Result 1 - 10 of total 154
(International review of neurobiology ; ...)
2012
| Author's details | ed. by Elissa J. Chesler ; Melissa A Haendel |
|---|---|
| Series title | International review of neurobiology ... |
| Language | English |
| Dates of publication | 2012-9999 |
| Publisher | Elsevier, AP |
| Publishing place | Amsterdam u.a. |
| Publishing country | Netherlands |
| Document type | Book ; Collection (display volumes) |
| HBZ-ID | HT017514475 |
| Database | Catalogue ZB MED Medicine, Health |
(International review of neurobiology ; 103)
2012
| Author's details | ed. by Elissa J. Chesler ; Melissa A Haendel |
|---|---|
| Series title | International review of neurobiology ; 103 Bioinformatics of behavior |
| Collection | Bioinformatics of behavior |
| Language | English |
| Size | XII, 199 S. : Ill., graph. Darst. |
| Edition | 1. ed. |
| Publisher | Elsevier, AP |
| Publishing place | Amsterdam u.a. |
| Publishing country | Netherlands |
| Document type | Book |
| HBZ-ID | HT017514481 |
| ISBN | 978-0-12-388408-4 ; 0-12-388408-X |
| Database | Catalogue ZB MED Medicine, Health |
| Shelf mark | Loan status | Location |
|---|---|---|
| Ui II Zs 180 -103- | verfügbar in Königswinter | Königswinter |
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
(International review of neurobiology ; 104)
2012
| Author's details | ed. by Elissa J. Chesler ; Melissa A Haendel |
|---|---|
| Series title | International review of neurobiology ; 104 Bioinformatics of behavior |
| Collection | Bioinformatics of behavior |
| Language | English |
| Size | X, 267 S. : Ill., graph. Darst. |
| Edition | 1. ed. |
| Publisher | Elsevier, AP |
| Publishing place | Amsterdam u.a. |
| Publishing country | Netherlands |
| Document type | Book |
| HBZ-ID | HT017514531 |
| ISBN | 978-0-12-398323-7 ; 0-12-398323-1 |
| Database | Catalogue ZB MED Medicine, Health |
| Shelf mark | Loan status | Location |
|---|---|---|
| Ui II Zs 180 -104- | verfügbar in Königswinter | Königswinter |
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
Yearbook of medical informatics
2020 Volume 29, Issue 1, Page(s) 159–162
Abstract: Objectives: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning.: Methods: ... ...
| Abstract | Objectives: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning. Methods: A comprehensive review of the medical informatics literature was performed to select the most interesting papers published in 2018 and 2019 and that document the utility of ontologies for computational analysis, including machine learning. Results: Fifteen articles were selected for inclusion in this survey paper. The chosen articles belong to three major themes: (i) the identification of phenotypic abnormalities in electronic health record (EHR) data using the Human Phenotype Ontology (ii) word and node embedding algorithms to supplement natural language processing (NLP) of EHRs and other medical texts; and (iii) hybrid ontology and NLP-based approaches to extracting structured and unstructured components of EHRs. Conclusion: Unprecedented amounts of clinically relevant data are now available for clinical and research use. Machine learning is increasingly being applied to these data sources for predictive analytics, precision medicine, and differential diagnosis. Ontologies have become an essential component of software pipelines designed to extract, code, and analyze clinical information by machine learning algorithms. The intersection of machine learning and semantics is proving to be an innovative space in clinical research. |
|---|---|
| MeSH term(s) | Artificial Intelligence ; Biological Ontologies ; Data Mining ; Electronic Health Records ; Humans ; Knowledge Management ; Machine Learning ; Natural Language Processing ; Translational Medical Research |
| Language | English |
| Publishing date | 2020-08-21 |
| Publishing country | Germany |
| Document type | Journal Article |
| ZDB-ID | 2251229-9 |
| ISSN | 2364-0502 ; 2364-0502 |
| ISSN (online) | 2364-0502 |
| ISSN | 2364-0502 |
| DOI | 10.1055/s-0040-1701991 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
BMC medical informatics and decision making
2024 Volume 24, Issue 1, Page(s) 30
Abstract: Objective: Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on ... ...
| Abstract | Objective: Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation. Materials and methods: The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations. Results: The best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches. Conclusion: Our experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task. |
|---|---|
| MeSH term(s) | Humans ; Knowledge ; Language ; Machine Learning ; Phenotype ; Rare Diseases |
| Language | English |
| Publishing date | 2024-01-31 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2046490-3 |
| ISSN | 1472-6947 ; 1472-6947 |
| ISSN (online) | 1472-6947 |
| ISSN | 1472-6947 |
| DOI | 10.1186/s12911-024-02439-w |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
Yearbook of Medical Informatics
2020 Volume 29, Issue 01, Page(s) 159–162
Abstract: Objectives: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning.: Methods: ... ...
| Abstract | Objectives: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning. Methods: A comprehensive review of the medical informatics literature was performed to select the most interesting papers published in 2018 and 2019 and that document the utility of ontologies for computational analysis, including machine learning. Results: Fifteen articles were selected for inclusion in this survey paper. The chosen articles belong to three major themes: (i) the identification of phenotypic abnormalities in electronic health record (EHR) data using the Human Phenotype Ontology; (ii) word and node embedding algorithms to supplement natural language processing (NLP) of EHRs and other medical texts; and (iii) hybrid ontology and NLP-based approaches to extracting structured and unstructured components of EHRs. Conclusion: Unprecedented amounts of clinically relevant data are now available for clinical and research use. Machine learning is increasingly being applied to these data sources for predictive analytics, precision medicine, and differential diagnosis. Ontologies have become an essential component of software pipelines designed to extract, code, and analyze clinical information by machine learning algorithms. The intersection of machine learning and semantics is proving to be an innovative space in clinical research. |
|---|---|
| Keywords | Artificial intelligence ; machine learning ; ontology ; natural language processing |
| Language | English |
| Publishing date | 2020-08-01 |
| Publisher | Georg Thieme Verlag KG |
| Publishing place | Stuttgart ; New York |
| Document type | Article |
| ZDB-ID | 2251229-9 |
| ISSN | 2364-0502 ; 0943-4747 ; 2364-0502 |
| ISSN (online) | 2364-0502 |
| ISSN | 0943-4747 ; 2364-0502 |
| DOI | 10.1055/s-0040-1701991 |
| Database | Thieme publisher's database |
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
medRxiv : the preprint server for health sciences
2023
Abstract: Postpartum depression (PPD), afflicting one in seven women, poses a major challenge in maternal health. Existing approaches to detect PPD heavily depend on in-person postpartum visits, leading to cases of the condition being overlooked and untreated. We ... ...
| Abstract | Postpartum depression (PPD), afflicting one in seven women, poses a major challenge in maternal health. Existing approaches to detect PPD heavily depend on in-person postpartum visits, leading to cases of the condition being overlooked and untreated. We explored the potential of consumer wearable-derived digital biomarkers for PPD recognition to address this gap. Our study demonstrated that intra-individual machine learning (ML) models developed using these digital biomarkers can discern between pre-pregnancy, pregnancy, postpartum without depression, and postpartum with depression time periods (i.e., PPD diagnosis). When evaluating variable importance, calories burned from the basal metabolic rate (calories BMR) emerged as the digital biomarker most predictive of PPD. To confirm the specificity of our method, we demonstrated that models developed in women without PPD could not accurately classify the PPD-equivalent phase. Prior depression history did not alter model efficacy for PPD recognition. Furthermore, the individualized models demonstrated superior performance compared to a conventional cohort-based model for the detection of PPD, underscoring the effectiveness of our individualized ML approach. This work establishes consumer wearables as a promising avenue for PPD identification. More importantly, it also emphasizes the utility of individualized ML model methodology, potentially transforming early disease detection strategies. |
|---|---|
| Language | English |
| Publishing date | 2023-10-14 |
| Publishing country | United States |
| Document type | Preprint |
| DOI | 10.1101/2023.10.13.23296965 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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Mammalian genome : official journal of the International Mammalian Genome Society
2019 Volume 30, Issue 5-6, Page(s) 143–150
Abstract: The International Mouse Phenotyping Consortium (IMPC) continues to expand the catalogue of mammalian gene function by conducting genome and phenome-wide phenotyping on knockout mouse lines. The extensive and standardized phenotype screens allow the ... ...
| Abstract | The International Mouse Phenotyping Consortium (IMPC) continues to expand the catalogue of mammalian gene function by conducting genome and phenome-wide phenotyping on knockout mouse lines. The extensive and standardized phenotype screens allow the identification of new potential models for human disease through cross-species comparison by computing the similarity between the phenotypes observed in the mutant mice and the human phenotypes associated to their orthologous loci in Mendelian disease. Here, we present an update on the novel disease models available from the most recent data release (DR10.0), with 5861 mouse genes fully or partially phenotyped and a total number of 69,982 phenotype calls reported. With approximately one-third of human Mendelian genes with orthologous null mouse phenotypes described, the range of available models relevant for human diseases keeps increasing. Among the breadth of new data, we identify previously uncharacterized disease genes in the mouse and additional phenotypes for genes with existing mutant lines mimicking the associated disorder. The automated and unbiased discovery of relevant models for all types of rare diseases implemented by the IMPC constitutes a powerful tool for human genetics and precision medicine. |
|---|---|
| MeSH term(s) | Animals ; Disease Models, Animal ; Genome ; Humans ; Mice ; Mice, Knockout ; Phenotype ; Precision Medicine |
| Language | English |
| Publishing date | 2019-05-24 |
| Publishing country | United States |
| Document type | Journal Article ; Research Support, N.I.H., Extramural ; Review |
| ZDB-ID | 1058547-3 |
| ISSN | 1432-1777 ; 0938-8990 |
| ISSN (online) | 1432-1777 |
| ISSN | 0938-8990 |
| DOI | 10.1007/s00335-019-09804-5 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
| Zs.A 3489: Show issues | Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG) |
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Bioinformatics (Oxford, England)
2024 Volume 40, Issue 3
Abstract: Motivation: Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and ... ...
| Abstract | Motivation: Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrarily complex nested knowledge schemas. Results: Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against an LLM to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for matched elements. We present examples of applying SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease relationships. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction methods, but greatly surpasses an LLM's native capability of grounding entities with unique identifiers. SPIRES has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any new training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. Availability and implementation: SPIRES is available as part of the open source OntoGPT package: https://github.com/monarch-initiative/ontogpt. |
|---|---|
| MeSH term(s) | Semantics ; Knowledge Bases ; Databases, Factual |
| Language | English |
| Publishing date | 2024-02-20 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 1422668-6 |
| ISSN | 1367-4811 ; 1367-4803 |
| ISSN (online) | 1367-4811 |
| ISSN | 1367-4803 |
| DOI | 10.1093/bioinformatics/btae104 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
| Zs.A 2374: Show issues | Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG) |
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International journal of medical informatics
2024 Volume 187, Page(s) 105461
Abstract: Objective: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors ... ...
| Abstract | Objective: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids). Materials and methods: We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. Results: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. Discussion: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation. Conclusion: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs. |
|---|---|
| Language | English |
| Publishing date | 2024-04-17 |
| Publishing country | Ireland |
| Document type | Journal Article |
| ZDB-ID | 1466296-6 |
| ISSN | 1872-8243 ; 1386-5056 |
| ISSN (online) | 1872-8243 |
| ISSN | 1386-5056 |
| DOI | 10.1016/j.ijmedinf.2024.105461 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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