Are morpho-syntactic features more predictive for the resolution of noun phrase coordination ambiguity than lexico-semantic similarity scores?

CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.

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Multi-faceted Semantic Clustering With Text-derived Phenotypes

10.1101/2021.05.26.21257830 ◽

2021 ◽

Author(s):

Luke T Slater ◽

John A Williams ◽

Andreas Karwath ◽

Hilary Fanning ◽

Simon Ball ◽

...

Keyword(s):

Semantic Similarity ◽

Unitary Similarity ◽

Human Phenotype ◽

Semantic Clustering ◽

Formal Ontologies ◽

Clinical Narrative ◽

Nuanced Understanding ◽

Complex Relationships ◽

Evaluation Techniques ◽

Similarity Scores

Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients or drugs. Constructing patient phenotype profiles using formal ontologies enables their analysis via semantic similarity, in turn enabling the use of background knowledge in clustering or classification analyses. However, traditional semantic similarity approaches collapse complex relationships between patient phenotypes into a unitary similarity scores for each pair of patients. Moreover, single scores may be based only on matching terms with the greatest information content (IC), ignoring other dimensions of patient similarity. This process necessarily leads to a loss of information in the resulting representation of patient similarity, and is especially apparent when using very large text-derived and highly multi-morbid phenotype profiles. Moreover, it renders finding a biological explanation for similarity very difficult; the black box problem. In this article, we explore the generation of multiple semantic similarity scores for patients based on different facets of their phenotypic manifestation, which we define through different sub-graphs in the Human Phenotype Ontology. We further present a new methodology for deriving sets of qualitative class descriptions for groups of entities described by ontology terms. Leveraging this strategy to obtain meaningful explanations for our semantic clusters alongside other evaluation techniques, we show that semantic clustering with ontology-derived facets enables the representation, and thus identification of, clinically relevant phenotype relationships not easily recoverable using overall clustering alone. In this way, we demonstrate the potential of faceted semantic clustering for gaining a deeper and more nuanced understanding of text-derived patient phenotypes.

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Information Content-Based Gene Ontology Semantic Similarity Approaches: Toward a Unified Framework Theory

BioMed Research International ◽

10.1155/2013/292063 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 31

Author(s):

Gaston K. Mazandu ◽

Nicola J. Mulder

Keyword(s):

Gene Ontology ◽

Information Content ◽

Semantic Similarity ◽

Experimental Evaluation ◽

Similarity Measures ◽

Mathematical Framework ◽

Unified Framework ◽

The Impact ◽

Unified Description ◽

Similarity Scores

Several approaches have been proposed for computing term information content (IC) and semantic similarity scores within the gene ontology (GO) directed acyclic graph (DAG). These approaches contributed to improving protein analyses at the functional level. Considering the recent proliferation of these approaches, a unified theory in a well-defined mathematical framework is necessary in order to provide a theoretical basis for validating these approaches. We review the existing IC-based ontological similarity approaches developed in the context of biomedical and bioinformatics fields to propose a general framework and unified description of all these measures. We have conducted an experimental evaluation to assess the impact of IC approaches, different normalization models, and correction factors on the performance of a functional similarity metric. Results reveal that considering only parents or only children of terms when assessing information content or semantic similarity scores negatively impacts the approach under consideration. This study produces a unified framework for current and future GO semantic similarity measures and provides theoretical basics for comparing different approaches. The experimental evaluation of different approaches based on different term information content models paves the way towards a solution to the issue of scoring a term’s specificity in the GO DAG.

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Revealing and avoiding bias in semantic similarity scores for protein pairs

BMC Bioinformatics ◽

10.1186/1471-2105-11-290 ◽

2010 ◽

Vol 11 (1) ◽

pp. 290 ◽

Cited By ~ 33

Author(s):

Jing Wang ◽

Xianxiao Zhou ◽

Jing Zhu ◽

Chenggui Zhou ◽

Zheng Guo

Keyword(s):

Semantic Similarity ◽

Similarity Scores

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Syntactic features of medieval Hispano-Romance

10.1093/oso/9780199687312.003.0004 ◽

2018 ◽

Author(s):

Steven N. Dworkin

Keyword(s):

Noun Phrase ◽

Word Order ◽

Direct Object ◽

Standard Language ◽

Old Spanish ◽

Subject Pronouns ◽

Syntactic Features ◽

Object Marking ◽

Syntactic Differences ◽

Modern Standard

This chapter describes selected issues of noun phrase, verb phrase, and sentential syntax. It emphasizes differences between the selected constructions in Old Spanish and in the modern standard language. Specific issues discussed include the function of determiners, the use of subject pronouns, the preverbal or postverbal placement of clitic object pronouns, direct object marking, and issues involving subject-verb-object and noun-adjective word order. The section on verbal syntax examines the use of the present, imperfect, and preterit tenses in medieval Hispano-Romance, the syntax of analytic or compound tenses, the syntactic differences between the synthetic and analytic futures, the syntax and semantics of the subjunctive, and the syntax of aver/tener and ser/estar.

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Zande

The Oxford Handbook of African Languages ◽

10.1093/oxfordhb/9780199609895.013.52 ◽

2020 ◽

pp. 520-529

Author(s):

Helma Pasch

Keyword(s):

Noun Phrase ◽

Personal Pronouns ◽

Serial Verb Constructions ◽

Syntactic Features ◽

Verb Constructions ◽

Possessive Constructions ◽

Floating Quantifiers ◽

Serial Verb ◽

Secondary Predicates

The morphology of Zande (Ubangi) has been known since the earliest descriptions of the 1920s by Gore and Lagae. This chapter focuses on recently discovered syntactic features. The first are the functions of three copulas, the functions of the two series of personal pronouns in possessive constructions, and the functions of secondary predicates. There are also intransitive copy pronouns which mark substantivized adjectives, floating quantifiers and numerals which follow the entire noun phrase, and the functions of the preposition be ‘from, because of’ which is derived from the denotation for ‘hand’. The verb ya ‘say’ has undergone multiple grammaticalizations and functions as a complementizer, and in serial verb constructions it marks immediate anteriority or ineffective attempt.

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A Comparative Study of the Simple Clause in Akan, Dagaare and English

Education and Linguistics Research ◽

10.5296/elr.v7i1.18353 ◽

2021 ◽

Vol 7 (1) ◽

pp. 62

Author(s):

Levina Nyameye Abunya ◽

Edward Owusu ◽

Faustina Marius Naapane

Keyword(s):

Noun Phrase ◽

Word Order ◽

Significant Variation ◽

Language Family ◽

Linguistic Features ◽

African Languages ◽

Syntactic Features ◽

Basic Word ◽

Serial Verb Construction ◽

Serial Verb

The paper compares how the simple clause is expressed in Akan (Kwa, Niger-Congo), Dagaare (Gur, Niger-Congo) and English. It examines the simple clause in relation to noun phrase, verbal phrases, adpositional phrases, basic word order in declarative and focus constructions, and the basic locative construction. Basically, the study reveals that despite the differences, Akan and Dagaare have a lot in common as compared to English. This of course shows how distant English is from the two African languages. Certain linguistic features such as serial verb construction and focus constructions were unique to Akan and Dagaare and this, is not surprising since languages within the same language family (Niger Congo) tend to share certain lexical, phonological, morphological and syntactic features. The significant variation between these languages shows where Akan and Dagaare languages diverge into other sub-family groups: Kwa and Gur, respectively.

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