Nietert, Manuel Manfred

[["dc.bibliographiccitation.firstpage","1072"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Journal of Personalized Medicine"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Voskamp, Malte"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.date.accessioned","2022-01-11T14:07:52Z"],["dc.date.available","2022-01-11T14:07:52Z"],["dc.date.issued","2021"],["dc.description.abstract","The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For this purpose, we extended MINERVA’s own drug and chemical search using the MINERVA plugin starter kit. We developed a plugin to provide a linkage between disease maps in MINERVA and application-specific databases of candidate therapeutics. The plugin has three main functionalities; one shows all the targets of all the compounds in the database, the second is a compound-based search to highlight targets of specific compounds, and the third can be used to find compounds that affect a certain target. As a use case, we applied the plugin to link a disease map and compound database we previously established in the context of cystic fibrosis and, herein, point out possible issues and difficulties. The plugin is publicly available on GitLab; the use-case application to cystic fibrosis, connecting disease maps and the compound database CandActCFTR, is available online."],["dc.description.abstract","The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For this purpose, we extended MINERVA’s own drug and chemical search using the MINERVA plugin starter kit. We developed a plugin to provide a linkage between disease maps in MINERVA and application-specific databases of candidate therapeutics. The plugin has three main functionalities; one shows all the targets of all the compounds in the database, the second is a compound-based search to highlight targets of specific compounds, and the third can be used to find compounds that affect a certain target. As a use case, we applied the plugin to link a disease map and compound database we previously established in the context of cystic fibrosis and, herein, point out possible issues and difficulties. The plugin is publicly available on GitLab; the use-case application to cystic fibrosis, connecting disease maps and the compound database CandActCFTR, is available online."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/jpm11111072"],["dc.identifier.pii","jpm11111072"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97883"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-507"],["dc.relation.eissn","2075-4426"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Mapping Compound Databases to Disease Maps—A MINERVA Plugin for CandActBase"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1278"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","Biomolecules"],["dc.bibliographiccitation.volume","12"],["dc.contributor.affiliation","Voskamp, Malte; 1Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany"],["dc.contributor.affiliation","Vinhoven, Liza; 1Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany"],["dc.contributor.affiliation","Stanke, Frauke; 2Clinic for Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany"],["dc.contributor.affiliation","Hafkemeyer, Sylvia; 4Mukoviszidose Institut gGmbH, In den Dauen 6, 53117 Bonn, Germany"],["dc.contributor.affiliation","Nietert, Manuel Manfred; 1Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany"],["dc.contributor.author","Voskamp, Malte"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Stanke, Frauke"],["dc.contributor.author","Hafkemeyer, Sylvia"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.contributor.editor","Song, Jiangning"],["dc.date.accessioned","2022-10-04T10:21:49Z"],["dc.date.available","2022-10-04T10:21:49Z"],["dc.date.issued","2022"],["dc.date.updated","2022-11-11T13:13:13Z"],["dc.description.abstract","An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.doi","10.3390/biom12091278"],["dc.identifier.pii","biom12091278"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/114511"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-600"],["dc.publisher","MDPI"],["dc.relation.eissn","2218-273X"],["dc.rights","CC BY-SA 4.0"],["dc.title","Integrating Text Mining into the Curation of Disease Maps"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","12351"],["dc.bibliographiccitation.firstpage","12351"],["dc.bibliographiccitation.issue","20"],["dc.bibliographiccitation.journal","International Journal of Molecular Sciences"],["dc.bibliographiccitation.volume","23"],["dc.contributor.affiliation","Vinhoven, Liza; 1Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany"],["dc.contributor.affiliation","Stanke, Frauke; 2Clinic for Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany"],["dc.contributor.affiliation","Hafkemeyer, Sylvia; 4Mukoviszidose Institut, In den Dauen 6, 53117 Bonn, Germany"],["dc.contributor.affiliation","Nietert, Manuel Manfred; 1Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Stanke, Frauke"],["dc.contributor.author","Hafkemeyer, Sylvia"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.contributor.editor","Guerrini, Gabriella"],["dc.contributor.editor","Giovannoni, Maria P."],["dc.date.accessioned","2022-12-01T08:31:41Z"],["dc.date.available","2022-12-01T08:31:41Z"],["dc.date.issued","2022"],["dc.date.updated","2022-11-11T13:12:17Z"],["dc.description.abstract","Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target–compound pairings and were able to suggest targets for all 309 active compounds in the database."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft DFG"],["dc.identifier.doi","10.3390/ijms232012351"],["dc.identifier.pii","ijms232012351"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118235"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-621"],["dc.publisher","MDPI"],["dc.relation.eissn","1422-0067"],["dc.relation.isreplacedby","hdl:2/118235"],["dc.rights","Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)."],["dc.title","Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1463"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","International Journal of Colorectal Disease"],["dc.bibliographiccitation.lastpage","1469"],["dc.bibliographiccitation.volume","32"],["dc.contributor.author","Lowes, Markus"],["dc.contributor.author","Kleiss, Mathias"],["dc.contributor.author","Lueck, Rainer"],["dc.contributor.author","Detken, Sven"],["dc.contributor.author","König, Alexander Otto"],["dc.contributor.author","Nietert, Manuel M."],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Stanek, Kathrin"],["dc.contributor.author","Langer, Claus"],["dc.contributor.author","Ghadimi, Michael B."],["dc.contributor.author","Conradi, Lena-Christin"],["dc.contributor.author","Homayounfar, Kia"],["dc.date.accessioned","2019-07-09T11:44:24Z"],["dc.date.available","2019-07-09T11:44:24Z"],["dc.date.issued","2017"],["dc.description.abstract","PURPOSE: Multidisciplinary tumor boards (MDT) have been advocated as standard of care in modern oncology. German guidelines for metastasized colorectal cancer (mCRC) recommend MDT discussion of colon cancer patients after completion of primary tumor therapy but stage IV colon cancer as well as rectal cancer patients prior to any therapy. In this health care research study, we evaluated application and decisional consequences of this approach in clinical routine. METHODS: All major institutions providing oncological care in southern Lower Saxony and Northern Hesse (N = 11) were invited. Patients with mCRC diagnosed between 01/2011 and 12/2013 were eligible. Data were collected using a standardized patient report form and stored in a GCP-conform EDC-system (secuTrial®). RESULTS: A university medical center, four teaching hospitals, one communal hospital, and three oncological focus practices participated in the study. In total, 470 patients with a median age of 70 years were enrolled. Guideline conform MDT discussion was performed in 63% of operated colon cancer patients, 38% of stage IV colon cancer patients and 47% of rectal cancer patients, respectively. Resection of metastases was performed in 41% of cases. Patients ≥70 years (n = 250) received significantly more often treatment following MDT discussion (86 versus 64%, p = 0.0002). Not the resection rate (48 versus 57%, p = 0.1574) but indication for preoperative chemotherapy (57 versus 33%, p = 0.0056) significantly differed when patients with single organ metastases experienced MDT discussion. CONCLUSIONS: MDT discussion is not as established as advocated by national guidelines. Treatment decisions differ especially in older patients and those with single organ metastases."],["dc.identifier.doi","10.1007/s00384-017-2871-z"],["dc.identifier.pmid","28779354"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14738"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59003"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","The utilization of multidisciplinary tumor boards (MDT) in clinical routine: results of a health care research study focusing on patients with metastasized colorectal cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","UNSP e0117818"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Heyde, Silvia von der"],["dc.contributor.author","Wagner, Steve"],["dc.contributor.author","Czerny, Alexander"],["dc.contributor.author","Nietert, Manuel M."],["dc.contributor.author","Ludewig, Fabian"],["dc.contributor.author","Salinas-Riester, Gabriela"],["dc.contributor.author","Arlt, Dorit"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2018-11-07T10:00:48Z"],["dc.date.available","2018-11-07T10:00:48Z"],["dc.date.issued","2015"],["dc.description.abstract","Intrinsic and acquired resistance to the monoclonal antibody drug trastuzumab is a major problem in the treatment of HER2-positive breast cancer. A deeper understanding of the underlying mechanisms could help to develop new agents. Our intention was to detect genes and single nucleotide polymorphisms (SNPs) affecting trastuzumab efficiency in cell culture. Three HER2-positive breast cancer cell lines with different resistance phenotypes were analyzed. We chose BT474 as model of trastuzumab sensitivity, HCC1954 as model of intrinsic resistance, and BTR50, derived from BT474, as model of acquired resistance. Based on RNA-Seq data, we performed differential expression analyses on these cell lines with and without trastuzumab treatment. Differentially expressed genes between the resistant cell lines and BT474 are expected to contribute to resistance. Differentially expressed genes between untreated and trastuzumab treated BT474 are expected to contribute to drug efficacy. To exclude false positives from the candidate gene set, we removed genes that were also differentially expressed between untreated and trastuzumab treated BTR50. We further searched for SNPs in the untreated cell lines which could contribute to trastuzumab resistance. The analysis resulted in 54 differentially expressed candidate genes that might be connected to trastuzumab efficiency. 90% of 40 selected candidates were validated by RT-qPCR. ALPP, CALCOCO1, CAV1, CYP1A2 and IGFBP3 were significantly higher expressed in the trastuzumab treated than in the untreated BT474 cell line. GDF15, IL8, LCN2, PTGS2 and 20 other genes were significantly higher expressed in HCC1954 than in BT474, while NCAM2, COLEC12, AFF3, TFF3, NRCAM, GREB1 and TFF1 were significantly lower expressed. Additionally, we inferred SNPs in HCC1954 for CAV1, PTGS2, IL8 and IGFBP3. The latter also had a variation in BTR50. 20% of the validated subset have already been mentioned in literature. For half of them we called and analyzed SNPs. These results contribute to a better understanding of trastuzumab action and resistance mechanisms."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2015"],["dc.identifier.doi","10.1371/journal.pone.0117818"],["dc.identifier.isi","000350683900039"],["dc.identifier.pmid","25710561"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11652"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/37888"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","mRNA Profiling Reveals Determinants of Trastuzumab Efficiency in HER2-Positive Breast Cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1229"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Journal of Cancer"],["dc.bibliographiccitation.lastpage","1237"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Rühlmann, Felix"],["dc.contributor.author","Nietert, Manuel M."],["dc.contributor.author","Sprenger, Thilo"],["dc.contributor.author","Wolff, Hendrik Andreas"],["dc.contributor.author","Homayounfar, Kia"],["dc.contributor.author","Middel, Peter"],["dc.contributor.author","Bohnenberger, Hanibal"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Ghadimi, Michael B."],["dc.contributor.author","Liersch, Torsten"],["dc.contributor.author","Conradi, Lena-Christin"],["dc.date.accessioned","2018-11-07T10:28:34Z"],["dc.date.available","2018-11-07T10:28:34Z"],["dc.date.issued","2017"],["dc.description.abstract","The cellular sarcoma gene (SRC) is a proto-oncogene encoding for a tyrosine kinase. SRC expression was determined in locally advanced rectal adenocarcinoma tissue from pretreatment biopsies and resection specimens. The expression level was correlated with clinicopathological parameters to evaluate the predictive and prognostic capacity. For this monocentric analysis 186 patients with locally advanced rectal cancer (median: 63.7 years; 130 men (69.9%), 56 women (30.1%)) were included. Patients with a carcinoma of the upper third of the rectum were treated with primary tumor resection (n=27; 14.5%). All other patients received a preoperative chemoradiotherapy (CRT) with 50.4 Gy and concomitant 5-fluorouracil (5-FU) or 5-FU+oxaliplatin followed by postoperative chemotherapy with 5-FU or 5-FU+ oxaliplatin. SRC expression was determined with immunohistochemical staining from pretreatment biopsies (n=152) and residual tumor tissue from the resection specimens (n=163). The results were correlated with clinicopathological parameters and long-term follow-up. The expression of SRC was determined in pretherapeutic biopsies (mean H-Score: 229) and resection specimens (mean H-Score: 254). High SRC expression in pretherapeutic tumor samples significantly correlated with a negative postoperative nodal status (p=0.005). Furthermore an increased protein expression in residual tumor tissue was associated with fewer distant metastases (p=0.04). The overexpression of SRC in pretreatment tumor biopsies showed also a trend for a longer cancer-specific survival (CSS; p=0.05) and fewer local relapses (p=0.06) during long-term follow-up. High SRC expression in rectal cancer seems to be associated with a better long-term outcome. This finding could help in the future to stratify patients for a recurrence risk adapted postoperative treatment."],["dc.identifier.doi","10.7150/jca.16980"],["dc.identifier.isi","000402474000015"],["dc.identifier.pmid","28607598"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14945"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43450"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Ivyspring Int Publ"],["dc.relation.issn","1837-9664"],["dc.rights","CC BY-NC 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc/4.0"],["dc.title","The Prognostic Value of Tyrosine Kinase SRC Expression in Locally Advanced Rectal Cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Pharmacology"],["dc.bibliographiccitation.volume","12"],["dc.contributor.affiliation","Nietert, Manuel Manfred; \r\n\r\n1\r\nDepartment of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Vinhoven, Liza; \r\n\r\n1\r\nDepartment of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Auer, Florian; \r\n\r\n3\r\nInstitute for Informatics, University of Augsburg, Augsburg, Germany"],["dc.contributor.affiliation","Hafkemeyer, Sylvia; \r\n\r\n4\r\nMukoviszidose Institut gGmbH, Bonn, Germany"],["dc.contributor.affiliation","Stanke, Frauke; \r\n\r\n5\r\nGerman Center for Lung Research (DZL), Partner Site BREATH, Hannover, Germany"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Auer, Florian"],["dc.contributor.author","Hafkemeyer, Sylvia"],["dc.contributor.author","Stanke, Frauke"],["dc.date.accessioned","2022-01-11T14:06:16Z"],["dc.date.available","2022-01-11T14:06:16Z"],["dc.date.issued","2021"],["dc.date.updated","2022-02-09T13:20:22Z"],["dc.description.abstract","Background: Cystic fibrosis (CF) is a genetic disease caused by mutations in CFTR , which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body. Recently, some therapeutics became available that directly target dysfunctional CFTR, yet research for more effective substances is ongoing. The database CandActCFTR aims to provide detailed and comprehensive information on candidate therapeutics for the activation of CFTR-mediated ion conductance aiding systems-biology approaches to identify substances that will synergistically activate CFTR-mediated ion conductance based on published data. Results: Until 10/2020, we derived data from 108 publications on 3,109 CFTR-relevant substances via the literature database PubMed and further 666 substances via ChEMBL; only 19 substances were shared between these sources. One hundred and forty-five molecules do not have a corresponding entry in PubChem or ChemSpider, which indicates that there currently is no single comprehensive database on chemical substances in the public domain. Apart from basic data on all compounds, we have visualized the chemical space derived from their chemical descriptors via a principal component analysis annotated for CFTR-relevant biological categories. Our online query tools enable the search for most similar compounds and provide the relevant annotations in a structured way. The integration of the KNIME software environment in the back-end facilitates a fast and user-friendly maintenance of the provided data sets and a quick extension with new functionalities, e.g., new analysis routines. CandActBase automatically integrates information from other online sources, such as synonyms from PubChem and provides links to other resources like ChEMBL or the source publications. Conclusion: CandActCFTR aims to establish a database model of candidate cystic fibrosis therapeutics for the activation of CFTR-mediated ion conductance to merge data from publicly available sources. Using CandActBase, our strategy to represent data from several internet resources in a merged and organized form can also be applied to other use cases. For substances tested as CFTR activating compounds, the search function allows users to check if a specific compound or a closely related substance was already tested in the CF field. The acquired information on tested substances will assist in the identification of the most promising candidates for future therapeutics."],["dc.description.abstract","Background: Cystic fibrosis (CF) is a genetic disease caused by mutations in CFTR , which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body. Recently, some therapeutics became available that directly target dysfunctional CFTR, yet research for more effective substances is ongoing. The database CandActCFTR aims to provide detailed and comprehensive information on candidate therapeutics for the activation of CFTR-mediated ion conductance aiding systems-biology approaches to identify substances that will synergistically activate CFTR-mediated ion conductance based on published data. Results: Until 10/2020, we derived data from 108 publications on 3,109 CFTR-relevant substances via the literature database PubMed and further 666 substances via ChEMBL; only 19 substances were shared between these sources. One hundred and forty-five molecules do not have a corresponding entry in PubChem or ChemSpider, which indicates that there currently is no single comprehensive database on chemical substances in the public domain. Apart from basic data on all compounds, we have visualized the chemical space derived from their chemical descriptors via a principal component analysis annotated for CFTR-relevant biological categories. Our online query tools enable the search for most similar compounds and provide the relevant annotations in a structured way. The integration of the KNIME software environment in the back-end facilitates a fast and user-friendly maintenance of the provided data sets and a quick extension with new functionalities, e.g., new analysis routines. CandActBase automatically integrates information from other online sources, such as synonyms from PubChem and provides links to other resources like ChEMBL or the source publications. Conclusion: CandActCFTR aims to establish a database model of candidate cystic fibrosis therapeutics for the activation of CFTR-mediated ion conductance to merge data from publicly available sources. Using CandActBase, our strategy to represent data from several internet resources in a merged and organized form can also be applied to other use cases. For substances tested as CFTR activating compounds, the search function allows users to check if a specific compound or a closely related substance was already tested in the CF field. The acquired information on tested substances will assist in the identification of the most promising candidates for future therapeutics."],["dc.identifier.doi","10.3389/fphar.2021.689205"],["dc.identifier.eissn","1663-9812"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97866"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-507"],["dc.relation.eissn","1663-9812"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Comprehensive Analysis of Chemical Structures That Have Been Tested as CFTR Activating Substances in a Publicly Available Database CandActCFTR"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","7590"],["dc.bibliographiccitation.issue","14"],["dc.bibliographiccitation.journal","International Journal of Molecular Sciences"],["dc.bibliographiccitation.volume","22"],["dc.contributor.affiliation","Vinhoven, Liza; \t\t \r\n\t\t Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany, liza.vinhoven@med.uni-goettingen.de"],["dc.contributor.affiliation","Stanke, Frauke; \t\t \r\n\t\t Clinic for Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany, mekus.frauke@mh-hannover.de\t\t \r\n\t\t Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), The German Center for Lung Research, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany, mekus.frauke@mh-hannover.de"],["dc.contributor.affiliation","Hafkemeyer, Sylvia; \t\t \r\n\t\t Mukoviszidose Institut gGmbH, In den Dauen 6, 53117 Bonn, Germany, shafkemeyer@muko.info"],["dc.contributor.affiliation","Nietert, Manuel Manfred; \t\t \r\n\t\t Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany, manuel.nietert@med.uni-goettingen.de\t\t \r\n\t\t CIDAS Campus Institute Data Science, Goldschmidtstraße 1, 37077 Göttingen, Germany, manuel.nietert@med.uni-goettingen.de"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Stanke, Frauke"],["dc.contributor.author","Hafkemeyer, Sylvia"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.date.accessioned","2021-08-12T07:45:58Z"],["dc.date.available","2021-08-12T07:45:58Z"],["dc.date.issued","2021"],["dc.date.updated","2022-09-06T07:25:31Z"],["dc.description.abstract","Different causative therapeutics for CF patients have been developed. There are still no mutation-specific therapeutics for some patients, especially those with rare CFTR mutations. For this purpose, high-throughput screens have been performed which result in various candidate compounds, with mostly unclear modes of action. In order to elucidate the mechanism of action for promising candidate substances and to be able to predict possible synergistic effects of substance combinations, we used a systems biology approach to create a model of the CFTR maturation pathway in cells in a standardized, human- and machine-readable format. It is composed of a core map, manually curated from small-scale experiments in human cells, and a coarse map including interactors identified in large-scale efforts. The manually curated core map includes 170 different molecular entities and 156 reactions from 221 publications. The coarse map encompasses 1384 unique proteins from four publications. The overlap between the two data sources amounts to 46 proteins. The CFTR Lifecycle Map can be used to support the identification of potential targets inside the cell and elucidate the mode of action for candidate substances. It thereby provides a backbone to structure available data as well as a tool to develop hypotheses regarding novel therapeutics."],["dc.description.abstract","Different causative therapeutics for CF patients have been developed. There are still no mutation-specific therapeutics for some patients, especially those with rare CFTR mutations. For this purpose, high-throughput screens have been performed which result in various candidate compounds, with mostly unclear modes of action. In order to elucidate the mechanism of action for promising candidate substances and to be able to predict possible synergistic effects of substance combinations, we used a systems biology approach to create a model of the CFTR maturation pathway in cells in a standardized, human- and machine-readable format. It is composed of a core map, manually curated from small-scale experiments in human cells, and a coarse map including interactors identified in large-scale efforts. The manually curated core map includes 170 different molecular entities and 156 reactions from 221 publications. The coarse map encompasses 1384 unique proteins from four publications. The overlap between the two data sources amounts to 46 proteins. The CFTR Lifecycle Map can be used to support the identification of potential targets inside the cell and elucidate the mode of action for candidate substances. It thereby provides a backbone to structure available data as well as a tool to develop hypotheses regarding novel therapeutics."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/ijms22147590"],["dc.identifier.pii","ijms22147590"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/88588"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-448"],["dc.relation.eissn","1422-0067"],["dc.relation.orgunit","Institut für Medizinische Bioinformatik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","CFTR Lifecycle Map—A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","e19725"],["dc.bibliographiccitation.issue","16"],["dc.bibliographiccitation.journal","Medicine"],["dc.bibliographiccitation.volume","99"],["dc.contributor.author","Uhlig, Johannes"],["dc.contributor.author","Biggemann, Lorenz"],["dc.contributor.author","Nietert, Manuel M."],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Lotz, Joachim"],["dc.contributor.author","Kim, Hyun S."],["dc.contributor.author","Trojan, Lutz"],["dc.contributor.author","Uhlig, Annemarie"],["dc.date.accessioned","2021-04-14T08:26:39Z"],["dc.date.available","2021-04-14T08:26:39Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1097/MD.0000000000019725"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17378"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82031"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","1536-5964"],["dc.relation.issn","0025-7974"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Discriminating malignant and benign clinical T1 renal masses on computed tomography"],["dc.title.alternative","A pragmatic radiomics and machine learning approach"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","35589"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Jo, Peter"],["dc.contributor.author","Nietert, Manuel M."],["dc.contributor.author","Gusky, Linda"],["dc.contributor.author","Kitz, Julia"],["dc.contributor.author","Conradi, Lena-Christin"],["dc.contributor.author","Müller-Dornieden, Annegret"],["dc.contributor.author","Schueler, Philipp"],["dc.contributor.author","Wolff, Hendrik Andreas"],["dc.contributor.author","Rüschoff, Josef R."],["dc.contributor.author","Ströbel, Philipp"],["dc.contributor.author","Grade, Marian"],["dc.contributor.author","Liersch, Torsten"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Ghadimi, Michael B."],["dc.contributor.author","Sax, Ulrich"],["dc.contributor.author","Gaedcke, Jochen"],["dc.date.accessioned","2018-11-07T10:06:58Z"],["dc.date.available","2018-11-07T10:06:58Z"],["dc.date.issued","2016"],["dc.description.abstract","Translational research relies on high-quality biospecimens. In patients with rectal cancer treated preoperatively with radiochemotherapy tissue based analyses are challenging. To assess quality challenges we analyzed tissue samples taken over the last years in a multicenter setting. We retrospectively evaluated overall 197 patients of the CAO/ARO/AIO-94- and 04-trial with locally advanced rectal cancer that were biopsied preoperatively at the University Medical Center Goettingen as well as in 10 cooperating hospitals in Germany. The cellular content of tumor, mucosa, stroma, necrosis and the amount of isolated DNA and RNA as well as the RNA integrity number (RIN) as quality parameters were evaluated. A high RNA yield (p = 2.75e-07) and the content of tumor (p = 0.004) is significantly associated to high RIN-values, whereas a high content of mucosa (p = 0.07) shows a trend and a high amount of necrosis (p = 0.01) is significantly associated with RNA of poor quality. Correlating biopsies from Goettingen and the cooperating centers showed comparable tumor content results. By taking small sized biopsies we could assess a clear correlation between a good RNA quality and a high amount of RNA and tumor cells. These results also indicate that specimens collected at different centers are of comparable quality."],["dc.identifier.doi","10.1038/srep35589"],["dc.identifier.isi","000385502600001"],["dc.identifier.pmid","27752113"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13950"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39196"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Nature Publishing Group"],["dc.relation.issn","2045-2322"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Neoadjuvant Therapy in Rectal Cancer - Biobanking of Preoperative Tumor Biopsies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]