ArticleBioData mining2017
OCDD: an obesity and co-morbid disease database.
Article in BioData mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 16 citations in OpenAlex.
- Type 2 Diabetes in Obesity: A Systems Biology Study on Serum and Adipose Tissue Proteomic Profiles.International journal of molecular sciences · 2023Article
- ScanBious: Survey for Obesity Genes Using PubMed Abstracts and DisGeNET.Journal of personalized medicine · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObesity is a medical condition that is known for increased body mass index (BMI). It is also associated with chronic low level inflammation. Obesity disrupts the immune-metabolic homeostasis by changing the secretion of adipocytes. This affects the end-organs, and gives rise to several diseases including type 2 diabetes, asthma, non-alcoholic fatty liver diseases and cancers. These diseases are known as co-morbid diseases. Several studies have explored the underlying molecular mechanisms of developing obesity associated comorbid diseases. To understand the development and progression of diseases associated with obesity, we need a detailed scenario of gene interactions and the distribution of the responsible genes in human system.
resultsObesity and Co-morbid Disease Database (OCDD) is designed for relating obesity and its co-morbid diseases using literature mining, and computational and systems biology approaches. OCDD is aimed to investigate the genes associated with comorbidity. Several existing databases have been used to extract molecular interactions and functional annotations of each gene. The degree of co-morbid associations has been measured and made available to the users. The database is available at http://www.isical.ac.in/~systemsbiology/OCDD/home.php.
conclusionsThe main objective of the database is to derive the relations among the genes that are involved in both obesity and its co-morbid diseases. Functional annotation of common genes, gene interaction networks and key driver analyses have made the database a valuable and comprehensive resource for investigating the causal links between obesity and co-morbid diseases.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.