Journal of Integrative Bioinformatics最新文献_第2页

Transformers meets neoantigen detection: a systematic literature review. 变形金刚与新抗原检测：系统文献综述。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-04 eCollection Date: 2024-06-01 DOI: 10.1515/jib-2023-0043

Vicente Machaca, Valeria Goyzueta, María Graciel Cruz, Erika Sejje, Luz Marina Pilco, Julio López, Yván Túpac

引用次数: 0

MakeSBML: a tool for converting between Antimony and SBML. MakeSBML：在 Antimony 和 SBML 之间进行转换的工具。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-06-11 eCollection Date: 2024-03-01 DOI: 10.1515/jib-2024-0002

Bartholomew E Jardine, Lucian P Smith, Herbert M Sauro

引用次数: 0

SBMLToolkit.jl: a Julia package for importing SBML into the SciML ecosystem. SBMLToolkit.jl：将 SBML 导入 SciML 生态系统的 Julia 软件包。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-05-28 eCollection Date: 2024-03-01 DOI: 10.1515/jib-2024-0003

Paul F Lang, Anand Jain, Christopher Rackauckas

{"title":"SBMLToolkit.jl: a Julia package for importing SBML into the SciML ecosystem.","authors":"Paul F Lang, Anand Jain, Christopher Rackauckas","doi":"10.1515/jib-2024-0003","DOIUrl":"10.1515/jib-2024-0003","url":null,"abstract":"Julia is a general purpose programming language that was designed for simplifying and accelerating numerical analysis and computational science. In particular the Scientific Machine Learning (SciML) ecosystem of Julia packages includes frameworks for high-performance symbolic-numeric computations. It allows users to automatically enhance high-level descriptions of their models with symbolic preprocessing and automatic sparsification and parallelization of computations. This enables performant solution of differential equations, efficient parameter estimation and methodologies for automated model discovery with neural differential equations and sparse identification of nonlinear dynamics. To give the systems biology community easy access to SciML, we developed SBMLToolkit.jl. SBMLToolkit.jl imports dynamic SBML models into the SciML ecosystem to accelerate model simulation and fitting of kinetic parameters. By providing computational systems biologists with easy access to the open-source Julia ecosystevnm, we hope to catalyze the development of further Julia tools in this domain and the growth of the Julia bioscience community. SBMLToolkit.jl is freely available under the MIT license. The source code is available at https://github.com/SciML/SBMLToolkit.jl.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unlocking the power of AI models: exploring protein folding prediction through comparative analysis. 释放人工智能模型的力量：通过比较分析探索蛋白质折叠预测。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-05-27 eCollection Date: 2024-06-01 DOI: 10.1515/jib-2023-0041

Paloma Tejera-Nevado, Emilio Serrano, Ana González-Herrero, Rodrigo Bermejo, Alejandro Rodríguez-González

{"title":"Unlocking the power of AI models: exploring protein folding prediction through comparative analysis.","authors":"Paloma Tejera-Nevado, Emilio Serrano, Ana González-Herrero, Rodrigo Bermejo, Alejandro Rodríguez-González","doi":"10.1515/jib-2023-0041","DOIUrl":"10.1515/jib-2023-0041","url":null,"abstract":"Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However, obtaining accurate predictions becomes essential in certain cases where the protein structure remains undescribed. This is particularly challenging when dealing with rare, diverse structures and complex sample preparation. Different metrics assess prediction reliability and offer insights into result strength, providing a comprehensive understanding of protein structure by combining different models. In a previous study, two proteins named ARM58 and ARM56 were investigated. These proteins contain four domains of unknown function and are present in Leishmania spp. ARM refers to an antimony resistance marker. The study's main objective is to assess the accuracy of the model's predictions, thereby providing insights into the complexities and supporting metrics underlying these findings. The analysis also extends to the comparison of predictions obtained from other species and organisms. Notably, one of these proteins shares an ortholog with Trypanosoma cruzi and Trypanosoma brucei, leading further significance to our analysis. This attempt underscored the importance of evaluating the diverse outputs from deep learning models, facilitating comparisons across different organisms and proteins. This becomes particularly pertinent in cases where no previous structural information is available.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11377126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The simulation experiment description markup language (SED-ML): language specification for level 1 version 5. 模拟实验描述标记语言（SED-ML）：第 1 级第 5 版语言规范。

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2024-04-15 DOI: 10.1515/jib-2024-0008

Lucian P. Smith, Frank T. Bergmann, Alan Garny, Tomáš Helikar, Jonathan Karr, D. Nickerson, Herbert M. Sauro, Dagmar Waltemath, Matthias König

{"title":"The simulation experiment description markup language (SED-ML): language specification for level 1 version 5.","authors":"Lucian P. Smith, Frank T. Bergmann, Alan Garny, Tomáš Helikar, Jonathan Karr, D. Nickerson, Herbert M. Sauro, Dagmar Waltemath, Matthias König","doi":"10.1515/jib-2024-0008","DOIUrl":"https://doi.org/10.1515/jib-2024-0008","url":null,"abstract":"Modern biological research is increasingly informed by computational simulation experiments, which necessitate the development of methods for annotating, archiving, sharing, and reproducing the conducted experiments. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. Level 1 Version 5 of SED-ML expands the ability of modelers to define simulations in SED-ML using the Kinetic Simulation Algorithm Onotoloy (KiSAO). While it was possible in Version 4 to define a simulation entirely using KiSAO, Version 5 now allows users to define tasks, model changes, ranges, and outputs using the ontology as well. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including various languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, and many simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140703567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Auto-phylo v2 and auto-phylo-pipeliner: building advanced, flexible, and reusable pipelines for phylogenetic inferences, estimation of variability levels and identification of positively selected amino acid sites. Auto-phylo v2 和 aut-phylo-pipeliner：构建先进、灵活和可重复使用的管道，用于系统发育推断、变异性水平估计和正选氨基酸位点识别。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-03-27 eCollection Date: 2024-06-01 DOI: 10.1515/jib-2023-0046

Hugo López-Fernández, Miguel Pinto, Cristina P Vieira, Pedro Duque, Miguel Reboiro-Jato, Jorge Vieira

{"title":"Auto-phylo v2 and auto-phylo-pipeliner: building advanced, flexible, and reusable pipelines for phylogenetic inferences, estimation of variability levels and identification of positively selected amino acid sites.","authors":"Hugo López-Fernández, Miguel Pinto, Cristina P Vieira, Pedro Duque, Miguel Reboiro-Jato, Jorge Vieira","doi":"10.1515/jib-2023-0046","DOIUrl":"10.1515/jib-2023-0046","url":null,"abstract":"The vast amount of genome sequence data that is available, and that is predicted to drastically increase in the near future, can only be efficiently dealt with by building automated pipelines. Indeed, the Earth Biogenome Project will produce high-quality reference genome sequences for all 1.8 million named living eukaryote species, providing unprecedented insight into the evolution of genes and gene families, and thus on biological issues. Here, new modules for gene annotation, further BLAST search algorithms, further multiple sequence alignment methods, the adding of reference sequences, further tree rooting methods, the estimation of rates of synonymous and nonsynonymous substitutions, and the identification of positively selected amino acid sites, have been added to auto-phylo (version 2), a recently developed software to address biological problems using phylogenetic inferences. Additionally, we present auto-phylo-pipeliner, a graphical user interface application that further facilitates the creation and running of auto-phylo pipelines. Inferences on S-RNase specificity, are critical for both cross-based breeding and for the establishment of pollination requirements. Therefore, as a test case, we develop an auto-phylo pipeline to identify amino acid sites under positive selection, that are, in principle, those determining S-RNase specificity, starting from both non-annotated Prunus genomes and sequences available in public databases.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11378518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140289644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps. MetaLo：从分子相互作用图中提取的逻辑模型代谢分析。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-02-06 eCollection Date: 2024-03-01 DOI: 10.1515/jib-2023-0048

Sahar Aghakhani, Anna Niarakis, Sylvain Soliman

{"title":"MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps.","authors":"Sahar Aghakhani, Anna Niarakis, Sylvain Soliman","doi":"10.1515/jib-2023-0048","DOIUrl":"10.1515/jib-2023-0048","url":null,"abstract":"Molecular interaction maps (MIMs) are static graphical representations depicting complex biochemical networks that can be formalized using one of the Systems Biology Graphical Notation languages. Regardless of their extensive coverage of various biological processes, they are limited in terms of dynamic insights. However, MIMs can serve as templates for developing dynamic computational models. We present MetaLo, an open-source Python package that enables the coupling of Boolean models inferred from process description MIMs with generic core metabolic networks. MetaLo provides a framework to study the impact of signaling cascades, gene regulation processes, and metabolic flux distribution of central energy production pathways. MetaLo computes the Boolean model's asynchronous asymptotic behavior, through the identification of trap-spaces, and extracts metabolic constraints to contextualize the generic metabolic network. MetaLo is able to handle large-scale Boolean models and genome-scale metabolic models without requiring kinetic information or manual tuning. The framework behind MetaLo enables in depth analysis of the regulatory model, and may allow tackling a lack of omics data in poorly addressed biological fields to contextualize generic metabolic networks along with improper automatic reconstructions of cell- and/or disease-specific metabolic networks. MetaLo is available at https://pypi.org/project/metalo/ under the terms of the GNU General Public License v3.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11293895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139693479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PharmoCo: a graph-based visualization of pharmacogenomic plausibility check reports for clinical decision support systems. PharmoCo：用于临床决策支持系统的基于图形的药物基因组可信性检查报告可视化。

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2023-12-28 eCollection Date: 2023-12-01 DOI: 10.1515/jib-2023-0026

Lena Raupach, Cassandra Königs

引用次数: 0

Gene-network based analysis of human placental trophoblast subtypes identifies critical genes as potential targets of therapeutic drugs. 基于基因网络的人类胎盘滋养细胞亚型分析确定了作为治疗药物潜在靶点的关键基因。

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2023-12-22 eCollection Date: 2023-12-01 DOI: 10.1515/jib-2023-0011

Andreas Ian Lackner, Jürgen Pollheimer, Paulina Latos, Martin Knöfler, Sandra Haider

{"title":"Gene-network based analysis of human placental trophoblast subtypes identifies critical genes as potential targets of therapeutic drugs.","authors":"Andreas Ian Lackner, Jürgen Pollheimer, Paulina Latos, Martin Knöfler, Sandra Haider","doi":"10.1515/jib-2023-0011","DOIUrl":"10.1515/jib-2023-0011","url":null,"abstract":"During early pregnancy, extravillous trophoblasts (EVTs) play a crucial role in modifying the maternal uterine environment. Failures in EVT lineage formation and differentiation can lead to pregnancy complications such as preeclampsia, fetal growth restriction, and pregnancy loss. Despite recent advances, our knowledge on molecular and external factors that control and affect EVT development remains incomplete. Using trophoblast organoid in vitro models, we recently discovered that coordinated manipulation of the transforming growth factor beta (TGFβ) signaling is essential for EVT development. To further investigate gene networks involved in EVT function and development, we performed weighted gene co-expression network analysis (WGCNA) on our RNA-Seq data. We identified 10 modules with a median module membership of over 0.8 and sizes ranging from 1005 (M1) to 72 (M27) network genes associated with TGFβ activation status or in vitro culturing, the latter being indicative for yet undiscovered factors that shape the EVT phenotypes. Lastly, we hypothesized that certain therapeutic drugs might unintentionally interfere with placentation by affecting EVT-specific gene expression. We used the STRING database to map correlations and the Drug-Gene Interaction database to identify drug targets. Our comprehensive dataset of drug-gene interactions provides insights into potential risks associated with certain drugs in early gestation.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10777358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138832989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An overview of machine learning and deep learning techniques for predicting epileptic seizures. 预测癫痫发作的机器学习和深度学习技术概述。

IF 1.9

Journal of Integrative Bioinformatics Pub Date : 2023-12-15 eCollection Date: 2023-12-01 DOI: 10.1515/jib-2023-0002

Marco Zurdo-Tabernero, Ángel Canal-Alonso, Fernando de la Prieta, Sara Rodríguez, Javier Prieto, Juan Manuel Corchado

{"title":"An overview of machine learning and deep learning techniques for predicting epileptic seizures.","authors":"Marco Zurdo-Tabernero, Ángel Canal-Alonso, Fernando de la Prieta, Sara Rodríguez, Javier Prieto, Juan Manuel Corchado","doi":"10.1515/jib-2023-0002","DOIUrl":"10.1515/jib-2023-0002","url":null,"abstract":"Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has shown potential as a cost-effective alternative for rapid diagnosis. In this study, we review the current state of machine learning in the detection and prediction of epileptic seizures. The objective of this study is to portray the existing machine learning methods for seizure prediction. Internet bibliographical searches were conducted to identify relevant literature on the topic. Through cross-referencing from key articles, additional references were obtained to provide a comprehensive overview of the techniques. As the aim of this paper aims is not a pure bibliographical review of the subject, the publications here cited have been selected among many others based on their number of citations. To implement accurate diagnostic and treatment tools, it is necessary to achieve a balance between prediction time, sensitivity, and specificity. This balance can be achieved using deep learning algorithms. The best performance and results are often achieved by combining multiple techniques and features, but this approach can also increase computational requirements.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10777364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138805520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0