Genevention's smart knowledge bases combine the
wealth of biomedical big data and the power of Artificial Intelligence systems.
Benefit from our interactive data exploration interfaces and
get from question to insights within a few clicks!
Genevention's intelligent database solutions combine biological big data and advanced analyses to identify the most specific molecule signatures for personalized medicine and biotechnology. Our easy-to-use interfaces allow you to interactively retrieve and intuitively explore biomarkers for research, diagnostics and therapy. Benefit from our cutting-edge data integration efforts to reveal molecular mechanisms of diseases and agricultural loss.
Generating experimental data is easier and cheaper than ever, but keeping an overview and getting the most out of this data is increasingly challenging. Genevention provides customized database solutions which help you to organize, annotate and analyze your high-throughput experimental data. We connect your data with existing ontologies and state-of-the-art analysis pipelines – all integrated in an easy-to-use interactive interface.
Addressing research questions in the life sciences today often requires gathering and analyzing vast amounts of data. However, identifying suitable datasets and applying appropriate analysis solutions requires a solid understanding of data characteristics and statistics. Genevention provides data management and computational analysis solutions for a broad range of life science research projects. Our experienced data scientists will help you to design studies, identify and organize data and apply cutting-edge machine learning techniques to gain deep insights.
*John Naisbitt, "Megatrends", 1982
Leverage the power of thousands of expression data sets across many species and experimental conditions.
Benefit from our semi-automatic meta-data extraction pipeline for high-quality annotation/curation of data sets
Our state-of-the-art processing pipelines combine the highest sensitivity and specificity for quantification of known and novel transcripts and the detection of pathogenic organisms.
Cutting-edge machine learning modules identify specific biomarkers, disease-relevant pathogens and causative mutation events.
Annotation-guided search bars allow to identify the most specific biomarkers of your interest within seconds. Browse disease-relevant associations of molecules and pathogens across tissues.
Gain insights by interactive visualizations and produce publication-ready figures.
Genevention GmbH is a Bioinformatics Startup that develops algorithms and databases to identify biomarkers from high-throughput experimental data. We apply cutting-edge machine learning techniques to the massive amount of available -omics data to gain insights into biological mechanisms driving diseases and agricultural loss. We aim to provide the tools that help medical researchers, pharmaceutical engineers and agricultural companies to understand and combat these mechanisms.
Inge Sillaber is a neurobiologist with many years of management experience in biotech industry. She has a comprehensive background in behavioural pharmacology and preclinical as well as clinical drug development.
Thomas Lingner is a data scientist with a strong background in computational biology. His expertise covers data mining & machine learning, high-throughput sequence analysis and metagenomics.
Stefan Bonn is a biochemist and computational biologist with a strong background in deep learning and data analysis.
Herbert Stadler is a neuroscientist and successful biotech entrepreneur.