The development of non-coding RNA ontology

Huang, Jingshan; Eilbeck, Karen; Blake, Judith A.; Dou, Dejing; Wei-li, HU; Natale, Darren A.; Ruttenberg, Alan; Huan, Jun; Zimmermann, Michael T.; Jiang, Guoqian; Lin, Yuling; Wu, Bin; Strachan, Harrison J.; Silva, Nisansa de; Kasukurthi, Mohan Vamsi; Jha, Vikash Kumar; He, Yongqun; Zhang, Shaojie; Wang, Xiaowei; Liu, Zixing; Borchert, Glen M.; Tan, Ming

doi:10.1504/ijdmb.2016.077072

Cited by 9 publications

(9 citation statements)

References 20 publications

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“…The OmniSearch analytical software was built upon semantic technologies (including semantic data annotation, semantic data integration, and semantic search) that are based on two domain ontologies, Ontology for MIcroRNA Targets (OMIT) [20–22] and Non-Coding RNA Ontology (NCRO) [17,18]. OmniSearch was developed to handle the significant challenge of effective miR data integration and knowledge acquisition.…”

Section: Methodsmentioning

confidence: 99%

“…Bio-ontologies have been widely utilized nowadays, such as: Gene Ontology (GO) [16], the most successful and widely used bio-ontology with three independent sub-ontologies (biological processes, molecular functions, and cellular components); Non-Coding RNA Ontology (NCRO) [17,18], an Open Biological and Biomedical Ontologies (OBO) [19] candidate reference ontology in non-coding RNA (ncRNA) domain; and Ontology for MIcroRNA Target (OMIT) [20–22], an application ontology to provide the community with common data elements and data exchange standards in the miR research.…”

Section: Related Workmentioning

confidence: 99%

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Co-expression analysis among microRNAs, long non-coding RNAs, and messenger RNAs to understand the pathogenesis and progression of diabetic kidney disease at the genetic level

Zhang

et al. 2017

Methods

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Diabetic kidney disease (DKD) is a serious disease that presents a major health problem worldwide. There is a desperate need to explore novel biomarkers to further facilitate the early diagnosis and effective treatment in DKD patients, thus preventing them from developing end-stage renal disease (ESRD). However, most regulation mechanisms at the genetic level in DKD still remain unclear. In this paper, we describe our innovative methodologies that integrate biological, computational, and statistical approaches to investigate important roles performed by regulations among microRNAs (miRs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs) in DKD. We conducted fully transparent, rigorously designed experiments. Our robust and reproducible results identified hsa-miR-223-3p as a candidate novel biomarker performing important roles in DKD disease process.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Co-expression analysis among microRNAs, long non-coding RNAs, and messenger RNAs to understand the pathogenesis and progression of diabetic kidney disease at the genetic level

Zhang

et al. 2017

Methods

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“…As defined by Thomas R. Gruber [2], 'ontologies are an explicit and formal specifications of the terms in the domain and the relations among them'. Ontologies have been expanding out from the realm of Artificial-Intelligence to domain specific tasks such as: Linguistics [4,5,9,21,22], Law [19], Medicine [10,11,13,14,16]. Ontologies have become common on the semantic iteration of the World-Wide Web [12].…”

Section: A Ontologiesmentioning

confidence: 99%

“…Due to the evolving ability of ontologies to overcome limitations in traditional natural language processing methods, the popularity of using ontologies in modern computation tasks are getting increased day by day. For an example, text classification [3], [4], word set expansions [5], linguistic information management [13], [14], and information extraction [15], [16] emphasize the growing popularity of the ontology based computations and processing.…”

Section: Introductionmentioning

confidence: 99%

Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population

Jayawardana

Silva

Perera

et al. 2018

Int J on Adv. in ICT for Emerging Countries

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Abstract-An ontology defines a set of representational primitives which model a domain of knowledge or discourse. With the arising fields such as information extraction and knowledge management, the role of ontology has become a driving factor of many modern day systems. Ontology population, on the other hand, is an inherently problematic process, as it needs manual intervention to prevent the conceptual drift. The semantic sensitive word embedding has become a popular topic in natural language processing with its capability to cope with the semantic challenges. Incorporating domain specific semantic similarity with the word embeddings could potentially improve the performance in terms of semantic similarity in specific domains. Thus, in this study, we propose a novel way of semi-supervised ontology population through word embeddings and domain specific semantic similarity as the basis. We built several models including traditional benchmark models and new types of models which are based on word embeddings. Finally, we ensemble them together to come up with a synergistic model which outperformed the candidate models by 33% in comparison to the best performed candidate model.

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“…Textpresso [2] is a text mining, information extraction, and information retrieval system that goes far beyond traditional keyword search engines, built for biological literature. Further in the biological domain, OmniSearch [3] is a semantic search system based on the ontology for microRNA-target gene interaction data [4]. All other fields such as medicine [5], geology [6], and music [7] also have their own unique aspects which make the information retrieval task more complex and domain specific.…”

Section: Introductionmentioning

confidence: 99%

Legal Document Retrieval Using Document Vector Embeddings and Deep Learning

Sugathadasa

Ayesha

Silva

et al. 2018

Advances in Intelligent Systems and Computing

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Domain specific information retrieval process has been a prominent and ongoing research in the field of natural language processing. Many researchers have incorporated different techniques to overcome the technical and domain specificity and provide a mature model for various domains of interest. The main bottleneck in these studies is the heavy coupling of domain experts, that makes the entire process to be time consuming and cumbersome. In this study, we have developed three novel models which are compared against a golden standard generated via the on line repositories provided, specifically for the legal domain. The three different models incorporated vector space representations of the legal domain, where document vector generation was done in two different mechanisms and as an ensemble of the above two. This study contains the research being carried out in the process of representing legal case documents into different vector spaces, whilst incorporating semantic word measures and natural language processing techniques. The ensemble model built in this study, shows a significantly higher accuracy level, which indeed proves the need for incorporation of domain specific semantic similarity measures into the information retrieval process. This study also shows, the impact of varying distribution of the word similarity measures, against varying document vector dimensions, which can lead to improvements in the process of legal information retrieval.

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The development of non-coding RNA ontology

Cited by 9 publications

References 20 publications

Co-expression analysis among microRNAs, long non-coding RNAs, and messenger RNAs to understand the pathogenesis and progression of diabetic kidney disease at the genetic level

Co-expression analysis among microRNAs, long non-coding RNAs, and messenger RNAs to understand the pathogenesis and progression of diabetic kidney disease at the genetic level

Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population

Legal Document Retrieval Using Document Vector Embeddings and Deep Learning

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