Obesity Prediction Service
InquiryOverview
Obesity is governed by environmental, epigenetic, and genetic factors, their complex interactions, etc., and is associated with many chronic diseases. Genome-wide association studies (GWAS), polygenic risk scores, and obesity-associated candidate gene screens are used to identify genetic predispositions to obesity. Dietary and lifestyle behaviors are key modifiable determinants of obesity and are analyzed for the development of prevention and treatment strategies. Based on these multiple strategies, Protheragen provides a high-quality obesity prediction service and translates our research experience into the cornerstone of obesity research. Our goal is to develop effective prediction tools, identify factors that modify the risk of obesity, and ultimately develop effective methods to prevent and treat obesity. In addition to this, we provide high-quality anti-obesity therapy development and preclinical research work and provide comprehensive support to our clients at all stages of their obesity research.
High-quality Anti-Obesity Therapy Development
Target screening
The therapeutic mechanisms of anti-obesity therapies include increasing energy expenditure, suppressing appetite, promoting white adipose tissue transformation, etc. Currently, a variety of therapeutic targets have been developed that exert anti-obesity effects, including various receptors, signaling molecules, and so on. We screen suitable targets and design specific anti-obesity therapeutics according to our client's research needs.
Therapeutic development
Gene therapy, cell therapy, antibody therapy, tissue engineering therapy, and nanotherapies are all innovative strategies that have an important place in anti-obesity research. We provide services to support these therapies from scratch design to preclinical studies. In addition to this, we screen and synthesize various anti-obesity drugs and analyze their therapeutic efficacy and safety.
Comprehensive Preclinical Studies
We have a variety of obesity models to support preclinical studies for the development of anti-obesity therapies. Based on our extensive experience in this area, we test the effects of these therapeutic strategies on energy metabolism, behavior, obesity reversal, etc., and evaluate their safety.
| Obesity Models | Evaluation of obesity therapies |
|---|---|
Unlocking New Insights: Obesity Prediction Services Based on Multiple Strategies
Flexible solutions ensure that clients receive comprehensive services and a wealth of expertise. Specific service processes include the following:
- Genetic and Epigenetic Sequencing-based Obesity Risk Analysis Service
The analysis involves whole genome sequencing (WGS), whole exome sequencing (WES), epigenetic sequencing, bioinformatics analysis, etc. Advanced algorithms and instruments give us the ability to rapidly analyze large amounts of data and provide reliable support for obesity risk analysis. Detailed analyses include the following:
- WGS-based Obesity-Related Gene Identification Service
- WES-based Obesity-Related Gene Identification Service
- Epigenome-wide Association Studies (EWAS)-based Obesity-Related Epigenetic Modification Analysis Service
- Multi-Omics Integration Analysis Service for Obesity Risk Prediction
- Functional Annotation and Pathway Analysis Service for Obesity Risk Prediction
- Gene-Environment Interactions Analysis Service for Obesity Risk Prediction
- Individual Genetic Profiling Service for Obesity Risk Prediction
- Physio-psycho-social Model-based Obesity Prediction Service
The biopsychosocial approach is an important way to understand diseases such as obesity. We construct biopsychosocial models and analyze the correlation between biological, psychological, and social factors and obesity to predict obesity.
- Blood Test-based Obesity Prediction Service
Blood tests and comparative analyses of leptin, lipocalin, etc., provide a large amount of information and improve the accuracy and precision of obesity prediction. We offer a comprehensive blood test-based obesity prediction service. A variety of advanced technologies provide detailed and reliable methods for blood analysis.
- Machine Learning and Predictive Modeling
GWAS and EWAS have generated large amounts of data and unlocked insightful descriptions of obesity and its associated traits. Applying machine learning methods to large and high-dimensional data is an effective strategy for exploring complex data and predicting obesity phenotypes. We develop integrated machine learning approaches that combine lifestyle and multiple histological data and consider their interactions to predict overweight and obesity status. Specific services include the following:
- Machine Learning-based Obesity Prediction Model Development Service
- Genetic Variant Panel Assisted Obesity Prediction Model Development Service
- Bioinformatics-based New Obesity Gene Screening Service
Screening of obesity genes is an important tool to explore the genetic mechanisms of obesity. We combine molecular biology technology, bioinformatics technology and other means to screen and elucidate the function of new obesity genes, the structure of gene expression promoters, the basis of the regulatory mechanism, and the role in obesity. Our team has accumulated many years of service experience and is adept at mining key information from complex experimental data and providing flexible solutions.
- Obesity-related Therapeutic Target Identification Service
Obesity-related therapeutic target identification is one of the most important aspects of developing innovative and effective therapies. Based on years of analytical experience, we provide a customized obesity-related therapeutic target identification service. Through a combination of computer simulation, in vitro analysis, and in vivo validation, we explore the therapeutic targets of obesity. Specific services include the following:
- Obesity-related Candidate Gene Identification Service
- Obesity-related Drug-Gene Interaction Analysis Service
Workflow
Applications
- Research on obesity molecular mechanism: Screening and analysis of new obesity-related genes provide valuable information for deepening research on the molecular mechanism of obesity.
- Development of anti-obesity therapies: Identification of therapeutic targets helps promote the development of more effective anti-obesity drugs, gene therapy, cell therapy, etc.
- Obesity intervention research: The prediction of obesity risk helps develop obesity intervention measures to reduce the risk of obesity and other complications.
Advantages
- Advanced instrumentation, software, and algorithms enable us to offer a full range of obesity prediction services based on genetic and epigenetic sequencing, and various models.
- We use statistical modeling and machine learning to efficiently analyze data to improve the accuracy of obesity prediction.
- We keep you updated on the progress of your experimental projects and address any problems encountered in obesity research.
Publication Data
Technology: Pathway enrichment analysis
Journal: Frontiers in Genetics
IF: 3.7
Published: 2022
Results: This study developed a model by describing the relationships and interactions of factors that influence obesity. The researchers performed a combined genome-wide and epigenome scan for three-way interactions between DNA methylation sites, single nucleotide polymorphisms (SNP), body mass index, diet, and lifestyle. After identifying genetic, epigenetic, and dietary factors, machine-learning algorithms were used to predict obesity in testers. A stochastic gradient boosting model was found to have the highest prediction accuracy of 70% for obesity by comparison. This study extends the understanding of obesity drivers and informs precise strategies to prevent and treat obesity.
Frequently Asked Questions
How can GWAS be applied to obesity research?
GWAS are used to identify sequence variants in the whole genome through whole genome resequencing, and then filter out obesity-related SNPs through various statistical analyses. It rapidly and accurately locates multiple target genes and then obtains obesity-related candidate genes or genomic regions, which has various advantages.
How can EWAS be applied to obesity research?
EWAS is an important tool for screening obesity-related methylation sites. It obtains genotypes by testing multiple individuals for genetic variants on a genome-wide scale and then combines them with accurate phenotypes for statistical analyses to screen for genetic variants that are most likely to affect obesity.
Protheragen integrates multiple technologies and algorithms to provide a high-quality obesity prediction service. Our team is with you every step of the way and will help your study succeed. Welcome to contact us to find out how an obesity prediction solution advances your research.
Reference
- Lee, Y.C.; et al. Using machine learning to predict obesity based on genome-wide and epigenome-wide gene-gene and gene-diet interactions. Frontiers in genetics. 2022, 12: 783845.
All of our services and products are intended for preclinical research use only and cannot be used to diagnose, treat or manage patients.