Journal of Bioinformatics and Computational Biology最新文献

{"title":"Computation and analysis of stationary and periodic solutions of the COVID-19 infection dynamics model.","authors":"Michael Khristichenko, Yuri Nechepurenko, Dmitry Grebennikov, Gennady Bocharov","doi":"10.1142/S0219720025400013","DOIUrl":"https://doi.org/10.1142/S0219720025400013","url":null,"abstract":"<p><p>In this work, we search for the conditions for the occurrence of long COVID using the recently developed COVID-19 disease dynamics model which is a system of delay differential equations. To do so, we search for stable stationary or periodic solutions of this model with low viral load that can be interpreted as long COVID using our recently developed technology for analysing time-delay systems. The results of the bifurcation and sensitivity analysis of the mathematical model of SARS-CoV-2 infection suggest the following biological conclusions concerning the mechanisms of pathogenesis of long COVID-19. First, the possibility of SARS-CoV-2 persistence requires a 3-time reduction of the virus production rate per infected cell, or 18-times increase of the antibody-mediated elimination rate of free viruses as compared to an acute infection baseline estimates. Second, the loss of kinetic coordination between virus-induced type I IFN, antibody, and cytotoxic T lymphocyte (CTL) responses can result in the development of mild severity long-lasting infection. Third, the low-level persistent SARS-CoV-2 infection is robust to up to 100-fold perturbations (increase) in viral load and most sensitive to parameters of the humoral immune response.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"23 1","pages":"2540001"},"PeriodicalIF":0.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765517","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}

{"title":"Cross-cellular analysis of chromatin accessibility markers H3K4me3 and DNase in the context of detecting cell-identity genes: An \"all-or-nothing\" approach.","authors":"Boon How Low, Kaushal Krishna Kaliskar, Stefano Perna, Bernett Lee","doi":"10.1142/S0219720025400025","DOIUrl":"https://doi.org/10.1142/S0219720025400025","url":null,"abstract":"<p><p>Cell identity is often associated to a subset of highly-expressed genes that define the cell processes, as opposed to essential genes that are always active. Cell-specific genes may be defined in opposition to essential genes, or via experimental means. Detection of said cell-specific genes is often a primary goal in the study of novel biosamples. Chromatin accessibility markers (such as DNase and H3K4me3) help identify actively transcribed genes, but data can be difficult to come by for entirely novel biosamples. In this study, we investigate the possibility of associating the cell-specificity status of genes with chromatin accessibility markers from different cell lines, and we suggest that the number of cell lines in which a gene is found to be marked by DNase/H3K4me3 is predictive of the essentiality status itself. We define a measure called the Cross-cellular Chromatin Openness (CCO) level, and show that it is associated with the essentiality status using two differentiation experiments. We then compare the CCO-level predictive power to existing scRNA-Seq and bulk RNA-Seq methods, showing it has good concordance when applicable.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"23 1","pages":"2540002"},"PeriodicalIF":0.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765527","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}

{"title":"SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction.","authors":"Yujie Chun, Huaihu Li, Shunfang Wang","doi":"10.1142/S0219720025500027","DOIUrl":"10.1142/S0219720025500027","url":null,"abstract":"<p><p>Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab experiments by saving time and resources and expediting the identification of potential targets. Current DTI methods predominantly focus on extracting semantic features from drug and protein sequences or utilizing structural information, often neglecting the integration of both. This gap hinders the achievement of a comprehensive representation of drug and protein molecules. To address this, we propose SS-DTI, a novel end-to-end deep learning approach that integrates both semantic and structural information. Our method features a multi-scale semantic feature extraction block to capture local and global information from sequences and employs Graph Convolutional Networks (GCNs) to learn structural features. Evaluations on four benchmark datasets demonstrate that SS-DTI outperforms state-of-the-art methods, showcasing its superior predictive performance. Our code is available at https://github.com/RobinChun/SS-DTI.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2550002"},"PeriodicalIF":0.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711799","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}

{"title":"Drug repurposing for non-small cell lung cancer by predicting drug response using pathway-level graph convolutional network.","authors":"I T Anjusha, K A Abdul Nazeer, N Saleena","doi":"10.1142/S0219720025500015","DOIUrl":"10.1142/S0219720025500015","url":null,"abstract":"<p><p>Drug repurposing is the process of identifying new clinical indications for an existing drug. Some of the recent studies utilized drug response prediction models to identify drugs that can be repurposed. By representing cell-line features as a pathway-pathway interaction network, we can better understand the connections between cellular processes and drug response mechanisms. Existing deep learning models for drug response prediction do not integrate known biological pathway-pathway interactions into the model. This paper presents a drug response prediction model that applies a graph convolution operation on a pathway-pathway interaction network to represent features of cancer cell-lines effectively. The model is used to identify potential drug repurposing candidates for Non-Small Cell Lung Cancer (NSCLC). Experiment results show that the inclusion of graph convolutional model applied on a pathway-pathway interaction network makes the proposed model more effective in predicting drug response than the state-of-the-art methods. Specifically, the model has shown better performance in terms of Root Mean Squared Error, Coefficient of Determination, and Pearson's Correlation Coefficient when applied to the GDSC1000 dataset. Also, most of the drugs that the model predicted as top candidates for NSCLC treatment are either undergoing clinical studies or have some evidence in the PubMed literature database.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2550001"},"PeriodicalIF":0.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711795","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}

{"title":"DDINet: Drug-drug interaction prediction network based on multi-molecular fingerprint features and multi-head attention centered weighted autoencoder.","authors":"K Soni Sharmila, Thanga Revathi S, Pokkuluri Kiran Sree","doi":"10.1142/S0219720025500039","DOIUrl":"https://doi.org/10.1142/S0219720025500039","url":null,"abstract":"<p><p>Drug-drug interactions (DDIs) pose a major concern in polypharmacy due to their potential to cause unexpected side effects that can adversely affect a patient's health. Therefore, it is crucial to identify DDIs effectively during the early stages of drug discovery and development. In this paper, a novel DDI prediction network (DDINet) is proposed to enhance the predictive performance over conventional DDI methods. Leveraging the DrugBank dataset, drugs are represented using the Simplified Molecular Input Line-Entry System (SMILES), with the RDKit software pre-processing the SMILES strings into their canonical forms. Multiple molecular fingerprinting techniques such as Extended Connectivity Fingerprints (ECFPs), Molecular ACCess System keys (MACCSkeys), PubChem Fingerprints, 3D molecular fingerprints (3D-FP), and molecular dynamics fingerprints (MDFPs) are employed to encode drug chemical structures into feature vectors. Drug similarities are computed using the Tanimoto coefficient (TC), and the final Structural Similarity Profile (SSP) is obtained by averaging the five molecular fingerprint types. The novelty of the approach lies in the integration of a Multi-head Attention centered Weighted Autoencoder (Mul_WAE) as the interaction prediction module, which leverages the Multi-head Attention (MHA) layer to focus on the most significant input features. Furthermore, we introduce the Upgraded Bald Eagle Search Optimization (UBesO) algorithm, which optimally selects the learnable parameters of the Mul_WAE based on cross-entropy loss, improving the model's convergence and performance. The proposed DDINet model achieves an accuracy of 99.77%, 99.66% of AUC, 99.5% average precision, 99.4% precision, and 99.49% recall, providing a comprehensive evaluation of the model's robustness. Beyond high accuracy, DDINet offers advantages in scalability, making it well suited for handling large datasets due to its efficient feature extraction and optimization processes. The unique combination of multiple molecular fingerprinting methods with the MHA layer and UBesO algorithm highlights the innovative aspects of our model and significantly improves prediction performance compared to existing approaches.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"23 1","pages":"2550003"},"PeriodicalIF":0.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765530","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}

{"title":"Gene regulatory network inference based on modified adaptive lasso.","authors":"Chao Li, Xiaoran Huang, Xiao Luo, Xiaohui Lin","doi":"10.1142/S0219720024500264","DOIUrl":"10.1142/S0219720024500264","url":null,"abstract":"<p><p>Gene regulatory networks (GRNs) reveal the regulatory interactions among genes and provide a visual tool to explain biological processes. However, how to identify direct relations among genes from gene expression data in the case of high-dimensional and small samples is a critical challenge. In this paper, we proposed a new GRN inference method based on a modified adaptive least absolute shrinkage and selection operator (MALasso). MALasso expands the number of samples based on the distance correlation and defines a new weighting manner for adaptive lasso to remove false positive edges of the networks in the iterative process. Simulated data and gene expression data from DREAM challenge were used to validate the performance of the proposed method MALasso. The comparison results among MALasso, adaptive lasso and other six state-of-the-art methods show that MALasso outperformed the competition methods in AUROCC and AUPRC in most cases and had a better ability to distinguish direct edges from indirect ones. Hence, by modifying the adaptive weighting manner of adaptive lasso, MALasso can detect linear and nonlinear relations, remove the false positive edges and identify direct relations among genes more accurately.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2450026"},"PeriodicalIF":0.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014473","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}

{"title":"The use of 4D data-independent acquisition-based proteomic analysis and machine learning to reveal potential biomarkers for stress levels.","authors":"Dehua Chen, Yongsheng Yang, Dongdong Shi, Zhenhua Zhang, Mei Wang, Qiao Pan, Jianwen Su, Zhen Wang","doi":"10.1142/S0219720024500252","DOIUrl":"10.1142/S0219720024500252","url":null,"abstract":"<p><p>Research suggests that individuals who experience prolonged exposure to stress may be at higher risk for developing psychological stress disorders. Currently, psychological stress is primarily evaluated by professional physicians using rating scales, which may be prone to subjective biases and limitations of the scales. Therefore, it is imperative to explore more objective, accurate, and efficient biomarkers for evaluating the level of psychological stress in an individual. In this study, we utilized 4D data-independent acquisition (4D-DIA) proteomics for quantitative protein analysis, and then employed support vector machine (SVM) combined with SHAP interpretation algorithm to identify potential biomarkers for psychological stress levels. Biomarkers validation was subsequently achieved through machine learning classification and a substantial amount of a priori knowledge derived from the knowledge graph. We performed cross-validation of the biomarkers using two batches of data, and the results showed that the combination of Glyceraldehyde-3-phosphate dehydrogenase and Fibronectin yielded an average area under the curve (AUC) of 92%, an average accuracy of 86%, an average F1 score of 79%, and an average sensitivity of 83%. Therefore, this combination may represent a potential approach for detecting stress levels to prevent psychological stress disorders.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2450025"},"PeriodicalIF":0.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639951","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}

{"title":"Author index Volume 22 (2024).","authors":"","doi":"10.1142/S0219720024990014","DOIUrl":"https://doi.org/10.1142/S0219720024990014","url":null,"abstract":"","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"22 6","pages":"2499001"},"PeriodicalIF":0.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442521","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}

{"title":"ASAP-DTA: Predicting drug-target binding affinity with adaptive structure aware networks.","authors":"Weibin Ding, Shaohua Jiang, Ting Xu, Zhijian Lyu","doi":"10.1142/S0219720024500288","DOIUrl":"https://doi.org/10.1142/S0219720024500288","url":null,"abstract":"<p><p>The prediction of drug-target affinity (DTA) is crucial for efficiently identifying potential targets for drug repurposing, thereby reducing resource wastage. In this paper, we propose a novel graph-based deep learning model for DTA that leverages adaptive structure-aware pooling for graph processing. Our approach integrates a self-attention mechanism with an enhanced graph neural network to capture the significance of each node in the graph, marking a significant advancement in graph feature extraction. Specifically, adjacent nodes in the 2D molecular graph are aggregated into clusters, with the features of these clusters weighted according to their attention scores to form the final molecular representation. In terms of model architecture, we utilize both global and hierarchical pooling, and assess the performance of the model on multiple benchmark datasets. The evaluation results on the KIBA dataset show that our model achieved the lowest mean squared error (MSE) of 0.126, which is a 0.5% reduction compared to the best-performing baseline method. Additionally, to validate the generalization capabilities of the model, we conduct comparative experiments on regression and binary classification tasks. The results demonstrate that our model outperforms previous models in both types of tasks.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"22 6","pages":"2450028"},"PeriodicalIF":0.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442501","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}

{"title":"Research on similarity retrieval method based on mass spectral entropy.","authors":"Li-Ping Wu, Li Yong, Xiang Cheng, Yang Zhou","doi":"10.1142/S0219720024500276","DOIUrl":"https://doi.org/10.1142/S0219720024500276","url":null,"abstract":"<p><p>Compound identification in small molecule research relies on comparing experimental mass spectra with mass spectral databases. However, unequal data lengths often lead to inefficient and inaccurate retrieval. Moreover, the similarity calculation methods used by commercial software have limitations. To address these issues, two mass spectrometry data processing methods namely the \"splicing-filling method\" and the \"matching-filling method\" have been proposed. In addition, an information entropy-based similarity calculation method for mass spectra is presented. The alignment method converts mass spectra of different lengths for unknown and known compounds into equal-length mass spectra, allowing more accurate calculation of similarities between mass spectra. Information entropy measurements are used to quantify the differences in intensity distributions in the aligned mass spectral data, which are then used to compare the degree of similarity between different mass spectra. The results of the example validation show that the two data alignment methods can effectively solve the problem of unequal lengths of mass spectral data in similarity calculation. The results of the mass spectral entropy method are reliable and suitable for the identification of mass spectra.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"22 6","pages":"2450027"},"PeriodicalIF":0.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442527","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}