifying Kcr sites is advantageous for the discovery of its biological mechanism and the development of new drugs for related diseases. However, traditional experimental methods for identifying Kcr sites are expensive and inefficient, necessitating the development of new computa-tional techniques. Methods:: In this work, to accurately identify Kcr sites, we propose a model for ensemble learning called Stacking-Kcr. Firstly, extract features from sequence information, physicochemical proper-ties, and sequence fragment similarity. Then, the two characteristics of sequence information and physicochemical properties are fused using automatic encoder and serial, respectively. Finally, the fused two features and sequence fragment similarity features are then respectively input into the four base classifiers, a meta classifier is constructed using the first level prediction results, and the final forecasting results are obtained. method: In this work, to accurately identify Kcr sites, we propose a model for ensemble learning called Stacking-Kcr. Firstly, extract features from sequence information, physicochemical properties, and sequence fragment similarity. Then, the two characteristics of sequence information and physicochemical properties are fused using automatic encoder and serial, respectively. Finally, the fused two features and sequence fragment similarity features are then respectively input into the four base classifiers, a meta classifier is constructed using the first level prediction results, and the final forecasting results are obtained. Results:: The five-fold cross-validation of this model has achieved an accuracy of 0.828 and an AUC of 0.910. This shows that the Stacking-Kcr method has obvious advantages over traditional machine learning methods. On independent test sets, Stacking-Kcr achieved an accuracy of 84.89% and an AUC of 92.21%, which was higher than 1.7% and 0.8% of other state-of-the-art tools. Addi-tionally, we trained Stacking-Kcr on the phosphorylation site, and the result is superior to the cur-rent model. Conclusion:: These outcomes are additional evidence that Stacking-Kcr has strong application po-tential and generalization performance.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"44 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138515001","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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