Introducing Semares: The Cutting-Edge Software for Omics Data Management, Analysis and Visualization

In the fast-paced world of omics research, having the right tools to manage, analyze and visualize data can make a big difference in your results. And that's why we are proud to introduce Semares™ , a software product designed to meet the needs of modern omics research.

With a focus on FAIR (Findable, Accessible, Interoperable, Reusable) semantic biomedical data integration, Semares™ provides a seamless integration of data from multiple sources. This allows you to access, analyze and visualize data in a single, user-friendly platform. One of the key features of Semares™ is the Nextflow workflow analysis plugins. These plugins allow you to automate complex workflows and pipelines, making your data analysis and visualization process more efficient and less time-consuming.

The Jupyter notebook interface provides a convenient way to create and share interactive data analysis and visualization reports. Whether you're working with a team or collaborating with colleagues, the Jupyter interface makes it easy to communicate and share your findings.

The interactive visualizations in Semares™ are also extendable, allowing you to create custom data displays and reports that fit your needs. This can help you uncover insights and patterns in your data that might have gone unnoticed with traditional visualization methods.

Another standout feature of Semares™ is its comprehensive search functionality. With a powerful search engine, you can quickly and easily find the data you need, saving you time and effort. And with features like advanced search filters, you can further refine your results to find exactly what you're looking for.

In conclusion, Semares™ is a game-changer for omics data management, analysis, and visualization. With its cutting-edge features and user-friendly interface, it has never been easier to access, analyze and visualize your data. Whether you're a researcher, a data analyst or a scientist, Semares is the perfect tool to help you get the most out of your omics data.

Genevention develops data integration platform and machine learning tools for prediction of undernutrition and treatment outcome assessment in elderly people: AMBROSIA

In older people with heart failure and atrial fibrillation, undernutrition is one of the key factors leading to inflammation, loss of function, disability and, ultimately, death. Since the inflammation is closely related to the intestinal microbiome, shaping the gut microbiota composition with a probiotic-based food could be an efficacious and safe approach to improve the cognitive functioning and skeletal muscle mass. AMBROSIA (Microbiota-Inflammation-Brain axis in heart failure: new food, biomarkerS and AI Approach for the prevention of undeRnutrition in Older) aims to develop an innovative food product to prevent undernutrition in HF and AF patients: a new chocolate bar containing a specific mix of probiotic strains and a cocktail of micro/macronutrients. The efficacy of the AMBROSIA bar on undernutrition prevention and its impact on cognitive functioning and skeletal muscle mass of older HF and AF patients will be evaluated through a prospective monocentric interventional clinical study. Several experimental high-throughput datasets such as biomarker, lipidomics and metagenomics profiles from urine, blood and saliva samples will be generated and analyzed during the AMBROSIA study. Using these data, statistical and machine learning methods will be developed for the identification of features from the “Microbiota-Inflammation-Brain axis” that are predictive for undernutrition (biomarkers) and/or related to AMBROSIA bar treatment outcome.

The AMBROSIA network consists of an international consortium with academic and industrial partners from five European countries (Italy, Spain, UK, Ireland, Germany). As an AMBROSIA partner, Genevention will develop a semantic data integration platform and knowledgebase for FAIR management and rich, harmonized annotation of clinical and experimental high-throughput data. We will develop, evaluate and integrate machine learning methods for identification of features from the “Microbiota-Inflammation-Brain axis” that are predictive for undernutrition and could be used as biomarkers. Statistical and machine learning tools will be developed and integrated in the platform for analysis of patient parameters related to treatment with the AMBROSIA bar. This work is financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), grant number 2820ERA20E, (Cofund ERA-NET “ERA-HDHL”).

  • «
  • 1 (current)
  • 2
  • »