標(biāo)題: Titlebook: Data Integration in the Life Sciences; 13th International C S?ren Auer,Maria-Esther Vidal Conference proceedings 2019 Springer Nature Switz [打印本頁] 作者: Retina 時(shí)間: 2025-3-21 16:12
書目名稱Data Integration in the Life Sciences影響因子(影響力)
書目名稱Data Integration in the Life Sciences影響因子(影響力)學(xué)科排名
書目名稱Data Integration in the Life Sciences網(wǎng)絡(luò)公開度
書目名稱Data Integration in the Life Sciences網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Integration in the Life Sciences被引頻次
書目名稱Data Integration in the Life Sciences被引頻次學(xué)科排名
書目名稱Data Integration in the Life Sciences年度引用
書目名稱Data Integration in the Life Sciences年度引用學(xué)科排名
書目名稱Data Integration in the Life Sciences讀者反饋
書目名稱Data Integration in the Life Sciences讀者反饋學(xué)科排名
作者: 外向者 時(shí)間: 2025-3-21 20:51
Yueyu Dong,Fei Dai,Mingming Qinof the annotation process, we propose the use of machine learning to combine the results of different annotation tools. We comparatively evaluate the results of the machine-learning based approach with the results of the single tools and a simpler set-based result combination.作者: CLOT 時(shí)間: 2025-3-22 01:32 作者: 缺陷 時(shí)間: 2025-3-22 06:25
https://doi.org/10.1007/978-3-030-06016-9artificial intelligence; computational linguistics; data integration; databases; information management; 作者: Dri727 時(shí)間: 2025-3-22 12:01
978-3-030-06015-2Springer Nature Switzerland AG 2019作者: 潛移默化 時(shí)間: 2025-3-22 15:17
A Learning-Based Approach to Combine Medical Annotation Resultsof the annotation process, we propose the use of machine learning to combine the results of different annotation tools. We comparatively evaluate the results of the machine-learning based approach with the results of the single tools and a simpler set-based result combination.作者: 潛移默化 時(shí)間: 2025-3-22 21:08 作者: pus840 時(shí)間: 2025-3-22 23:06 作者: 執(zhí)拗 時(shí)間: 2025-3-23 02:33
Fei Xu,Fangming Liu,Dekang Zhu,Hai Jin genomic components can often be modeled and visualized in graph structures. In this paper we propose the integration of several data sets into a graph database. We study the aptness of the database system in terms of analysis and visualization of a genome regulatory network (GRN) by running a bench作者: Dendritic-Cells 時(shí)間: 2025-3-23 08:29 作者: 反抗者 時(shí)間: 2025-3-23 13:31
https://doi.org/10.1007/978-3-030-69992-5 even more complex. Popular tools for facilitating the daily routine for the clinical researchers are more often based on machine learning (ML) algorithms. Those tools might facilitate data management, data integration or even content classification. Besides commercial functionalities, there are man作者: FADE 時(shí)間: 2025-3-23 16:50
Linkun Zhang,Yuxia Lei,Zhengyan Wangore than a collection of (digital) documents. The main reason is in the fact that the document is the principal form of communication and—since underlying data, software and other materials mostly remain unpublished—the fact that the scholarly article is, essentially, the only form used to communica作者: RAG 時(shí)間: 2025-3-23 20:34
Linkun Zhang,Yuxia Lei,Zhengyan Wangong statistical tools can lead to the reporting of dubious correlations as significant results. Missing information from lab protocols or other metadata can make verification impossible. Especially with the advent of Big Data in the life sciences and the hereby-involved measurement of thousands of m作者: euphoria 時(shí)間: 2025-3-24 00:13
Computer Communications and Networksased applications have increased considerably during the last decade. Particularly in Life Sciences, RDF datasets are utilized to represent diverse concepts, e.g., proteins, genes, mutations, diseases, drugs, and side effects. Albeit publicly accessible, the exploration of these RDF datasets require作者: 狗舍 時(shí)間: 2025-3-24 06:04
Miyuru Dayarathna,Toyotaro Suzumura In order to extract knowledge, these data should be curated, integrated, and semantically described. Accordingly, several semantic integration techniques have been developed; albeit effective, they may suffer from scalability in terms of different properties of big data. Even scaled-up approaches m作者: Fester 時(shí)間: 2025-3-24 10:25 作者: 身體萌芽 時(shí)間: 2025-3-24 11:55
https://doi.org/10.1007/978-3-319-54645-2iratory and other viral infections can be judged from blood samples; however, it has not yet been determined which genetic expression levels are predictive, in particular for the early transition states of the disease onset. For these reasons, we analyse the expression levels of infected and non-inf作者: Crepitus 時(shí)間: 2025-3-24 16:52
Yueyu Dong,Fei Dai,Mingming Qin as billing and reimbursement, quality control, epidemiological studies, and cohort identification for clinical trials. The codes are based on standardized vocabularies. Diagnostics, for example, are frequently coded using the International Classification of Diseases (ICD), which is a taxonomy of di作者: AUGUR 時(shí)間: 2025-3-24 21:31 作者: Invigorate 時(shí)間: 2025-3-25 00:47 作者: 否認(rèn) 時(shí)間: 2025-3-25 07:17 作者: landmark 時(shí)間: 2025-3-25 08:46 作者: 按等級 時(shí)間: 2025-3-25 15:08
https://doi.org/10.1007/978-3-030-69992-5roach to bring together the medical findings on the one hand, and the metadata of the findings on the other hand, and compared several common classifier to have the best results. In order to conduct this study, we used the data and the technology of the Enterprise Clinical Research Data Warehouse (E作者: Suppository 時(shí)間: 2025-3-25 18:46 作者: 護(hù)身符 時(shí)間: 2025-3-25 20:07 作者: chemical-peel 時(shí)間: 2025-3-26 04:07
https://doi.org/10.1007/978-3-319-54645-2m the overall set of 12,023 genes, we identified the 10 top-ranked genes which proved to be most discriminatory with regards to prediction of the infection state. Our two models focus on the time stamp nearest to . hours and nearest to . “.” denoting the symptom onset (at different time points) acco作者: Itinerant 時(shí)間: 2025-3-26 05:12 作者: 指派 時(shí)間: 2025-3-26 09:36
Do Scaling Algorithms Preserve Word2Vec Semantics? A Case Study for Medical Entitiess precision/recall. We show that the quality of results gained using simpler and easier to compute scaling approaches like MDS or PCA correlates strongly with the expected quality when using the same number of Word2Vec training dimensions. This has even more impact if after initial Word2Vec training作者: defile 時(shí)間: 2025-3-26 16:22 作者: FLOUR 時(shí)間: 2025-3-26 20:11
Interactive Visualization for Large-Scale Multi-factorial Research Designsph visualization tailored to experiments using a factorial experimental design. Our solution summarizes sample sources and extracted samples based on similarity of independent variables, enabling a quick grasp of the scientific question at the core of the experiment even for large studies. We suppor作者: 螢火蟲 時(shí)間: 2025-3-26 21:24
Data Integration for Supporting Biomedical Knowledge Graph Creation at Large-Scaledge graph creation by up?to 70% of the time that is consumed following a traditional approach. Accordingly, the experimental results suggest that ConMap can be a semantic data integration solution that embody FAIR principles specifically in terms of interoperability.作者: hair-bulb 時(shí)間: 2025-3-27 03:55
Using Machine Learning to Distinguish Infected from Non-infected Subjects at an Early Stage Based onm the overall set of 12,023 genes, we identified the 10 top-ranked genes which proved to be most discriminatory with regards to prediction of the infection state. Our two models focus on the time stamp nearest to . hours and nearest to . “.” denoting the symptom onset (at different time points) acco作者: 極大的痛苦 時(shí)間: 2025-3-27 06:54
Automated Coding of Medical Diagnostics from Free-Text: The Role of Parameters Optimization and Imbarocess of ICD coding. In this article, we investigate the use of Support Vector Machines (SVM) and the binary relevance method for multi-label classification in the task of automatic ICD coding from free-text discharge summaries. In particular, we explored the role of SVM parameters optimization and作者: prosthesis 時(shí)間: 2025-3-27 09:40 作者: invulnerable 時(shí)間: 2025-3-27 14:07 作者: 肌肉 時(shí)間: 2025-3-27 18:54
Construction and Visualization of Dynamic Biological Networks: Benchmarking the Neo4J Graph Database genomic components can often be modeled and visualized in graph structures. In this paper we propose the integration of several data sets into a graph database. We study the aptness of the database system in terms of analysis and visualization of a genome regulatory network (GRN) by running a bench作者: Entirety 時(shí)間: 2025-3-28 00:57
A Knowledge-Driven Pipeline for Transforming Big Data into Actionable Knowledgeowledge encoded in available big data. In order to address these requirements while scaling up?to the dominant dimensions of big biomedical data –volume, variety, and veracity– novel data integration techniques need to be defined. In this paper, we devise a knowledge-driven approach that relies on S作者: Override 時(shí)間: 2025-3-28 04:08
Leaving No Stone Unturned: Using Machine Learning Based Approaches for Information Extraction from F even more complex. Popular tools for facilitating the daily routine for the clinical researchers are more often based on machine learning (ML) algorithms. Those tools might facilitate data management, data integration or even content classification. Besides commercial functionalities, there are man作者: 無法取消 時(shí)間: 2025-3-28 07:11
Towards Research Infrastructures that Curate Scientific Information: A Use Case in Life Sciencesore than a collection of (digital) documents. The main reason is in the fact that the document is the principal form of communication and—since underlying data, software and other materials mostly remain unpublished—the fact that the scholarly article is, essentially, the only form used to communica作者: conjunctivitis 時(shí)間: 2025-3-28 13:46 作者: 離開可分裂 時(shí)間: 2025-3-28 16:47 作者: 沙發(fā) 時(shí)間: 2025-3-28 18:56 作者: Employee 時(shí)間: 2025-3-29 01:28 作者: Proclaim 時(shí)間: 2025-3-29 07:06 作者: 致命 時(shí)間: 2025-3-29 10:02 作者: 入伍儀式 時(shí)間: 2025-3-29 13:57
A Learning-Based Approach to Combine Medical Annotation Resultsof the annotation process, we propose the use of machine learning to combine the results of different annotation tools. We comparatively evaluate the results of the machine-learning based approach with the results of the single tools and a simpler set-based result combination.作者: 愛花花兒憤怒 時(shí)間: 2025-3-29 16:13
Knowledge Graph Completion to Predict Polypharmacy Side Effectser, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly eff作者: 改革運(yùn)動(dòng) 時(shí)間: 2025-3-29 22:20 作者: 松軟 時(shí)間: 2025-3-30 01:32
Combining Semantic and Lexical Measures to Evaluate Medical Terms Similarityic similarity measures to improve the evaluation of terms relatedness. We validate our approach through a set of experiments based on a corpus of reference constructed by domain experts of the medical field and further evaluate the impact of ontology evolution on the used semantic similarity measures.作者: Metastasis 時(shí)間: 2025-3-30 06:18 作者: octogenarian 時(shí)間: 2025-3-30 11:09
Computer Communications and Networksonstrate FedSDM, a semantic data manager for federations of RDF datasets. Attendees will be able to explore the relationships among the RDF datasets in a federation, as well as the characteristics of the RDF classes included in each RDF dataset (.).作者: patriarch 時(shí)間: 2025-3-30 13:08 作者: originality 時(shí)間: 2025-3-30 20:33
FedSDM: Semantic Data Manager for Federations of RDF Datasetsonstrate FedSDM, a semantic data manager for federations of RDF datasets. Attendees will be able to explore the relationships among the RDF datasets in a federation, as well as the characteristics of the RDF classes included in each RDF dataset (.).作者: GLIB 時(shí)間: 2025-3-30 22:50 作者: glucagon 時(shí)間: 2025-3-31 01:32 作者: anthropologist 時(shí)間: 2025-3-31 08:45 作者: 腐蝕 時(shí)間: 2025-3-31 09:16 作者: Atrium 時(shí)間: 2025-3-31 14:17
Conference proceedings 2019over, Germany, in November 2018..The 5 full, 8 short, 3 poster and 4 demo papers presented in this volume were carefully reviewed and selected from 22 submissions. The papers are organized in topical sections named: big biomedical data integration and management; data exploration in the life science
