In this paper we explore the measurement of activity in ontology projects as an aspect of community ontology building. When choosing whether to use an ontology or whether to participate in its development, having some knowledge of how actively that ontology is developed is an important issue. Our knowledge of biology grows and changes and an ontology must adapt to keep pace with those changes and also adapt with respect to other ontologies and organisational principles. In essence, we need to know if there is an 'active' community involved with a project or whether a given ontology is inactive or moribund. We explore the use of additions, deletions and changes to ontology files, the regularity and frequency of releases, and the number of ontology repository updates to an ontology as the basis for measuring activity in an ontology. We present our results of this study, which show a dramatic range of activity across some of the more prominent community ontologies, illustrating very active and mature efforts through to those which appear to have become dormant for a number of possible reasons. We show that global activity within the community has remained at a similar level over the last 2 years. Measuring additions, deletions and changes, together with release frequency, appear to be useful metrics of activity and useful pointers towards future behaviour. Measuring who is making edits to ontologies is harder to capture; this raises issues of record keeping in ontology projects and in micro-credit, although we have identified one ontologist that appears influential across many community efforts; a Super-Ontologist. We also discuss confounding factors in our activity metric and discuss how it can be improved and adopted as an assessment criterion for community ontology development. Overall, we show that it is possible to objectively measure the activity in an ontology and to make some prediction about future activity.
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http://dx.doi.org/10.1016/j.jbi.2012.04.002 | DOI Listing |
Discov Oncol
December 2024
Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Background: Low-grade glioma (LGG) is a slow-growing but invasive tumor that affects brain function. Histone deacetylases (HDACs) play a critical role in gene regulation and tumor progression. This study aims to develop a prognostic model based on HDAC-related genes to aid in risk stratification and predict therapeutic responses.
View Article and Find Full Text PDFDiscov Oncol
December 2024
Department of Hygiene, School of Public Health, Bengbu Medical University, Bengbu, 233030, Anhui, People's Republic of China.
Purpose: This work investigated the effect of FBXO5 in hepatocellular carcinoma (HCC) and the mechanism of action of arbutin in its inhibition.
Methods: FBXO5 mRNA and protein expressions in the tumor were assessed using TCGA, ICGC and HPA databases. Cox regression analysis and Kaplan-Meier survival curves were employed to assess the impact of FBXO5 on the survival outcomes of patients with HCC.
Scand J Gastroenterol
December 2024
Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, China.
Background: Pancreatic adenocarcinoma (PAAD) is a deadly cancer marked by extensive collagen deposition and limited response to immunotherapy. Discoidin domain receptor1 (DDR1), part of the transmembrane receptor tyrosine kinase family, is linked to inflammation regulation and immune cell infiltration. However, its role in controlling cytokines and chemokines in the microenvironment of PAAD is still unclear.
View Article and Find Full Text PDFFront Psychiatry
December 2024
Department of Psychiatry, The Fifth Hospital of Shanxi Medical University, The Fifth Clinical Medical College of Shanxi Medical University, Shanxi Provincial People's Hospital, Taiyuan, China.
Background: Major depressive disorder (MDD) is a severe psychiatric disorder characterized by complex etiology, with genetic determinants that are not fully understood. The objective of this study was to investigate the pathogenesis of MDD and to explore its association with the immune system by identifying hub biomarkers using bioinformatics analyses and examining immune infiltrates in human autopsy samples.
Methods: Gene microarray data were obtained from the Gene Expression Omnibus (GEO) datasets GSE32280, GSE76826, GSE98793, and GSE39653.
Clin Cosmet Investig Dermatol
December 2024
Department of Dermatology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Objective: Alopecia areata (AA) is an autoimmune skin disease. Observational studies have reported an association between AA and cancer. However, the causal relationship between AA and cancer has not been reported.
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