Journal of Integrative Bioinformatics最新文献

Immunoinformatics-guided design of a multiepitope peptide vaccine targeting the receptor-binding domain of SARS-CoV-2 spike glycoprotein: insights from Indonesian samples. 免疫信息学指导下针对SARS-CoV-2刺突糖蛋白受体结合域的多表位肽疫苗设计：来自印度尼西亚样本的见解

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2025-01-14 DOI: 10.1515/jib-2024-0025

Irvan Faizal, Darrian Chandra, Tarwadi, Sabar Pambudi, Astutiati Nurhasanah, Rizky Priambodo, Muhammad Yusuf

{"title":"Immunoinformatics-guided design of a multiepitope peptide vaccine targeting the receptor-binding domain of SARS-CoV-2 spike glycoprotein: insights from Indonesian samples.","authors":"Irvan Faizal, Darrian Chandra, Tarwadi, Sabar Pambudi, Astutiati Nurhasanah, Rizky Priambodo, Muhammad Yusuf","doi":"10.1515/jib-2024-0025","DOIUrl":"https://doi.org/10.1515/jib-2024-0025","url":null,"abstract":"The emergence of new variants of SARS-CoV-2, including Alpha, Beta, Gamma, Delta, Omicron variants, and XBB sub-variants, contributes to the number of coronavirus cases worldwide. SARS-CoV-2 is a positive RNA virus with a genome of 29.9 kb that encodes four structural proteins: spike glycoprotein (S), envelope glycoprotein (E), membrane glycoprotein (M), and nucleocapsid glycoprotein (N). These proteins are vital for viral activity, with the S protein facilitating attachment and membrane fusion through the receptor-binding domain (RBD) located in the S1 subunit. The RBD recognizes and binds to the human angiotensin-converting enzyme 2 (ACE-2) protein. An immunoinformatic-aided design of a peptide-based multiepitope vaccine candidate targeting the RBD glycoprotein is constructed from the SARS-CoV-2 sequence data base from various regions of Indonesia (Jakarta, West Java, and Bali). The results show that the RBD region of with accession ID EPI_ISL_15982641 from West Java had the highest antigenicity of 0.5904. This isolate is non-toxic and non-allergenic and shows a total of 18 LBL epitopes, 72 CTL epitopes, and 98 HTL epitopes. The epitope that has the best overall binding affinity was GCHNKCAY for MHC-I and GGCVFSYVGCHNKCAYWV for MHC-II which show a binding affinity of -13.6 and -15.5 (kcal/mol), respectively. Therefore, this study aims to design an epitope vaccine candidate based on samples from Indonesia that has good characteristics and may have the potential to stimulate an immune response against SARS-CoV-2.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973272","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

MiRNA target enrichment analysis of co-expression network modules reveals important miRNAs and their roles in breast cancer progression. 共表达网络模块的MiRNA靶富集分析揭示了重要的MiRNA及其在乳腺癌进展中的作用。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-12-25 eCollection Date: 2024-12-01 DOI: 10.1515/jib-2022-0036

Mohammad Javad Bazyari, Seyed Hamid Aghaee-Bakhtiari

{"title":"MiRNA target enrichment analysis of co-expression network modules reveals important miRNAs and their roles in breast cancer progression.","authors":"Mohammad Javad Bazyari, Seyed Hamid Aghaee-Bakhtiari","doi":"10.1515/jib-2022-0036","DOIUrl":"10.1515/jib-2022-0036","url":null,"abstract":"Breast cancer has the highest incidence and is the fifth cause of death in cancers. Progression is one of the important features of breast cancer which makes it a life-threatening cancer. MicroRNAs are small RNA molecules that have pivotal roles in the regulation of gene expression and they control different properties in breast cancer such as progression. Recently, systems biology offers novel approaches to study complicated biological systems like miRNAs to find their regulatory roles. One of these approaches is analysis of weighted co-expression network in which genes with similar expression patterns are considered as a single module. Because the genes in one module have similar expression, it is rational to think the same regulatory elements such as miRNAs control their expression. Herein, we use WGCNA to find important modules related to breast cancer progression and use hypergeometric test to perform miRNA target enrichment analysis and find important miRNAs. Also, we use negative correlation between miRNA expression and modules as the second filter to ensure choosing the right candidate miRNAs regarding to important modules. We found hsa-mir-23b, hsa-let-7b and hsa-mir-30a are important miRNAs in breast cancer and also investigated their roles in breast cancer progression.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883636","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

Exploring the therapeutic potential of Asparagus africanus in polycystic ovarian syndrome: a computational analysis. 探索非洲芦笋治疗多囊卵巢综合征的潜力：计算分析。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-12-12 eCollection Date: 2024-12-01 DOI: 10.1515/jib-2024-0019

Sania Riaz, Fatima Haider, Rizwan- Ur-Rehman, Aqsa Zafar

{"title":"Exploring the therapeutic potential of Asparagus africanus in polycystic ovarian syndrome: a computational analysis.","authors":"Sania Riaz, Fatima Haider, Rizwan- Ur-Rehman, Aqsa Zafar","doi":"10.1515/jib-2024-0019","DOIUrl":"10.1515/jib-2024-0019","url":null,"abstract":"PCOS is a multifaceted condition characterized by ovarian abnormalities, metabolic disorders, anovulation, and hormonal imbalances. In response to the growing demand for treatments with fewer side effects, the exploration of herbal-origin drugs has gained prominence. Asparagus africanus, a traditional medicinal plant that exhibits anti-inflammatory, antioxidant, and anti-androgenic properties may have a cure for PCOS. The plant has rich biochemical profile prompted its exploration as a potential source for drug development. The aim of this study is to investigate the potential therapeutic efficacy of A. africanus in the management of PCOS through molecular docking studies with Luteinizing Hormone Receptor and Follicle-Stimulating Hormone Receptor proteins. The identified compounds underwent molecular docking against key proteins associated with PCOS, namely Luteinizing Hormone Receptor and Follicle-Stimulating Hormone Receptor. The results underscored the lead compound's superiority, demonstrating favorable pharmacokinetics, ADME characteristics, and strong molecular binding without any observed toxicity in comparison to standard drug. This study, by leveraging natural compounds sourced from A. africanus, provides valuable insights and advances towards developing more effective and safer treatments for PCOS. The findings contribute to the evolving landscape of PCOS therapeutics, emphasizing the potential of herbal-origin drugs in mitigating the complexities of this syndrome.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808595","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

TREMSUCS-TCGA - an integrated workflow for the identification of biomarkers for treatment success. TREMSUCS-TCGA -用于鉴定治疗成功的生物标志物的集成工作流程。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-12-11 eCollection Date: 2024-12-01 DOI: 10.1515/jib-2024-0031

Gabor Balogh, Natasha Jorge, Célia Dupain, Maud Kamal, Nicolas Servant, Christophe Le Tourneau, Peter F Stadler, Stephan H Bernhart

{"title":"TREMSUCS-TCGA - an integrated workflow for the identification of biomarkers for treatment success.","authors":"Gabor Balogh, Natasha Jorge, Célia Dupain, Maud Kamal, Nicolas Servant, Christophe Le Tourneau, Peter F Stadler, Stephan H Bernhart","doi":"10.1515/jib-2024-0031","DOIUrl":"10.1515/jib-2024-0031","url":null,"abstract":"Many publicly available databases provide disease related data, that makes it possible to link genomic data to medical and meta-data. The cancer genome atlas (TCGA), for example, compiles tens of thousand of datasets covering a wide array of cancer types. Here we introduce an interactive and highly automatized TCGA-based workflow that links and analyses epigenomic and transcriptomic data with treatment and survival data in order to identify possible biomarkers that indicate treatment success. TREMSUCS-TCGA is flexible with respect to type of cancer and treatment and provides standard methods for differential expression analysis or DMR detection. Furthermore, it makes it possible to examine several cancer types together in a pan-cancer type approach. Parallelisation and reproducibility of all steps is ensured with the workflowmanagement system Snakemake. TREMSUCS-TCGA produces a comprehensive single report file which holds all relevant results in descriptive and tabular form that can be explored in an interactive manner. As a showcase application we describe a comprehensive analysis of the available data for the combination of patients with squamous cell carcinomas of head and neck, cervix and lung treated with cisplatin, carboplatin and the combination of carboplatin and paclitaxel. The best ranked biomarker candidates are discussed in the light of the existing literature, indicating plausible causal relationships to the relevant cancer entities.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803005","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

Ion channel classification through machine learning and protein language model embeddings. 通过机器学习和蛋白质语言模型嵌入进行离子通道分类。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-11-25 eCollection Date: 2024-12-01 DOI: 10.1515/jib-2023-0047

Hamed Ghazikhani, Gregory Butler

{"title":"Ion channel classification through machine learning and protein language model embeddings.","authors":"Hamed Ghazikhani, Gregory Butler","doi":"10.1515/jib-2023-0047","DOIUrl":"10.1515/jib-2023-0047","url":null,"abstract":"Ion channels are critical membrane proteins that regulate ion flux across cellular membranes, influencing numerous biological functions. The resource-intensive nature of traditional wet lab experiments for ion channel identification has led to an increasing emphasis on computational techniques. This study extends our previous work on protein language models for ion channel prediction, significantly advancing the methodology and performance. We employ a comprehensive array of machine learning algorithms, including k-Nearest Neighbors, Random Forest, Support Vector Machines, and Feed-Forward Neural Networks, alongside a novel Convolutional Neural Network (CNN) approach. These methods leverage fine-tuned embeddings from ProtBERT, ProtBERT-BFD, and MembraneBERT to differentiate ion channels from non-ion channels. Our empirical findings demonstrate that TooT-BERT-CNN-C, which combines features from ProtBERT-BFD and a CNN, substantially surpasses existing benchmarks. On our original dataset, it achieves a Matthews Correlation Coefficient (MCC) of 0.8584 and an accuracy of 98.35 %. More impressively, on a newly curated, larger dataset (DS-Cv2), it attains an MCC of 0.9492 and an ROC AUC of 0.9968 on the independent test set. These results not only highlight the power of integrating protein language models with deep learning for ion channel classification but also underscore the importance of using up-to-date, comprehensive datasets in bioinformatics tasks. Our approach represents a significant advancement in computational methods for ion channel identification, with potential implications for accelerating research in ion channel biology and aiding drug discovery efforts.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689725","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

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 eCollection Date: 2024-09-01 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":"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":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142585141","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

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 eCollection Date: 2024-09-01 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 eCollection Date: 2024-12-01 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":"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":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300772","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

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 eCollection Date: 2024-09-01 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":"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":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141758","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

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 eCollection Date: 2024-09-01 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":"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":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876714","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