Journal of Bioinformatics and Computational Biology最新文献_第3页

AAindex-PPII: Predicting polyproline type II helix structure based on amino acid indexes with an improved BiGRU-TextCNN model. AAindex PPII：用改进的BiGRU TextCNN模型基于氨基酸指数预测聚脯氨酸II型螺旋结构。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2023-10-01 Epub Date: 2023-10-28 DOI: 10.1142/S0219720023500221

Jiasheng He, Shun Zhang, Chun Fang

引用次数: 0

iAMY-RECMFF: Identifying amyloidgenic peptides by using residue pairwise energy content matrix and features fusion algorithm. iAMY RECMFF：利用残基成对能量含量矩阵和特征融合算法识别淀粉桥肽。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2023-10-01 Epub Date: 2023-10-27 DOI: 10.1142/S0219720023500233

Zizheng Yu, Zhijian Yin, Hongliang Zou

{"title":"iAMY-RECMFF: Identifying amyloidgenic peptides by using residue pairwise energy content matrix and features fusion algorithm.","authors":"Zizheng Yu, Zhijian Yin, Hongliang Zou","doi":"10.1142/S0219720023500233","DOIUrl":"10.1142/S0219720023500233","url":null,"abstract":"Various diseases, including Huntington's disease, Alzheimer's disease, and Parkinson's disease, have been reported to be linked to amyloid. Therefore, it is crucial to distinguish amyloid from non-amyloid proteins or peptides. While experimental approaches are typically preferred, they are costly and time-consuming. In this study, we have developed a machine learning framework called iAMY-RECMFF to discriminate amyloidgenic from non-amyloidgenic peptides. In our model, we first encoded the peptide sequences using the residue pairwise energy content matrix. We then utilized Pearson's correlation coefficient and distance correlation to extract useful information from this matrix. Additionally, we employed an improved similarity network fusion algorithm to integrate features from different perspectives. The Fisher approach was adopted to select the optimal feature subset. Finally, the selected features were inputted into a support vector machine for identifying amyloidgenic peptides. Experimental results demonstrate that our proposed method significantly improves the identification of amyloidgenic peptides compared to existing predictors. This suggests that our method may serve as a powerful tool in identifying amyloidgenic peptides. To facilitate academic use, the dataset and codes used in the current study are accessible at https://figshare.com/articles/online_resource/iAMY-RECMFF/22816916.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2350023"},"PeriodicalIF":1.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71414897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CBDT-Oglyc: Prediction of O-glycosylation sites using ChiMIC-based balanced decision table and feature selection. CBDT-Oglyc:使用基于ChiMIC的平衡决策表和特征选择预测O-糖基化位点。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2023-10-01 Epub Date: 2023-10-28 DOI: 10.1142/S0219720023500245

Ying Zeng, Zheming Yuan, Yuan Chen, Ying Hu

{"title":"CBDT-Oglyc: Prediction of O-glycosylation sites using ChiMIC-based balanced decision table and feature selection.","authors":"Ying Zeng, Zheming Yuan, Yuan Chen, Ying Hu","doi":"10.1142/S0219720023500245","DOIUrl":"10.1142/S0219720023500245","url":null,"abstract":"O-glycosylation (Oglyc) plays an important role in various biological processes. The key to understanding the mechanisms of Oglyc is identifying the corresponding glycosylation sites. Two critical steps, feature selection and classifier design, greatly affect the accuracy of computational methods for predicting Oglyc sites. Based on an efficient feature selection algorithm and a classifier capable of handling imbalanced datasets, a new computational method, ChiMIC-based balanced decision table O-glycosylation (CBDT-Oglyc), is proposed. ChiMIC-based balanced decision table for O-glycosylation (CBDT-Oglyc), is proposed to predict Oglyc sites in proteins. Sequence characterization is performed by combining amino acid composition (AAC), undirected composition of [Formula: see text]-spaced amino acid pairs (undirected-CKSAAP) and pseudo-position-specific scoring matrix (PsePSSM). Chi-MIC-share algorithm is used for feature selection, which simplifies the model and improves predictive accuracy. For imbalanced classification, a backtracking method based on local chi-square test is designed, and then cost-sensitive learning is incorporated to construct a novel classifier named ChiMIC-based balanced decision table (CBDT). Based on a 1:49 (positives:negatives) training set, the CBDT classifier achieves significantly better prediction performance than traditional classifiers. Moreover, the independent test results on separate human and mouse glycoproteins show that CBDT-Oglyc outperforms previous methods in global accuracy. CBDT-Oglyc shows great promise in predicting Oglyc sites and is expected to facilitate further experimental studies on protein glycosylation.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2350024"},"PeriodicalIF":1.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71414896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepRT: Predicting compounds presence in pathway modules and classifying into module classes using deep neural networks based on molecular properties. DeepRT：使用基于分子特性的深度神经网络预测化合物在通路模块中的存在并分类为模块类。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2023-08-01 Epub Date: 2023-08-24 DOI: 10.1142/S0219720023500178

Hayat Ali Shah, Juan Liu, Zhihui Yang, Feng Yang, Qiang Zhang, Jing Feng

{"title":"DeepRT: Predicting compounds presence in pathway modules and classifying into module classes using deep neural networks based on molecular properties.","authors":"Hayat Ali Shah, Juan Liu, Zhihui Yang, Feng Yang, Qiang Zhang, Jing Feng","doi":"10.1142/S0219720023500178","DOIUrl":"10.1142/S0219720023500178","url":null,"abstract":"Metabolic pathways play a crucial role in understanding the biochemistry of organisms. In metabolic pathways, modules refer to clusters of interconnected reactions or sub-networks representing specific functional units or biological processes within the overall pathway. In pathway modules, compounds are major elements and refer to the various molecules that participate in the biochemical reactions within the pathway modules. These molecules can include substrates, intermediates and final products. Determining the presence relation of compounds and pathway modules is essential for synthesizing new molecules and predicting hidden reactions. To date, several computational methods have been proposed to address this problem. However, all methods only predict the metabolic pathways and their types, not the pathway modules. To address this issue, we proposed a novel deep learning model, DeepRT that integrates message passing neural networks (MPNNs) and transformer encoder. This combination allows DeepRT to effectively extract global and local structure information from the molecular graph. The model is designed to perform two tasks: first, determining the present relation of the compound with the pathway module, and second, predicting the relation of query compound and module classes. The proposed DeepRT model evaluated on a dataset comprising compounds and pathway modules, and it outperforms existing approaches.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"21 4","pages":"2350017"},"PeriodicalIF":1.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10306371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-omics data analysis reveals the biological implications of alternative splicing events in lung adenocarcinoma. 多组学数据分析揭示了肺腺癌中选择性剪接事件的生物学意义。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2023-08-01 Epub Date: 2023-09-08 DOI: 10.1142/S0219720023500208

Fuyan Hu, Bifeng Chen, Qing Wang, Zhiyuan Yang, Man Chu

{"title":"Multi-omics data analysis reveals the biological implications of alternative splicing events in lung adenocarcinoma.","authors":"Fuyan Hu, Bifeng Chen, Qing Wang, Zhiyuan Yang, Man Chu","doi":"10.1142/S0219720023500208","DOIUrl":"10.1142/S0219720023500208","url":null,"abstract":"Cancer is characterized by the dysregulation of alternative splicing (AS). However, the comprehensive regulatory mechanisms of AS in lung adenocarcinoma (LUAD) are poorly understood. Here, we displayed the AS landscape in LUAD based on the integrated analyses of LUAD's multi-omics data. We identified 13,995 AS events in 6309 genes as differentially expressed alternative splicing events (DEASEs) mainly covering protein-coding genes. These DEASEs were strongly linked to \"cancer hallmarks\", such as apoptosis, DNA repair, cell cycle, cell proliferation, angiogenesis, immune response, generation of precursor metabolites and energy, p53 signaling pathway and PI3K-AKT signaling pathway. We further built a regulatory network connecting splicing factors (SFs) and DEASEs. In addition, RNA-binding protein (RBP) mutations that can affect DEASEs were investigated to find some potential cancer drivers. Further association analysis demonstrated that DNA methylation levels were highly correlated with DEASEs. In summary, our results can bring new insight into understanding the mechanism of AS and provide novel biomarkers for personalized medicine of LUAD.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"21 4","pages":"2350020"},"PeriodicalIF":1.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10307695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification. 基于模型的协变量调整聚类算法及其在癌症分层中的应用。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2023-08-01 Epub Date: 2023-09-08 DOI: 10.1142/S0219720023500191

Carlos E M Relvas, Asuka Nakata, Guoan Chen, David G Beer, Noriko Gotoh, Andre Fujita

引用次数: 0

Local RNA folding revisited. 对局部RNA折叠进行了重新研究。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2023-08-01 Epub Date: 2023-07-28 DOI: 10.1142/S0219720023500166

Maria Waldl, Thomas Spicher, Ronny Lorenz, Irene K Beckmann, Ivo L Hofacker, Sarah Von Löhneysen, Peter F Stadler

引用次数: 0

Facilitating the drug repurposing with iC/E strategy: A practice on novel nNOS inhibitor discovery. 利用iC/E策略促进药物再利用：新nNOS抑制剂发现的实践。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2023-08-01 Epub Date: 2023-09-06 DOI: 10.1142/S021972002350018X

Zhaoyang Hu, Qingsen Liu, Zhong Ni

{"title":"Facilitating the drug repurposing with iC/E strategy: A practice on novel nNOS inhibitor discovery.","authors":"Zhaoyang Hu, Qingsen Liu, Zhong Ni","doi":"10.1142/S021972002350018X","DOIUrl":"10.1142/S021972002350018X","url":null,"abstract":"Over the past decades, many existing drugs and clinical/preclinical compounds have been repositioned as new therapeutic indication from which they were originally intended and to treat off-target diseases by targeting their noncognate protein receptors, such as Sildenafil and Paxlovid, termed drug repurposing (DRP). Despite its significant attraction in the current medicinal community, the DRP is usually considered as a matter of accidents that cannot be fulfilled reliably by traditional drug discovery protocol. In this study, we proposed an integrated computational/experimental (iC/E) strategy to facilitate the DRP within a framework of rational drug design, which was practiced on the identification of new neuronal nitric oxide synthase (nNOS) inhibitors from a structurally diverse, functionally distinct drug pool. We demonstrated that the iC/E strategy is very efficient and readily feasible, which confirmed that the phosphodiesterase inhibitor DB06237 showed a high inhibitory potency against nNOS synthase domain, while other two general drugs, i.e. DB02302 and DB08258, can also inhibit the synthase at nanomolar level. Structural bioinformatics analysis revealed diverse noncovalent interactions such as hydrogen bonds, hydrophobic forces and van der Waals contacts across the complex interface of nNOS active site with these identified drugs, conferring both stability and specificity for the complex recognition and association.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"21 4","pages":"2350018"},"PeriodicalIF":1.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10669370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Drug synergy model for malignant diseases using deep learning. 基于深度学习的恶性疾病药物协同模型。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2023-06-01 DOI: 10.1142/S0219720023500142

Pooja Rani, Kamlesh Dutta, Vijay Kumar

{"title":"Drug synergy model for malignant diseases using deep learning.","authors":"Pooja Rani, Kamlesh Dutta, Vijay Kumar","doi":"10.1142/S0219720023500142","DOIUrl":"https://doi.org/10.1142/S0219720023500142","url":null,"abstract":"Drug synergy has emerged as a viable treatment option for malignancy. Drug synergy reduces toxicity, improves therapeutic efficacy, and overcomes drug resistance when compared to single-drug doses. Thus, it has attained significant interest from academics and pharmaceutical organizations. Due to the enormous combinatorial search space, it is impossible to experimentally validate every conceivable combination for synergistic interaction. Due to advancement in artificial intelligence, the computational techniques are being utilized to identify synergistic drug combinations, whereas prior literature has focused on treating certain malignancies. As a result, high-order drug combinations have been given little consideration. Here, DrugSymby, a novel deep-learning model is proposed for predicting drug combinations. To achieve this objective, the data is collected from datasets that include information on anti-cancer drugs, gene expression profiles of malignant cell lines, and screening data against a wide range of malignant cell lines. The proposed model was developed using this data and achieved high performance with f1-score of 0.98, recall of 0.99, and precision of 0.98. The evaluation results of DrugSymby model utilizing drug combination screening data from the NCI-ALMANAC screening dataset indicate drug combination prediction is effective. The proposed model will be used to determine the most successful synergistic drug combinations, and also increase the possibilities of exploring new drug combinations.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"21 3","pages":"2350014"},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10127381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating temporal and spatial variabilities for identifying ion binding proteins in phage. 整合噬菌体中离子结合蛋白的时空变异。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2023-06-01 DOI: 10.1142/S0219720023500105

Hongliang Zou, Zizheng Yu, Zhijian Yin

{"title":"Integrating temporal and spatial variabilities for identifying ion binding proteins in phage.","authors":"Hongliang Zou, Zizheng Yu, Zhijian Yin","doi":"10.1142/S0219720023500105","DOIUrl":"https://doi.org/10.1142/S0219720023500105","url":null,"abstract":"Recent studies reported that ion binding proteins (IBPs) in phage play a key role in developing drugs to treat diseases caused by drug-resistant bacteria. Therefore, correct recognition of IBPs is an urgent task, which is beneficial for understanding their biological functions. To explore this issue, a new computational model was developed to identify IBPs in this study. First, we used the physicochemical (PC) property and Pearson's correlation coefficient (PCC) to denote protein sequences, and the temporal and spatial variabilities were employed to extract features. Next, a similarity network fusion algorithm was employed to capture the correlation characteristics between these two different kinds of features. Then, a feature selection method called F-score was utilized to remove the influence of redundant and irrelative information. Finally, these reserved features were fed into support vector machine (SVM) to discriminate IBPs from non-IBPs. Experimental results showed that the proposed method has significant improvement in the classification performance, as compared with the state-of-the-art approach. The Matlab codes and dataset used in this study are available at https://figshare.com/articles/online_resource/iIBP-TSV/21779567 for academic use.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"21 3","pages":"2350010"},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9750670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0