Journal of Integrative Bioinformatics最新文献_第4页

Constructing networks for comparison of collagen types. 构建比较胶原类型的网络。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-15 eCollection Date: 2024-09-01 DOI: 10.1515/jib-2024-0020

Valentin Wesp, Lukas Scholz, Janine M Ziermann-Canabarro, Stefan Schuster, Heiko Stark

{"title":"Constructing networks for comparison of collagen types.","authors":"Valentin Wesp, Lukas Scholz, Janine M Ziermann-Canabarro, Stefan Schuster, Heiko Stark","doi":"10.1515/jib-2024-0020","DOIUrl":"10.1515/jib-2024-0020","url":null,"abstract":"Collagens are structural proteins that are predominantly found in the extracellular matrix of multicellular animals, where they are mainly responsible for the stability and structural integrity of various tissues. All collagens contain polypeptide strands (α-chains). There are several types of collagens, some of which differ significantly in form, function, and tissue specificity. Because of their importance in clinical research, they are grouped into subdivisions, the so-called collagen families, and their sequences are often analysed. However, problems arise with highly homologous sequence segments. To increase the accuracy of collagen classification and prediction of their functions, the structure of these collagens and their expression in different tissues could result in a better focus on sequence segments of interest. Here, we analyse collagen families with different levels of conservation. As a result, clusters with high interconnectivity can be found, such as the fibrillar collagens, the COL4 network-forming collagens, and the COL9 FACITs. Furthermore, a large cluster between network-forming, FACIT, and COL28a1 α-chains is formed with COL6a3 as a major hub node. The formation of clusters also signifies, why it is important to always analyse the α-chains and why structural changes can have a wide range of effects on the body.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602166","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

Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction. 检测病例对照队列中的异常值，改进深度学习网络对精神分裂症的预测。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-15 eCollection Date: 2024-06-01 DOI: 10.1515/jib-2023-0042

Daniel Martins, Maryam Abbasi, Conceição Egas, Joel P Arrais

{"title":"Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction.","authors":"Daniel Martins, Maryam Abbasi, Conceição Egas, Joel P Arrais","doi":"10.1515/jib-2023-0042","DOIUrl":"10.1515/jib-2023-0042","url":null,"abstract":"This study delves into the intricate genetic and clinical aspects of Schizophrenia, a complex mental disorder with uncertain etiology. Deep Learning (DL) holds promise for analyzing large genomic datasets to uncover new risk factors. However, based on reports of non-negligible misdiagnosis rates for SCZ, case-control cohorts may contain outlying genetic profiles, hindering compelling performances of classification models. The research employed a case-control dataset sourced from the Swedish populace. A gene-annotation-based DL architecture was developed and employed in two stages. First, the model was trained on the entire dataset to highlight differences between cases and controls. Then, samples likely to be misclassified were excluded, and the model was retrained on the refined dataset for performance evaluation. The results indicate that SCZ prevalence and misdiagnosis rates can affect case-control cohorts, potentially compromising future studies reliant on such datasets. However, by detecting and filtering outliers, the study demonstrates the feasibility of adapting DL methodologies to large-scale biological problems, producing results more aligned with existing heritability estimates for SCZ. This approach not only advances the comprehension of the genetic background of SCZ but also opens doors for adapting DL techniques in complex research for precision medicine in mental health.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11377398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617597","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

Layout of anatomical structures and blood vessels based on the foundational model of anatomy. 根据解剖学基础模型，布局解剖结构和血管。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-12 eCollection Date: 2024-09-01 DOI: 10.1515/jib-2024-0023

Niklas Gröne, Benjamin Grüneisen, Karsten Klein, Bernard de Bono, Tobias Czauderna, Falk Schreiber

引用次数: 0

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":" ","pages":""},"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":" ","pages":""},"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

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":" ","pages":""},"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":" ","pages":""},"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