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Data Integration in the Life Sciences de Sören Auer

Data Integration in the Life Sciences: Lecture Notes in Computer Science, cartea 11371

Editat de Sören Auer, Maria-Esther Vidal
en Limba Engleză Paperback – 31 dec 2018
This book constitutes revised selected papers from the 13th International Conference on Data Integration in the Life Sciences, DILS 2018, held in Hannover, 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 sciences; biomedical data analytics; and big biomedical applications.
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Specificații

ISBN-13: 9783030060152
ISBN-10: 3030060152
Pagini: 232
Ilustrații: XI, 218 p. 81 illus., 62 illus. in color.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.36 kg
Ediția:1st edition 2019
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Big Biomedical Data Integration and Management.- Do Scaling Algorithms Preserve Word2Vec Semantics? A Case Study for Medical Entities.- Combining semantic and lexical measures to evaluate medical terms similarity.- Construction and Visualization of Dynamic Biological Networks: Benchmarking the Neo4J Graph Database.- A Knowledge-driven Pipeline from Transforming Big Data into Actionable Knowledge.- Leaving no stone unturned: Using machine learning based approaches for information extraction from full texts of a research data warehouse.- Data Exploration in the Life Sciences.- Towards research infrastructures that curate scientific information: A use case in life sciences.- Interactive Visualization for large-scale multi-factorial Research Designs.- FedSDM: Semantic Data Manager for Federations of RDF Datasets.- Data Integration for Supporting Biomedical Knowledge Graph Creation at Large-Scale.- DISBi: A flexible framework for integrating systems biology data.- Biomedical Data Analytics.- Using Machine Learning to Distinguish Infected from Non-Infected Subjects at an Early Stage Based on Viral Inoculation.- Automated Coding of Medical Diagnostics from Free-Text: the Role of Parameters Optimization and Imbalanced Classes.- A learning-based approach to combine medical annotation results.- Knowledge Graph Completion to Predict Polypharmacy Side Effects.- Big Biomedical Applications.- Lung Cancer Concept Annotation from Spanish Clinical Narratives.- Linked Data based Multi-Omics Integration and Visualization for Cancer Decision Networks.- The Hannover Medical School Enterprise Clinical Research Data Warehouse: 5 years of experience.- User-Driven Development of a Novel Molecular Tumor Board Support Tool.- Using Semantic Programming for developing a Web Content Management System for semantic Phenotype Data.- Converting Alzheimer's disease map into a heavyweight ontology: a formal network to integrate data.