GEM: The GAAIN Entity Mapper

Ashish, Naveen; Dewan, Peehoo; Ambite, José Luis; Toga, Arthur W.

doi:10.1007/978-3-319-21843-4_2

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Here we report the second version of the GEM system (Ashish et al, 2015 ). The first version (GEM-1.0) (Ashish et al, 2015 ), deployed in December 2014 is a knowledge driven system. Element matches are determined in a heuristic manner based on element similarity derived off the element metadata in data dictionaries.…”

Section: Methodsmentioning

confidence: 99%

“…In GEM-1.0 we determined element matches by (i) Blocking or filtering out improbable match candidates based on metadata constraints (the METADATA FILTER in Figure 1 ) and (ii) Ranking probable matches in order of the element similarity that was based on the element text description similarity. GEM-1.0 is described in more detail in Ashish et al ( 2015 ).…”

Section: Methodsmentioning

confidence: 99%

“…Many of these features are synthesized from the information extracted from the data dictionary about the elements. For instance the text description similarity scores and ranks are synthesized from the element text descriptions, this is described in more detail in Ashish et al ( 2015 ). The name match score is provided by a name-matching classification module.…”

Section: Methodsmentioning

confidence: 99%

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The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping

Ashish

Dewan

Toga

2016

Front. Neuroinform.

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This work is focused on mapping biomedical datasets to a common representation, as an integral part of data harmonization for integrated biomedical data access and sharing. We present GEM, an intelligent software assistant for automated data mapping across different datasets or from a dataset to a common data model. The GEM system automates data mapping by providing precise suggestions for data element mappings. It leverages the detailed metadata about elements in associated dataset documentation such as data dictionaries that are typically available with biomedical datasets. It employs unsupervised text mining techniques to determine similarity between data elements and also employs machine-learning classifiers to identify element matches. It further provides an active-learning capability where the process of training the GEM system is optimized. Our experimental evaluations show that the GEM system provides highly accurate data mappings (over 90% accuracy) for real datasets of thousands of data elements each, in the Alzheimer's disease research domain. Further, the effort in training the system for new datasets is also optimized. We are currently employing the GEM system to map Alzheimer's disease datasets from around the globe into a common representation, as part of a global Alzheimer's disease integrated data sharing and analysis network called GAAIN1. GEM achieves significantly higher data mapping accuracy for biomedical datasets compared to other state-of-the-art tools for database schema matching that have similar functionality. With the use of active-learning capabilities, the user effort in training the system is minimal.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping

Ashish

Dewan

Toga

2016

Front. Neuroinform.

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“…TADbit [19] provides two strategies for mapping via GEM [39,40]: iterative and trimming mapping methods used in hiclib and HiC‐Pro respectively. Users can choose either method depending on whether the enzyme is known or not.…”

Section: Popular Hi‐c Data Analyzing Toolsmentioning

confidence: 99%

Computational tools for Hi‐C data analysis

Han

Wei

2017

Quant. Biol.

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Background: In eukaryotic genome, chromatin is not randomly distributed in cell nuclei, but instead is organized into higher-order structures. Emerging evidence indicates that these higher-order chromatin structures play important roles in regulating genome functions such as transcription and DNA replication. With the advancement in 3C (chromosome conformation capture) based technologies, Hi-C has been widely used to investigate genome-wide longrange chromatin interactions during cellular differentiation and oncogenesis. Since the first publication of Hi-C assay in 2009, lots of bioinformatic tools have been implemented for processing Hi-C data from mapping raw reads to normalizing contact matrix and high interpretation, either providing a whole workflow pipeline or focusing on a particular process. Results: This article reviews the general Hi-C data processing workflow and the currently popular Hi-C data processing tools. We highlight on how these tools are used for a full interpretation of Hi-C results. Conclusions: Hi-C assay is a powerful tool to investigate the higher-order chromatin structure. Continued development of novel methods for Hi-C data analysis will be necessary for better understanding the regulatory function of genome organization.

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The GAAIN Entity Mapper: Towards Practical Medical Informatics Application

Dewan

2015

Lecture Notes in Computer Science

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GEM: The GAAIN Entity Mapper

Cited by 5 publications

References 21 publications

The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping

The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping

Computational tools for Hi‐C data analysis

The GAAIN Entity Mapper: Towards Practical Medical Informatics Application

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