Deepbiomics
Solution for advanced insights in health analysis
DeepBiomics represents an advanced approach to integrating and analyzing multiomics data through deep learning techniques. It focuses on utilizing sophisticated deep learning algorithms to handle complex and high-dimensional biological data from from genomics, transcriptomics, proteomics, and clinical phenotypes.
How does DeepBiomics work?
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Innovative graph and network-based representation
It uses advanced graph and network-based representations, to create clear and simple summaries of complex biological processes, proteins, or gene sequences by breaking them into manageable components. Its innovative design makes it an essential tool for biological research and precision healthcare, providing quick and accurate personalized insights.
Tailored Algorithms for a Holistic Understanding of Biological Systems
The DeepBiomics Platform functions as a quick computational engine, quickly evaluating complex biological data to improve tailored healthcare insights. Our advanced algorithms work with a wide range of data technologies, such as gene ontology, route databases, metagenomics, proteomics, epigenetics, 3D structural data of proteins, cellular imaging, and large text corpora.
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Increasing Intricacy while Maintaining Nuance
Our network and graph models capture the complex intricacies of biological systems, and ensure the precise delivery of personalized data. This commitment aligns with our mission to accelerate insights and drive innovative healthcare solutions.
Explore our research areas
Type 2 Diabetes with AI and Microbiome Data
Our analytical methods are designed to intelligently link microbiome components to clinical pathways, identifying crucial elements for predicting T2D risk.
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Type 2 Diabetes Care with AI and Microbiome Data
Type 2 diabetes (T2D) is driven by complex biological mechanisms, and recent research highlights the significant role of the gut microbiome in its development. At HORAIZON, we are accelerating personalized healthcare by using advanced machine learning algorithms to analyze microbiome 16S and shotgun sequencing data from diverse cohorts of both healthy individuals and those with T2D.
Our analytical methods are designed to intelligently link microbiome components to clinical pathways, identifying crucial elements for predicting T2D risk. This approach not only serves as a predictive tool but also advances clinical research, paving the way for new treatments and interventions.
Acknowledging the variability in T2D prevalence across different ethnic groups, we’ve developed ethnicity-specific predictive models. These specialized algorithms refine predictions by incorporating microbial biomarkers, thereby enabling more accurate risk assessments tailored to each population.
Cardiovascular Risk Prediction Through Advanced Proteomics
Our models excel at identifying a constellation of key biomarkers, significantly enhancing the prediction of cardiovascular events.
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Cardiovascular Risk Prediction Through Advanced Proteomics
In the rapidly evolving field of personalized medicine, accurately identifying individuals at high cardiovascular risk is more critical than ever. Traditional methods that rely on single biomarkers often fall short in providing effective risk estimates. These models excel at identifying a constellation of key biomarkers, significantly enhancing the prediction of cardiovascular events.
Our proteome-based risk model is trained on extensive datasets that cover a wide range of cases and conditions. It undergoes rigorous comparison with traditional risk factors in primary prevention settings and is validated on independent cohorts to ensure its reliability and applicability.
The predictive models we have created at HORAIZON not only introduce novel approaches but also significantly outperform conventional clinical risk factors. These advanced models are crucial tools in personalized medicine, improving both the accuracy and timeliness of cardiovascular risk predictions.
Epigenetics and inflammatory bowel disease
Traditional treatment approaches often rely on a trial-and-error method due to the lack of reliable biomarkers to predict treatment outcomes.
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Epigenetics and inflammatory bowel disease
The introduction of biologics, such as anti-TNF agents, has significantly advanced the management of Crohn's disease. Despite this progress, about 70% of patients either do not respond to these medications or lose their responsiveness over time. Traditional treatment approaches often rely on a trial-and-error method due to the lack of reliable biomarkers to predict treatment outcomes. As a result, many patients endure months or even years of ineffective treatment, risking severe complications such as intestinal obstructions, perforations, multiple surgical resections, and even cancer.
To address the challenge of ineffective Crohn's disease treatments, the HORAIZON team has developed innovative machine learning models that analyze epigenetic patterns in peripheral blood samples. These models identify specific epigenetic signatures at baseline that predict responses to biologic agents, including clinical remission and mucosal repair.
This breakthrough allows healthcare providers to tailor treatment plans based on these markers, moving away from the traditional trial-and-error approach. By implementing data-driven, personalized strategies, we aim to enhance treatment efficacy and minimize risks.Our focus on epigenetics and machine learning represents a significant advance in precision medicine, optimizing treatment and reducing delays in effective intervention.