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Data Integration in the Life Sciences

Overview of attention for book
Cover of 'Data Integration in the Life Sciences'

Table of Contents

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    Book Overview
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    Chapter 1 Combining Multiple Knowledge Sources: A Case Study of Drug Induced Liver Injury
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    Chapter 2 GEM: The GAAIN Entity Mapper.
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    Chapter 3 Data Integration in the Human Brain Project
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    Chapter 4 SchizConnect: Virtual Data Integration in Neuroimaging.
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    Chapter 5 Annotating Medical Forms Using UMLS
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    Chapter 6 OnSim: A Similarity Measure for Determining Relatedness Between Ontology Terms
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    Chapter 7 AnnEvol: An Evolutionary Framework to Description Ontology-Based Annotations
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    Chapter 8 Terminology development towards harmonizing multiple clinical neuroimaging research repositories.
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    Chapter 9 Creating Biomedical Ontologies Using mOntage
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    Chapter 10 Creation of Definitions for Ontologies: A Case Study in the Leukemia Domain
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    Chapter 11 Integration of Hematopoietic Cell Transplantation Outcomes Data
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    Chapter 12 ICD Code Retrieval: Novel Approach for Assisted Disease Classification
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    Chapter 13 Integration of Multimodal Neuroimaging and Electroencephalography for the Study of Acute Epileptiform Activity After Traumatic Brain Injury
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    Chapter 14 SVI: A Simple Single-Nucleotide Human Variant Interpretation Tool for Clinical Use
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    Chapter 15 Quality Control Considerations for the Effective Integration of Neuroimaging Data
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    Chapter 16 Integration of Behavioral, Structural, Functional, and Genetic Data for the Study of Autism Spectrum Disorders
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    Chapter 17 Reverse Engineering Measures of Clinical Care Quality: Sequential Pattern Mining
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    Chapter 18 Inference and Verification of Probabilistic Graphical Models from High-Dimensional Data
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    Chapter 19 SPIRIT-ML: A Machine Learning Platform for Deriving Knowledge from Biomedical Datasets
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    Chapter 20 Demonstration: Mining Sentence and Annotation Evidence for a Cross Genome Study of the Plant Hormone Ethylene
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    Chapter 21 GAAIN Virtual Appliances: Virtual Machine Technology for Scientific Data Analysis
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    Chapter 22 The GAAIN Entity Mapper: Towards Practical Medical Informatics Application
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    Chapter 23 Efficient Management of Cached Data in the Global Alzheimer’s Association Interactive Network
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    Chapter 24 Domain Specific Document Retrieval Framework for Real-Time Social Health Data
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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Citations

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Title
Data Integration in the Life Sciences
Published by
Lecture notes in computer science, January 2015
DOI 10.1007/978-3-319-21843-4
ISBNs
978-3-31-921842-7, 978-3-31-921843-4
Editors

Naveen Ashish, Jose-Luis Ambite

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 November 2018.
All research outputs
#5,260,874
of 24,998,746 outputs
Outputs from Lecture notes in computer science
#1,569
of 8,155 outputs
Outputs of similar age
#70,444
of 364,470 outputs
Outputs of similar age from Lecture notes in computer science
#73
of 257 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,155 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 80% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 364,470 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 257 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.