Journal of Integrative Bioinformatics最新文献

{"title":"Co-creation environment with cloud virtual reality and real-time artificial intelligence toward the design of molecular robots.","authors":"Akihiko Konagaya, Gregory Gutmann, Yuhui Zhang","doi":"10.1515/jib-2022-0017","DOIUrl":"10.1515/jib-2022-0017","url":null,"abstract":"<p><p>This paper describes the design philosophy for our cloud-based virtual reality (VR) co-creation environment (CCE) for molecular modeling. Using interactive VR simulation can provide enhanced perspectives in molecular modeling for intuitive live demonstration and experimentation in the CCE. Then the use of the CCE can enhance knowledge creation by bringing people together to share and create ideas or knowledge that may not emerge otherwise. Our prototype CCE discussed here, which was developed to demonstrate our design philosophy, has already enabled multiple members to log in and touch virtual molecules running on a cloud server with no noticeable network latency via real-time artificial intelligence techniques. The CCE plays an essential role in the rational design of molecular robot parts, which consist of bio-molecules such as DNA and protein molecules.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9271825","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}

{"title":"<i>In silico</i> studies of natural product-like caffeine derivatives as potential MAO-B inhibitors/AA_2AR antagonists for the treatment of Parkinson's disease.","authors":"Yassir Boulaamane, Mahmoud A A Ibrahim, Mohammed Reda Britel, Amal Maurady","doi":"10.1515/jib-2021-0027","DOIUrl":"10.1515/jib-2021-0027","url":null,"abstract":"<p><p>Parkinson's disease is considered the second most frequent neurodegenerative disease. It is described by the loss of dopaminergic neurons in the mid-brain. For many decades, L-DOPA has been considered as the gold standard for treating Parkinson's disease motor symptoms, however, due to the decrease of efficacy, in the long run, there is an urgent need for novel antiparkinsonian drugs. Caffeine derivatives have been reported several times for their neuroprotective properties and dual blockade of monoamine oxidase (MAO) and adenosine A_2A receptors (AA_2AR). Natural products are currently attracting more focus due to structural diversity and safety in contrast to synthetic drugs. In the present work, computational studies were conducted on natural product-like caffeine derivatives to search for novel potent candidates acting as dual MAO-B inhibitors/AA_2AR antagonists for Parkinson's disease. Our findings revealed two natural products among the top hits: CNP0202316 and CNP0365210 fulfill the requirements of drugs acting on the brain. The selected lead compounds were further studied using molecular dynamics simulation to assess their stability with MAO-B. Current findings might shift the interest towards natural-based compounds and could be exploited to further optimize caffeine derivatives into a successful dual-target-directed drug for managing and halting the neuronal damage in Parkinson's disease patients.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9462328","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}

{"title":"Bridging data management platforms and visualization tools to enable ad-hoc and smart analytics in life sciences.","authors":"Christian Panse, Christian Trachsel, Can Türker","doi":"10.1515/jib-2022-0031","DOIUrl":"10.1515/jib-2022-0031","url":null,"abstract":"<p><p>Core facilities have to offer technologies that best serve the needs of their users and provide them a competitive advantage in research. They have to set up and maintain instruments in the range of ten to a hundred, which produce large amounts of data and serve thousands of active projects and customers. Particular emphasis has to be given to the reproducibility of the results. More and more, the entire process from building the research hypothesis, conducting the experiments, doing the measurements, through the data explorations and analysis is solely driven by very few experts in various scientific fields. Still, the ability to perform the entire data exploration in real-time on a personal computer is often hampered by the heterogeneity of software, the data structure formats of the output, and the enormous data sizes. These impact the design and architecture of the implemented software stack. At the Functional Genomics Center Zurich (FGCZ), a joint state-of-the-art research and training facility of ETH Zurich and the University of Zurich, we have developed the B-Fabric system, which has served for more than a decade, an entire life sciences community with fundamental data science support. In this paper, we sketch how such a system can be used to glue together data (including metadata), computing infrastructures (clusters and clouds), and visualization software to support instant data exploration and visual analysis. We illustrate our in-daily life implemented approach using visualization applications of mass spectrometry data.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9148533","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}

{"title":"Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare - a proof of concept study for the prediction of coronary artery calcification scores.","authors":"Justus Wolff, Julian Matschinske, Dietrich Baumgart, Anne Pytlik, Andreas Keck, Arunakiry Natarajan, Claudio E von Schacky, Josch K Pauling, Jan Baumbach","doi":"10.1515/jib-2022-0032","DOIUrl":"10.1515/jib-2022-0032","url":null,"abstract":"<p><p>The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could support that is federated machine learning (FL) since it allows for privacy preserving data access. For this proof of concept, a prediction model for coronary artery calcification scores (CACS) has been applied. The FL was trained based on the data in the different institutions, while the centralized machine learning model was trained on one allocation of data. Both algorithms predict patients with risk scores ≥5 based on age, biological sex, waist circumference, dyslipidemia and HbA1c. The centralized model yields a sensitivity of c. 66% and a specificity of c. 70%. The FL slightly outperforms that with a sensitivity of 67% while slightly underperforming it with a specificity of 69%. It could be demonstrated that CACS prediction is feasible via both, a centralized and an FL approach, and that both show very comparable accuracy. In order to increase accuracy, additional and a higher volume of patient data is required and for that FL is utterly necessary. The developed \"CACulator\" serves as proof of concept, is available as research tool and shall support future research to facilitate AI implementation.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9092887","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}

{"title":"On the way to plant data commons - a genotyping use case.","authors":"Manuel Feser, Patrick König, Anne Fiebig, Daniel Arend, Matthias Lange, Uwe Scholz","doi":"10.1515/jib-2022-0033","DOIUrl":"10.1515/jib-2022-0033","url":null,"abstract":"<p><p>Over the last years it has been observed that the progress in data collection in life science has created increasing demand and opportunities for advanced bioinformatics. This includes data management as well as the individual data analysis and often covers the entire data life cycle. A variety of tools have been developed to store, share, or reuse the data produced in the different domains such as genotyping. Especially imputation, as a subfield of genotyping, requires good Research Data Management (RDM) strategies to enable use and re-use of genotypic data. To aim for sustainable software, it is necessary to develop tools and surrounding ecosystems, which are reusable and maintainable. Reusability in the context of streamlined tools can e.g. be achieved by standardizing the input and output of the different tools and adapting to open and broadly used file formats. By using such established file formats, the tools can also be connected with others, improving the overall interoperability of the software. Finally, it is important to build strong communities that maintain the tools by developing and contributing new features and maintenance updates. In this article, concepts for this will be presented for an imputation service.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9462321","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}

{"title":"Systematic assessment of template-based genome-scale metabolic models created with the BiGG Integration Tool.","authors":"Alexandre Oliveira,&nbsp;Emanuel Cunha,&nbsp;Fernando Cruz,&nbsp;João Capela,&nbsp;João C Sequeira,&nbsp;Marta Sampaio,&nbsp;Cláudia Sampaio,&nbsp;Oscar Dias","doi":"10.1515/jib-2022-0014","DOIUrl":"https://doi.org/10.1515/jib-2022-0014","url":null,"abstract":"<p><p>Genome-scale metabolic models (GEMs) are essential tools for <i>in silico</i> phenotype prediction and strain optimisation. The most straightforward GEMs reconstruction approach uses published models as templates to generate the initial draft, requiring further curation. Such an approach is used by BiGG Integration Tool (BIT), available for <i>merlin</i> users. This tool uses models from BiGG Models database as templates for the draft models. Moreover, BIT allows the selection between different template combinations. The main objective of this study is to assess the draft models generated using this tool and compare them BIT, comparing these to CarveMe models, both of which use the BiGG database, and curated models. For this, three organisms were selected, namely <i>Streptococcus thermophilus</i>, <i>Xylella fastidiosa</i> and <i>Mycobacterium tuberculosis.</i> The models' variability was assessed using reactions and genes' metabolic functions. This study concluded that models generated with BIT for each organism were differentiated, despite sharing a significant portion of metabolic functions. Furthermore, the template seems to influence the content of the models, though to a lower extent. When comparing each draft with curated models, BIT had better performances than CarveMe in all metrics. Hence, BIT can be considered a fast and reliable alternative for draft reconstruction for bacteria models.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9521827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40344291","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}

{"title":"A device and an app for the diagnosis and self-management of tinnitus.","authors":"Pierpaolo Vittorini,&nbsp;Pablo Chamoso,&nbsp;Fernando De la Prieta","doi":"10.1515/jib-2022-0004","DOIUrl":"https://doi.org/10.1515/jib-2022-0004","url":null,"abstract":"<p><p>Tinnitus is an annoying ringing in the ears, in varying shades and intensities. Tinnitus can affect a person's overall health and social well-being (e.g., sleep problems, trouble concentrating, anxiety, depression and inability to work). The diagnostic procedure of tinnitus usually consists of three steps: an audiological examination, psychoacoustic measurement, and a disability evaluation. All steps are performed by physicians, who use specialised hardware/software and administer questionnaires. This paper presents a system, to be used by patients, for the diagnosis and self-management of tinnitus. The system is made up of an app and a device. The app is responsible for executing - through the device - a part of the required audiological and psychoacoustic examinations, as well as administering questionnaires that evaluate disability. The paper reviews the quality of the automated audiometric reporting and the user experience provided by the app. Descriptive and inferential statistics were used to support the findings. The results show that automated reporting is comparable with that of physicians and that user experience was improved by re-designing and re-developing the acufenometry of the app. As for the user experience, two experts in Human-Computer Interaction evaluated the first version of the app: their agreement was good (Cohen's <i>K</i> = 0.639) and the average rating of the app was 1.43/2. Also patients evaluated the app in its initial version: the satisfactory tasks (audiometry and questionnaires) were rated as 4.31/5 and 4.65/5. The unsatisfactory task (acufenometry) was improved and the average rating increased from 2.86/5 to 3.96/5 (<i>p</i> = 0.0005). Finally, the general usability of the app was increased from the initial value of 73.6/100 to 85.4/100 (<i>p</i> = 0.0003). The strengths of the project are twofold. Firstly, the automated reporting feature, which - to the best of our knowledge - is the first attempt in this area. Secondly, the overall app usability, which was evaluated and improved during its development. In summary, the conclusion drawn from the conducted project is that the system works as expected, and despite some weaknesses, also the replication of the device would not be expensive, and it can be used in different scenarios.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 3","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33447017","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}

{"title":"KaIDA: a modular tool for assisting image annotation in deep learning.","authors":"Marcel P Schilling, Svenja Schmelzer, Lukas Klinger, Markus Reischl","doi":"10.1515/jib-2022-0018","DOIUrl":"10.1515/jib-2022-0018","url":null,"abstract":"<p><p>Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehensive tool for assistance which is time- consuming, burdensome, and not intuitive. Using the here presented modular Karlsruhe Image Data Annotation (KaIDA) tool, for the first time assisted annotation in various image processing tasks is possible to support users during this process. It aims to simplify annotation, increase user efficiency, enhance annotation quality, and provide additional useful annotation-related functionalities. KaIDA is available open-source at https://git.scc.kit.edu/sc1357/kaida.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9094039","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}

{"title":"Evaluating molecular representations in machine learning models for drug response prediction and interpretability.","authors":"Delora Baptista, João Correia, Bruno Pereira, Miguel Rocha","doi":"10.1515/jib-2022-0006","DOIUrl":"10.1515/jib-2022-0006","url":null,"abstract":"<p><p>Machine learning (ML) is increasingly being used to guide drug discovery processes. When applying ML approaches to chemical datasets, molecular descriptors and fingerprints are typically used to represent compounds as numerical vectors. However, in recent years, end-to-end deep learning (DL) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. This study set out to investigate which compound representation methods are the most suitable for drug sensitivity prediction in cancer cell lines. Twelve different representations were benchmarked on 5 compound screening datasets, using DeepMol, a new chemoinformatics package developed by our research group, to perform these analyses. The results of this study show that the predictive performance of end-to-end DL models is comparable to, and at times surpasses, that of models trained on molecular fingerprints, even when less training data is available. This study also found that combining several compound representation methods into an ensemble can improve performance. Finally, we show that a <i>post hoc</i> feature attribution method can boost the explainability of the DL models.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9521826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33438674","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}

{"title":"Design X Bioinformatics: a community-driven initiative to connect bioinformatics and design.","authors":"Björn Sommer,&nbsp;Daisuke Inoue,&nbsp;Marc Baaden","doi":"10.1515/jib-2022-0037","DOIUrl":"https://doi.org/10.1515/jib-2022-0037","url":null,"abstract":"<p><p>Bioinformatics applies computer science approaches to the analysis of biological data. It is widely known for its genomics-based analysis approaches that have supported, for example, the 1000 Genomes Project. In addition, bioinformatics relates to many other areas, such as analysis of microscopic images (e.g., organelle localization), molecular modelling (e.g., proteins, biological membranes), and visualization of biological networks (e.g., protein-protein interaction networks, metabolism). Design is a highly interdisciplinary field that incorporates aspects such as aesthetic, economic, functional, philosophical, and/or socio-political considerations into the creative process and is usually determined by context. While visualization plays a critical role in bioinformatics, as reflected in a number of conferences and workshops in the field, design in bioinformatics-related research contexts in particular is not as well studied. With this special issue in conjunction with an international workshop, we aim to bring together bioinformaticians from different fields with designers, design researchers, and medical and scientific illustrators to discuss future challenges in the context of bioinformatics and design.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40527885","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}