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Article ; Online: Integrated analysis of transcriptome and epigenome reveals

Bahrami, Basireh / Wolfien, Markus / Nikpour, Parvaneh

2024 Volume 16, Issue 3, Page(s) 159–173

Abstract: Background: ...

Abstract	Background:
MeSH term(s)	Humans ; Stomach Neoplasms/genetics ; Stomach Neoplasms/pathology ; Transcriptome ; Epigenome ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics
Chemical Substances	Biomarkers, Tumor
Language	English
Publishing date	2024-01-29
Publishing country	England
Document type	Journal Article
ZDB-ID	2537199-X
ISSN	1750-192X ; 1750-1911
ISSN (online)	1750-192X
ISSN	1750-1911
DOI	10.2217/epi-2023-0213
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data.

Ahmadi, Najia / Nguyen, Quang Vu / Sedlmayr, Martin / Wolfien, Markus

Scientific reports

2024 Volume 14, Issue 1, Page(s) 2287

Abstract: The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the aid of ...

Abstract	The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the aid of such large data pools, researchers are able to develop clinical prediction models for improved disease classification, risk assessment, and beyond. To fully utilize this potential, Machine Learning (ML) methods are commonly required to process these large amounts of data on disease-specific patient cohorts. As a consequence, the Observational Health Data Sciences and Informatics (OHDSI) collaborative develops a framework to facilitate the application of ML models for these standardized patient datasets by using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). In this study, we compare the feasibility of current web-based OHDSI approaches, namely ATLAS and "Patient-level Prediction" (PLP), against a native solution (R based) to conduct such ML-based patient-level prediction analyses in OMOP. This will enable potential users to select the most suitable approach for their investigation. Each of the applied ML solutions was individually utilized to solve the same patient-level prediction task. Both approaches went through an exemplary benchmarking analysis to assess the weaknesses and strengths of the PLP R-Package. In this work, the performance of this package was subsequently compared versus the commonly used native R-package called Machine Learning in R 3 (mlr3), and its sub-packages. The approaches were evaluated on performance, execution time, and ease of model implementation. The results show that the PLP package has shorter execution times, which indicates great scalability, as well as intuitive code implementation, and numerous possibilities for visualization. However, limitations in comparison to native packages were depicted in the implementation of specific ML classifiers (e.g., Lasso), which may result in a decreased performance for real-world prediction problems. The findings here contribute to the overall effort of developing ML-based prediction models on a clinical scale and provide a snapshot for future studies that explicitly aim to develop patient-level prediction models in OMOP CDM.
MeSH term(s)	Humans ; Databases, Factual ; Machine Learning ; Medical Informatics ; Electronic Health Records
Language	English
Publishing date	2024-01-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-52723-y
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Conference proceedings: Hyperparameter Tuning Matters for Generative Models to Produce Improved Synthetic Data in Small Tabular Datasets

Hahn, Waldemar / Wolfien, Markus

2023 , Page(s) Abstr. 290

Event/congress	68. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS); Heilbronn; Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie; 2023
Keywords	Medizin, Gesundheit ; synthetic data ; tabular data ; data generation ; HPO
Publishing date	2023-09-15
Publisher	German Medical Science GMS Publishing House; Düsseldorf
Document type	Conference proceedings
DOI	10.3205/23gmds018
Database	German Medical Science

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Article ; Online: NaviCenta - The disease map for placental research.

Scheel, Julia / Hoch, Matti / Wolfien, Markus / Gupta, Shailendra

Placenta

2023 Volume 143, Page(s) 12–15

Abstract: The placenta remains the key organ to pregnancy complications, such as preeclampsia, contrarily the pathophysiology underlying the placental dysfunctions remains elusive. Here, we present our Disease Map "NaviCenta", which is an online resource based on ... ...

Abstract	The placenta remains the key organ to pregnancy complications, such as preeclampsia, contrarily the pathophysiology underlying the placental dysfunctions remains elusive. Here, we present our Disease Map "NaviCenta", which is an online resource based on the interactions between tissues, cellular compartments, and molecules that mediate disease-related processes in the placenta. We built cellular and molecular interaction networks based upon manual curation and annotation of publicly available information in the scientific literature, pathways resources, and Omics data. NaviCenta (Navigate the plaCenta) serves as an open access, spatio-temporal, multi-scale knowledge base, and analytical tool for enhanced interpretation and hypothesis testing on various placental disease phenotypes.
MeSH term(s)	Pregnancy ; Female ; Humans ; Placenta/metabolism ; Placenta Diseases/metabolism ; Pregnancy Complications/metabolism ; Pre-Eclampsia/metabolism
Language	English
Publishing date	2023-09-19
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	603951-0
ISSN	1532-3102 ; 0143-4004
ISSN (online)	1532-3102
ISSN	0143-4004
DOI	10.1016/j.placenta.2023.09.007
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Article: Artificial Intelligence Reporting Guidelines' Adherence in Nephrology for Improved Research and Clinical Outcomes.

Salybekov, Amankeldi A / Wolfien, Markus / Hahn, Waldemar / Hidaka, Sumi / Kobayashi, Shuzo

Biomedicines

2024 Volume 12, Issue 3

Abstract: The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields, including nephrology. The integration of various AI techniques in nephrology facilitates the prediction of the early detection, diagnosis, prognosis, and ... ...

Abstract	The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields, including nephrology. The integration of various AI techniques in nephrology facilitates the prediction of the early detection, diagnosis, prognosis, and treatment of kidney disease. Nevertheless, recent reports have demonstrated that the majority of published clinical AI studies lack uniform AI reporting standards, which poses significant challenges in interpreting, replicating, and translating the studies into routine clinical use. In response to these issues, worldwide initiatives have created guidelines for publishing AI-related studies that outline the minimal necessary information that researchers should include. By following standardized reporting frameworks, researchers and clinicians can ensure the reproducibility, reliability, and ethical use of AI models. This will ultimately lead to improved research outcomes, enhanced clinical decision-making, and better patient management. This review article highlights the importance of adhering to AI reporting guidelines in medical research, with a focus on nephrology and urology, and clinical practice for advancing the field and optimizing patient care.
Language	English
Publishing date	2024-03-07
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines12030606
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Article ; Online: Context-Sensitive Common Data Models for Genetic Rare Diseases - A Concept.

Ahmadi, Najia / Zoch, Michele / Sedlmayr, Brita / Schuler, Katharina / Hahn, Waldemar / Sedlmayr, Martin / Wolfien, Markus

Studies in health technology and informatics

2023 Volume 305, Page(s) 139–140

Abstract: Current challenges of rare diseases need to involve patients, physicians, and the research community to generate new insights on comprehensive patient cohorts. Interestingly, the integration of patient context has been insufficiently considered, but ... ...

Abstract	Current challenges of rare diseases need to involve patients, physicians, and the research community to generate new insights on comprehensive patient cohorts. Interestingly, the integration of patient context has been insufficiently considered, but might tremendously improve the accuracy of predictive models for individual patients. Here, we conceptualized an extension of the European Platform for Rare Disease Registration data model with contextual factors. This extended model can serve as an enhanced baseline and is well-suited for analyses using artificial intelligence models for improved predictions. The study is an initial result that will develop context-sensitive common data models for genetic rare diseases.
MeSH term(s)	Humans ; Artificial Intelligence ; Rare Diseases/genetics ; Physicians
Language	English
Publishing date	2023-06-29
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI230443
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Article: Methods Used in the Development of Common Data Models for Health Data: Scoping Review.

Ahmadi, Najia / Zoch, Michele / Kelbert, Patricia / Noll, Richard / Schaaf, Jannik / Wolfien, Markus / Sedlmayr, Martin

JMIR medical informatics

2023 Volume 11, Page(s) e45116

Abstract: Background: Common data models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in the health domain. CDMs unite data from disparate sources and ease collaborations across institutions, resulting in the ... ...

Abstract	Background: Common data models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in the health domain. CDMs unite data from disparate sources and ease collaborations across institutions, resulting in the generation of large standardized data repositories across different entities. An overview of existing CDMs and methods used to develop these data sets may assist in the development process of future models for the health domain, such as for decision support systems. Objective: This scoping review investigates methods used in the development of CDMs for health data. We aim to provide a broad overview of approaches and guidelines that are used in the development of CDMs (ie, common data elements or common data sets) for different health domains on an international level. Methods: This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We conducted the literature search in prominent databases, namely, PubMed, Web of Science, Science Direct, and Scopus, starting from January 2000 until March 2022. We identified and screened 1309 articles. The included articles were evaluated based on the type of adopted method, which was used in the conception, users' needs collection, implementation, and evaluation phases of CDMs, and whether stakeholders (such as medical experts, patients' representatives, and IT staff) were involved during the process. Moreover, the models were grouped into iterative or linear types based on the imperativeness of the stages during development. Results: We finally identified 59 articles that fit our eligibility criteria. Of these articles, 45 specifically focused on common medical conditions, 10 focused on rare medical conditions, and the remaining 4 focused on both conditions. The development process usually involved stakeholders but in different ways (eg, working group meetings, Delphi approaches, interviews, and questionnaires). Twenty-two models followed an iterative process. Conclusions: The included articles showed the diversity of methods used to develop a CDM in different domains of health. We highlight the need for more specialized CDM development methods in the health domain and propose a suggestive development process that might ease the development of CDMs in the health domain in the future.
Language	English
Publishing date	2023-08-03
Publishing country	Canada
Document type	Journal Article ; Review
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/45116
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Article ; Online: Construction of a three-component regulatory network of transcribed ultraconserved regions for the identification of prognostic biomarkers in gastric cancer.

Khalafiyan, Anis / Emadi-Baygi, Modjtaba / Wolfien, Markus / Salehzadeh-Yazdi, Ali / Nikpour, Parvaneh

Journal of cellular biochemistry

2023 Volume 124, Issue 3, Page(s) 396–408

Abstract: Altered expression and functional roles of the transcribed ultraconserved regions (T-UCRs), as genomic sequences with 100% conservation between the genomes of human, mouse, and rat, in the pathophysiology of neoplasms has already been investigated. ... ...

Abstract	Altered expression and functional roles of the transcribed ultraconserved regions (T-UCRs), as genomic sequences with 100% conservation between the genomes of human, mouse, and rat, in the pathophysiology of neoplasms has already been investigated. Nevertheless, the relevance of the functions for T-UCRs in gastric cancer (GC) is still the subject of inquiry. In the current study, we first used a genome-wide profiling approach to analyze the expression of T-UCRs in GC patients. Then, we constructed a three-component regulatory network and investigated potential diagnostic and prognostic values of the T-UCRs. The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset was used as a resource for the RNA-sequencing data. FeatureCounts was utilized to quantify the number of reads mapped to each T-UCR. Differential expression analysis was then conducted using DESeq2. In the following, interactions between T-UCRs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were combined into a three-component network. Enrichment analyses were performed and a protein-protein interaction (PPI) network was constructed. The R Survival package was utilized to identify survival-related significantly differentially expressed T-UCRs (DET-UCRs). Using an in-house cohort of GC tissues, expression of two DET-UCRs was furthermore experimentally verified. Our results showed that several T-UCRs were dysregulated in TCGA-STAD tumoral samples compared to nontumoral counterparts. The three-component network was constructed which composed of DET-UCRs, miRNAs, and mRNAs nodes. Functional enrichment and PPI network analyses revealed important enriched signaling pathways and gene ontologies such as "pathway in cancer" and regulation of cell proliferation and apoptosis. Five T-UCRs were significantly correlated with the overall survival of GC patients. While no expression of uc.232 was observed in our in-house cohort of GC tissues, uc.343 showed an increased expression, although not statistically significant, in gastric tumoral tissues. The constructed three-component regulatory network of T-UCRs in GC presents a comprehensive understanding of the underlying gene expression regulation processes involved in tumor development and can serve as a basis to investigate potential prognostic biomarkers and therapeutic targets.
MeSH term(s)	Humans ; Rats ; Mice ; Animals ; Stomach Neoplasms/genetics ; Prognosis ; Conserved Sequence/genetics ; Gene Expression Regulation, Neoplastic ; MicroRNAs/genetics ; Adenocarcinoma/genetics ; Biomarkers ; RNA, Long Noncoding ; Gene Regulatory Networks ; Biomarkers, Tumor/genetics
Chemical Substances	MicroRNAs ; Biomarkers ; RNA, Long Noncoding ; Biomarkers, Tumor
Language	English
Publishing date	2023-02-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	392402-6
ISSN	1097-4644 ; 0730-2312
ISSN (online)	1097-4644
ISSN	0730-2312
DOI	10.1002/jcb.30373
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Article ; Online: OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review.

Ahmadi, Najia / Peng, Yuan / Wolfien, Markus / Zoch, Michéle / Sedlmayr, Martin

International journal of molecular sciences

2022 Volume 23, Issue 19

Abstract: The current generation of sequencing technologies has led to significant advances in identifying novel disease-associated mutations and generated large amounts of data in a high-throughput manner. Such data in conjunction with clinical routine data are ... ...

Abstract	The current generation of sequencing technologies has led to significant advances in identifying novel disease-associated mutations and generated large amounts of data in a high-throughput manner. Such data in conjunction with clinical routine data are proven to be highly useful in deriving population-level and patient-level predictions, especially in the field of cancer precision medicine. However, data harmonization across multiple national and international clinical sites is an essential step for the assessment of events and outcomes associated with patients, which is currently not adequately addressed. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an internationally established research data repository introduced by the Observational Health Data Science and Informatics (OHDSI) community to overcome this issue. To address the needs of cancer research, the genomic vocabulary extension was introduced in 2020 to support the standardization of subsequent data analysis. In this review, we evaluate the current potential of the OMOP CDM to be applicable in cancer prediction and how comprehensively the genomic vocabulary extension of the OMOP can serve current needs of AI-based predictions. For this, we systematically screened the literature for articles that use the OMOP CDM in predictive analyses in cancer and investigated the underlying predictive models/tools. Interestingly, we found 248 articles, of which most use the OMOP for harmonizing their data, but only 5 make use of predictive algorithms on OMOP-based data and fulfill our criteria. The studies present multicentric investigations, in which the OMOP played an essential role in discovering and optimizing machine learning (ML)-based models. Ultimately, the use of the OMOP CDM leads to standardized data-driven studies for multiple clinical sites and enables a more solid basis utilizing, e.g., ML models that can be reused and combined in early prediction, diagnosis, and improvement of personalized cancer care and biomarker discovery.
MeSH term(s)	Biomarkers ; Data Analysis ; Databases, Factual ; Electronic Health Records ; Humans ; Medical Informatics ; Neoplasms/diagnosis ; Neoplasms/genetics ; Precision Medicine
Chemical Substances	Biomarkers
Language	English
Publishing date	2022-10-05
Publishing country	Switzerland
Document type	Journal Article ; Review ; Systematic Review
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms231911834
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Article: Leptin deficiency-caused behavioral change - A comparative analysis using EthoVision and DeepLabCut.

Bühler, Daniel / Power Guerra, Nicole / Müller, Luisa / Wolkenhauer, Olaf / Düffer, Martin / Vollmar, Brigitte / Kuhla, Angela / Wolfien, Markus

Frontiers in neuroscience

2023 Volume 17, Page(s) 1052079

Abstract: Introduction: Obese rodents e.g., the leptin-deficient (ob/ob) mouse exhibit remarkable behavioral changes and are therefore ideal models for evaluating mental disorders resulting from obesity. In doing so, female as well as male ob/ob mice at 8, 24, ... ...

Abstract	Introduction: Obese rodents e.g., the leptin-deficient (ob/ob) mouse exhibit remarkable behavioral changes and are therefore ideal models for evaluating mental disorders resulting from obesity. In doing so, female as well as male ob/ob mice at 8, 24, and 40 weeks of age underwent two common behavioral tests, namely the Open Field test and Elevated Plus Maze, to investigate behavioral alteration in a sex- and age dependent manner. The accuracy of these tests is often dependent on the observer that can subjectively influence the data. Methods: To avoid this bias, mice were tracked with a video system. Video files were further analyzed by the compared use of two software, namely EthoVision (EV) and DeepLabCut (DLC). In DLC a Deep Learning application forms the basis for using artificial intelligence in behavioral research in the future, also with regard to the reduction of animal numbers. Results: After no sex and partly also no age-related differences were found, comparison revealed that both software lead to almost identical results and are therefore similar in their basic outcomes, especially in the determination of velocity and total distance movement. Moreover, we observed additional benefits of DLC compared to EV as it enabled the interpretation of more complex behavior, such as rearing and leaning, in an automated manner. Discussion: Based on the comparable results from both software, our study can serve as a starting point for investigating behavioral alterations in preclinical studies of obesity by using DLC to optimize and probably to predict behavioral observations in the future.
Language	English
Publishing date	2023-03-24
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2023.1052079
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