Dataset Information

Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering.

ABSTRACT: Aim of this procedural method is to construct well-founded corpora of scientific literature, and, hence, to track the evolution of knowledge fields from the reconstruction and clustering of words' life-cycles. The method contains: •an original selection process of relevant keywords involving the identification of relevant stems and stem n-grams through a matching with item lists of relevant glossaries;•several types of normalization of temporal trajectories of word raw frequencies•a properly customized clustering of word life-cycles, with a graphical extensive investigation of the best candidates for cluster number, to unveil the important dynamics and decipher the history of a scientific field.

SUBMITTER: Trevisani M

PROVIDER: S-EPMC6287063 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering.

Trevisani Matilde M Tuzzi Arjuna A

MethodsX 20181119

Aim of this procedural method is to construct well-founded corpora of scientific literature, and, hence, to track the evolution of knowledge fields from the reconstruction and clustering of words' life-cycles. The method contains: •an original selection process of relevant keywords involving the identification of relevant stems and stem <i>n</i>-grams through a matching with item lists of relevant glossaries;•several types of normalization of temporal trajectories of word raw frequencies•a prope ...[more]

PMID: 30568881

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Project description:Background Limited knowledge and unclear underlying biology of many rare diseases pose significant challenges to patients, clinicians, and scientists. To address these challenges, there is an urgent need to inspire and encourage scientists to propose and pursue innovative research studies that aim to uncover the genetic and molecular causes of more rare diseases and ultimately to identify effective therapeutic solutions. A clear understanding of current research efforts, knowledge/research gaps, and funding patterns as scientific evidence is crucial to systematically accelerate the pace of research discovery in rare diseases, which is an overarching goal of this study. Methods To semantically represent NIH funding data for rare diseases and advance its use of effectively promoting rare disease research, we identified NIH funded projects for rare diseases by mapping GARD diseases to the project based on project titles; subsequently we presented and managed those identified projects in a knowledge graph using Neo4j software, hosted at NCATS, based on a pre-defined data model that captures semantics among the data. With this developed knowledge graph, we were able to perform several case studies to demonstrate scientific evidence generation for supporting rare disease research discovery. Results Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting amongst mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research. Conclusion We developed an integrative knowledge graph with rare disease funding data and demonstrated its use as a source from where we can effectively identify and generate scientific evidence to support rare disease research. With the success of this preliminary study, we plan to implement advanced computational approaches for analyzing more funding related data, e.g., project abstracts and PubMed article abstracts, and linking to other types of biomedical data to perform more sophisticated research gap analysis and identify opportunities for future research in rare diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s13023-021-02120-9.

Project description:The molecular and cellular processes required for development, function, and regression of the primate corpus luteum (CL) are poorly defined. We hypothesized that there are dynamic changes in gene expression occurring during the CL lifespan, which represent proteins and pathways critical to its regulation. Therefore, a genomic approach was utilized to systematically identify differentially expressed genes in the rhesus macaque CL during the luteal phase of natural menstrual cycles. CL were collected between days 3-5 (early stage), 7-8 (mid), 10-12 (mid-late), 14-16 (late), or 18-19 (very-late) after the midcycle LH surge. From the early through very-late stages, 3234 transcripts were differentially expressed, with 879 occurring from the early through late stages that encompass the processes of luteinization, maintenance, and functional regression. To characterize gene changes most relevant to these processes, ontology analysis was performed using the list of 879 differentially expressed transcripts. Four main groups of related genes were identified with relevance to luteal physiology including: 1) immune function; 2) hormone and growth factor signaling; 3) steroidogenesis; and 4) prostaglandin biosynthesis, metabolism, and signaling. A subset of genes representing each of the four major categories was selected for validation of microarray results by quantitative real-time PCR. Results in mRNA levels were similar between the two methodologies for 17 of 18 genes. Additionally, protein levels for 3 genes were determined by Western blot analysis to parallel mRNA levels. This database will facilitate the identification of many novel or previously underappreciated pathways that regulate the structure and function of the primate CL. Keywords: time course The sole experimental factor is the stage of the luteal phase when the CL was collected. There were five stages: 1) between days 3-5 post LH-surge (early stage), 2) 7-8 (mid stage), 3) 10-12 (mid-late stage), 4) 14-16 (late stage), and 5) 18-19 (very-late stage). The early CL are undergoing luteinization, the mid are fully functional CL that are at their peak progesterone producing capacity, the mid-late stage is a transitionary period where CL are still producing significant quantities of progesterone but are nearing the time when luteolysis initiates in non-conception cycles, the late stage corresponds to CL undergoing functional regression (cessation of progesterone secretion), and the very-late stage (menses) is when the structural remodeling and apoptosis associated with luteolysis is occurring. Measuring mRNA levels in CL collected during these periods provides a comprehensive analysis of the primate transcriptome throughout CL lifespan. There were 20 biological replicates: n = 4 per stage, 5 stages in total. The early CL were used as the baseline reference for gene expression.

Dataset Information

Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering.

Publications

Chronological corpora curve clustering: From scientific corpora construction to knowledge dynamics discovery through word life-cycles clustering.

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