Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri A.; Khanna, Shashank; Frisoni, Giovanni B.; Hofmann‐Apitius, Martin

doi:10.3233/jad-161148

Cited by 15 publications

(10 citation statements)

References 72 publications

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“…Curated data represent molecular (“omics”) entities and their interaction and clinical data (including trial data). A comprehensive, semantic framework representing and formalising knowledge about anatomy (FMA [ 113 ], BRCO [ 114 ], NIF [ 115 ]), about major neurodegenerative diseases (ADO [ 64 ], PDON [ 65 ], MSO [ 116 ]) and about assays and readouts (variables) used in clinical trials (NDD-CTO [ 117 ]; NIFT [ 118 ]; clinicaltrials.gov [ 119 ]) enables “shared semantics” over all scales (from the molecular via the cellular and organ level to the cohort and population level) and all entity types (ranging from genes and proteins to cognitive testing and neuro-imaging).…”

Section: Bioinformatics Methods For the Identification Of Disease mentioning

confidence: 99%

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders

Hofmann-Apitius

Ball

Gebel

et al. 2015

IJMS

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Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies—data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

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Section: Bioinformatics Methods For the Identification Of Disease mentioning

confidence: 99%

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders

Hofmann-Apitius

Ball

Gebel

et al. 2015

IJMS

Self Cite

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“…Another example is the Alzheimer's Disease Ontology (ADO, see [17]) E ADO or the Neuro-Image Terminology (NIFT, see [18]) E N IF T coming with their hierarchy R ADO , R N IF T . The process of NER will lead to another context relation E hasAnnotation .…”

Section: B Extending the Knowledge Graph Using Nlp-technologiesmentioning

confidence: 99%

Knowledge Extraction and Applications utilizing Context Data in Knowledge Graphs

Dörpinghaus

Stefan

2019

Annals of Computer Science and Information Systems

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Context is widely considered for NLP and knowledge discovery since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such context data, but also to store this data for further NLP approaches. Here, we propose a multiple step knowledge graphbased approach to utilize context data for NLP and knowledge expression and extraction. We introduce the graph-theoretic foundation for a general context concept within semantic networks and show a proof-of-concept-based on biomedical literature and text mining. We discuss the impact of this novel approach on text analysis, various forms of text recognition and knowledge extraction and retrieval.

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“…We will use SCAIView, see [14] or https://www.scaiview.com), an information retrieval system for knowledge discovery for a similar approach. SCAIView was used in many recent research projects, for example regarding neurodegenerative diseases [15], brain imaging features [16] and other theoretic research like document clustering, see [17]. The advantage is, that SCAIView already provides us with Named Entities for MeSH but also other ontological representing biomedical entities.…”

Section: Examplementioning

confidence: 99%

What was the Question? A Systematization of Information Retrieval and NLP Problems

Dörpinghaus¹,

Darms²,

Jacobs³

2018

Annals of Computer Science and Information Systems

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In this paper we suggest a novel systematization of Information Retrieval and Natural Language Processing problems. Using this rather general description of problems we are able to discuss and proof the equivalence of some problems. We provide reformulations of well-known problems like Named Entity Recognition using our novel description and discuss further research and the expected outcome. We will discuss the relation of two problems, cluster labeling and search query finding. With these results we are able to provide a novel optimization approach to both problems. This novel systematization approach provides a yet unknown view generating new classes of problems in NLP. It brings application and algorithmic approaches together and offers a better description with concepts of theoretical computer science.

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Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features

Cited by 15 publications

References 72 publications

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders

Knowledge Extraction and Applications utilizing Context Data in Knowledge Graphs

What was the Question? A Systematization of Information Retrieval and NLP Problems

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