Easily customize your own databases and AI analytics for your specified research.

Using our platform, you can choose which public and private biomedical databases and AI tools you wish to include within your search to visualize the connected patterns a particular gene or sets of genes have among your chosen databases.

A Suite Of Tools

Currently, we have accomplished developing the first layer of our AI Research tool, building our Symbolic Regression Analysis service for identifying unique combinations of Single Nucleotide Polymorphisms (SNPs) and a related Gene Annotation service that performs a search among several key genetic databases that connect your searched genes to Gene Ontologies, Metabolic & Signaling Pathways, and Protein-Protein interactions.

The MOZI.AI Biomedical Research Service

Once you’ve entered into our ecosystem, you have direct access to a suite of AI driven data science solutions for your biomedical research.

Gene Annotation

The Gene Annotation Service integrates a number of biomedical reference databases which it uses and produces classification models to provide context to help the researcher gain useful insights and explanations about the possible significance of her input genes.

MOSES Biomedical Analysis

Meta-optimizing semantic evolutionary search (MOSES) is a new approach to program evolution, based on representation-building and probabilistic modeling. MOSES has been successfully applied to solve hard problems in domains such as computational biology, sentiment evaluation, and agent control. Results tend to be more accurate, and require less objective function evaluations, than other program evolution systems.


A probabilistic logic network (PLN) is a conceptual, mathematical and computational approach to uncertain inference; inspired by logic programming, but using probabilities in place of crisp (true/false) truth values, and fractional uncertainty in place of crisp known/unknown values. In order to carry out effective reasoning in real-world circumstances, artificial intelligence software must robustly handle uncertainty.

Powered by our Team TEAM

The MOZI.AI Team combines a passion for research, data driven AI, and proven methodologies in development, marketing & distribution.

Kent Zaitlik
Mike Duncan
Chief Computational Biologist
Dr. Ben Goertzel
Chief AI Scientist
Abdulrahamn Semrie
Software Engineer
Zheng Lin
Biomedical Engineer
Hedra Yusuf
Software Engineer
Enkusellasie Wendwosen
Software Engineer
Denis Odinokov
Bioinformatics Project Manager
Linas Veptas
Senior Software Advisor
Cassio Penachin
Senior Software Advisor
Tony van de Ven
Senior Business Advisor

Contact Mozi.AI

Please feel free to reach out to us about our product for a free trial or technology partnerships below! Thank you and we look forward to connecting with you!

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