Journal of Integrative Bioinformatics最新文献

A roadmap for a middleware as a federation service for integrative data retrieval of agricultural data. 作为联盟服务的中间件路线图，用于农业数据的综合数据检索。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-11-07 DOI: 10.1515/jib-2024-0027

Jorge García Brizuela, Carsten Scharfenberg, Carmen Scheuner, Florian Hoedt, Patrick König, Angela Kranz, Antonia Leidel, Daniel Martini, Gabriel Schneider, Julian Schneider, Lea Sophie Singson, Harald von Waldow, Nils Wehrmeyer, Björn Usadel, Stephan Lesch, Xenia Specka, Matthias Lange, Daniel Arend

{"title":"A roadmap for a middleware as a federation service for integrative data retrieval of agricultural data.","authors":"Jorge García Brizuela, Carsten Scharfenberg, Carmen Scheuner, Florian Hoedt, Patrick König, Angela Kranz, Antonia Leidel, Daniel Martini, Gabriel Schneider, Julian Schneider, Lea Sophie Singson, Harald von Waldow, Nils Wehrmeyer, Björn Usadel, Stephan Lesch, Xenia Specka, Matthias Lange, Daniel Arend","doi":"10.1515/jib-2024-0027","DOIUrl":"https://doi.org/10.1515/jib-2024-0027","url":null,"abstract":"Agriculture is confronted with several challenges such as climate change, the loss of biodiversity and stagnating productivity. The massive increasing amount of data and new digital technologies promise to overcome them, but they necessitate careful data integration and data management to make them usable. The FAIRagro consortium is part of the National Research Data Infrastructure (NFDI) in Germany and will develop FAIR compliant infrastructure services for the agrosystems science community, which will be integrated in the existing research data infrastructure service landscape. Here we present the initial steps of designing and implementing the FAIRagro middleware infrastructure to connect existing data infrastructures. The middleware will feature services for the seamless data integration across diverse infrastructures. Data and metadata are streamlined for research in agrosystems science by downstream processing in the central FAIRagro Search and Inventory Portal and the data integration and analysis workflow system \"SciWIn\".","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142585141","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

International symposium on integrative bioinformatics 2024 - editorial. 2024 年综合生物信息学国际研讨会--社论。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-11-07 DOI: 10.1515/jib-2024-0051

Can Türker, Christian Panse, Bjorn Sommer, Marcel Friedrichs, Ralf Hofestädt

引用次数: 0

The potential of Mitragyna speciosa leaves as a natural source of antioxidants for disease prevention. Mitragyna speciosa 叶子作为天然抗氧化剂预防疾病的潜力。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-09-17 DOI: 10.1515/jib-2023-0030

Ihsanul Arief, Gagus Ketut Sunnardianto, Syahrul Khairi, Wahyu Dita Saputri

{"title":"The potential of Mitragyna speciosa leaves as a natural source of antioxidants for disease prevention.","authors":"Ihsanul Arief, Gagus Ketut Sunnardianto, Syahrul Khairi, Wahyu Dita Saputri","doi":"10.1515/jib-2023-0030","DOIUrl":"https://doi.org/10.1515/jib-2023-0030","url":null,"abstract":"Mitragyna speciosa is famous for its addictive effect. On the other hand, this plant has good potential as an antioxidant agent, and so far, it was not explicitly explained what the most contributing compound in the leaves to that activity is. This study has been conducted using several computational methods to determine which compounds are the most active in interacting with cytochrome P450, myeloperoxidase, and NADPH oxidase proteins. First, virtual screening was carried out based on molecular docking, followed by profiling the properties of adsorption, distribution, metabolism, excretion, and toxicity (ADMET); the second one is the molecular dynamics (MD) simulations for 100 ns. The virtual screening results showed that three compounds acted as inhibitors for each protein: (-)-epicatechin, sitogluside, and corynoxeine. The ADMET profiles of the three compounds exhibit good drug ability and toxicity. The trajectories study from MD simulations predicts that the complexes of these three compounds with their respective target proteins are stable. Furthermore, these compounds identified in this computational study can be a potential guide for future experiments aimed at assessing the antioxidant properties through in vitro testing.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300772","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

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction. MCMVDRP：用于癌症药物反应预测的多通道多视角深度学习框架。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-09-02 DOI: 10.1515/jib-2024-0026

Xiangyu Li, Xiumin Shi, Yuxuan Li, Lu Wang

{"title":"MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.","authors":"Xiangyu Li, Xiumin Shi, Yuxuan Li, Lu Wang","doi":"10.1515/jib-2024-0026","DOIUrl":"https://doi.org/10.1515/jib-2024-0026","url":null,"abstract":"Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often results in divergent therapeutic responses to analogous anti-cancer drug treatments within the same cohort of cancer patients. Hence, predicting the drug response by analysing the genomic profile characteristics of individual patients holds significant research importance. With the notable progress in machine learning and deep learning, many effective methods have emerged for predicting drug responses utilizing features from both drugs and cell lines. However, these methods are inadequate in capturing a sufficient number of features inherent to drugs. Consequently, we propose a representational approach for drugs that incorporates three distinct types of features: the molecular graph, the SMILE strings, and the molecular fingerprints. In this study, a novel deep learning model, named MCMVDRP, is introduced for the prediction of cancer drug responses. In our proposed model, an amalgamation of these extracted features is performed, followed by the utilization of fully connected layers to predict the drug response based on the IC50 values. Experimental results demonstrate that the presented model outperforms current state-of-the-art models in performance.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141758","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

Leonhard Med, a trusted research environment for processing sensitive research data. Leonhard Med，一个用于处理敏感研究数据的可信研究环境。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-08-02 DOI: 10.1515/jib-2024-0021

Michal J Okoniewski, Anna Wiegand, Diana Coman Schmid, Christian Bolliger, Cristian Bovino, Mattia Belluco, Thomas Wüst, Olivier Byrde, Sergio Maffioletti, Bernd Rinn

{"title":"Leonhard Med, a trusted research environment for processing sensitive research data.","authors":"Michal J Okoniewski, Anna Wiegand, Diana Coman Schmid, Christian Bolliger, Cristian Bovino, Mattia Belluco, Thomas Wüst, Olivier Byrde, Sergio Maffioletti, Bernd Rinn","doi":"10.1515/jib-2024-0021","DOIUrl":"https://doi.org/10.1515/jib-2024-0021","url":null,"abstract":"This paper provides an overview of the development and operation of the Leonhard Med Trusted Research Environment (TRE) at ETH Zurich. Leonhard Med gives scientific researchers the ability to securely work on sensitive research data. We give an overview of the user perspective, the legal framework for processing sensitive data, design history, current status, and operations. Leonhard Med is an efficient, highly secure Trusted Research Environment for data processing, hosted at ETH Zurich and operated by the Scientific IT Services (SIS) of ETH. It provides a full stack of security controls that allow researchers to store, access, manage, and process sensitive data according to Swiss legislation and ETH Zurich Data Protection policies. In addition, Leonhard Med fulfills the BioMedIT Information Security Policies and is compatible with international data protection laws and therefore can be utilized within the scope of national and international collaboration research projects. Initially designed as a \"bare-metal\" High-Performance Computing (HPC) platform to achieve maximum performance, Leonhard Med was later re-designed as a virtualized, private cloud platform to offer more flexibility to its customers. Sensitive data can be analyzed in secure, segregated spaces called tenants. Technical and Organizational Measures (TOMs) are in place to assure the confidentiality, integrity, and availability of sensitive data. At the same time, Leonhard Med ensures broad access to cutting-edge research software, especially for the analysis of human -omics data and other personalized health applications.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876714","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

Exploring animal behaviour multilayer networks in immersive environments - a conceptual framework. 探索身临其境环境中的动物行为多层网络--一个概念框架。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-24 DOI: 10.1515/jib-2024-0022

Stefan Paul Feyer, Bruno Pinaud, Karsten Klein, Etienne Lein, Falk Schreiber

引用次数: 0

Inferences on the evolution of the ascorbic acid synthesis pathway in insects using Phylogenetic Tree Collapser (PTC), a tool for the automated collapsing of phylogenetic trees using taxonomic information. 利用系统发生树折叠器（PTC）推断昆虫抗坏血酸合成途径的进化，PTC 是一种利用分类信息自动折叠系统发生树的工具。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-24 eCollection Date: 2024-06-01 DOI: 10.1515/jib-2023-0051

Daniel Glez-Peña, Hugo López-Fernández, Pedro Duque, Cristina P Vieira, Jorge Vieira

{"title":"Inferences on the evolution of the ascorbic acid synthesis pathway in insects using Phylogenetic Tree Collapser (PTC), a tool for the automated collapsing of phylogenetic trees using taxonomic information.","authors":"Daniel Glez-Peña, Hugo López-Fernández, Pedro Duque, Cristina P Vieira, Jorge Vieira","doi":"10.1515/jib-2023-0051","DOIUrl":"10.1515/jib-2023-0051","url":null,"abstract":"When inferring the evolution of a gene/gene family, it is advisable to use all available coding sequences (CDS) from as many species genomes as possible in order to infer and date all gene duplications and losses. Nowadays, this means using hundreds or even thousands of CDSs, which makes the inferred phylogenetic trees difficult to visualize and interpret. Therefore, it is useful to have an automated way of collapsing large phylogenetic trees according to a taxonomic term decided by the user (family, class, or order, for instance), in order to highlight the minimal set of sequences that should be used to recapitulate the full history of the gene/gene family being studied at that taxonomic level, that can be refined using additional software. Here we present the Phylogenetic Tree Collapser (PTC) program (https://github.com/pegi3s/phylogenetic-tree-collapser), a flexible tool for automated tree collapsing using taxonomic information, that can be easily used by researchers without a background in informatics, since it only requires the installation of Docker, Podman or Singularity. The utility of PTC is demonstrated by addressing the evolution of the ascorbic acid synthesis pathway in insects. A Docker image is available at Docker Hub (https://hub.docker.com/r/pegi3s/phylogenetic-tree-collapser) with PTC installed and ready-to-run.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11377030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762605","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

Specifications of standards in systems and synthetic biology: status, developments, and tools in 2024. 系统和合成生物学标准规范：2024 年的现状、发展和工具。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-22 eCollection Date: 2024-03-01 DOI: 10.1515/jib-2024-0015

Martin Golebiewski, Gary Bader, Padraig Gleeson, Thomas E Gorochowski, Sarah M Keating, Matthias König, Chris J Myers, David P Nickerson, Björn Sommer, Dagmar Waltemath, Falk Schreiber

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":null,"pages":null},"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 DOI: 10.1515/jib-2024-0023

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

引用次数: 0