Behind a user-friendly interface, a complex and powerful artificial neural network

DeepProphet: The state-of-the-art machine learning techniques serving Life Science Research

GeneRecommender is based on a proprietary neural network, called DeepProphet, scanning millions of scientific articles and processing about 445 million connections. This engine is able, given a set of genes and/or diseases in which the user is interested, to suggest a new set of genes that are related and should be considered by the users. We integrated data from several public DBs to exploit the most updated information.


Millions of scientific papers have been used to train GeneRecommender's Artificial Intelligence engine. During the learning process, the Artificial Neural Network considered what life science researchers worldwide have produced in the past 30 years to extract patterns and correlations among genes and diseases.

Scientific Papers Scanned
GeneSymbols Considered
Diseases Considered
Genes Citations
Diseases Citations



Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. Within the concept of AI, different methods and techniques can be found. Nowadays, one of the most useful and promising of these techniques and methods is Machine Learning.


Machine Learning is a multifaceted notion that draws on methodologies from statistics, computer science, and data processing. Machine Learning products are computer programs that use data to formulate predictions, get accurate answers, and make decisions without being explicitly programmed, but rather learning from data as humans do daily.


Classical software engineering involves an expert setting precise rules when developing software. In many cases formulating the rules can be prohibitively complex. Machine Learning algorithms work in a different way. All patterns discovered in the past would draw new conclusions to form new data sets. This makes it possible to process a really huge amount of information.

The Algorithm - DeepProphet2

GeneRecommender uses DeepProphet2, an advanced deep learning algorithm trained on all the scientific papers available online. Indeed, using cutting-edge Natural Language Processing (NLP) techniques, the system extracts key information from manuscripts and uses it to train a deep learning neural network to predict which genes might be associated with a given study. In order to accomplish this task, TheProphetAI developed a customer transformer-based model based on a design similar to that of the most famous and successful language models, such as LaMDA and GPT-3. We have tested the System using gene sets representing pathways and diseases. Recursively, a randomly chosen gene was left out and the remaining input was provided, we tested whether the algorithm could predict which gene was missing based on its first recommendation. Since the algorithm does not know the gene sets during training, if it can complete them, it has learned an accurate representation of genes. Using this kind of analysis, we reached an astonishing result: an AUC of 0.9.

If you would like to go into technical details about the algorithm and its validation, please consider downloading our scientific paper: []

Human and AI cooperation

You can greatly benefit from the introduction of Artificial Intelligence systems into your workflow. In order to maximize these technologies’ potential, AI must be adopted safely and appropriately. While AI cannot replace humans in their jobs, it can help and support them in their daily tasks, improving their efficiency and effectiveness. The predictions made by AI systems aren’t always right, but they offer a useful starting point for solving specific issues.

Our belief is that the real potential lies in the cooperation between humans and machines. Deep analysis by experts is required to understand the real value of the recommendations obtained from the AI engine.

While you can find tools on our platform to understand the results of each recommendation, please keep in mind that we humans use AI because we cannot work with such data!

Do the results always turn out correctly? Although it may not be possible every time, if you insert a sufficient number of targets, you are likely to see some valuable results statistically. Most of the recommended genes will provide you with some useful insights for your research.

The process that Artificial Intelligence follows is the same as the process Humans follow in a fraction of the time. Researchers like you often use scientific literature to learn about what has been done before on similar topics and to better understand their own research. Indeed, we are using exactly the same data, elaborated by a powerful neural network that knows what the scientific community has already studied and tested and can use this knowledge to generate recommendations.

AI will not substitute your work, but it will help you to speed up your studies with its innovative and crucial insights.

Data sources included in the Platform

Pathways: Reactome

Griss J, Viteri G, Sidiropoulos K, Nguyen V, Fabregat A, Hermjakob H. ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis. Mol Cell Proteomics. 2020 Sep 9. doi: 10.1074/mcp. PubMed PMID: 32907876.

Our Development Values

Two-cell embryo, Mitosis under microscope (3D illustration)



Security and safety

User Oriented

We are committed to real innovation

TheProphetAI develops its algorithms and models to bring innovation to the life science sector. Machine learning, artificial intelligence, and deep learning are not just buzzwords for us. They are the foundation of our platform.

TheProphetAI performs rigorous tests on every algorithm

Platform algorithms have all been tested against public benchmarks. Indeed, we perform rigorous tests on every model you may find on GeneRecommer. If you would like to know more about the performances of our AI model please refer to our paper. 

The platform has been built following the highest security and safety standards

We believe in the importance of privacy. Data are “the new gold” and for this reason, should be managed in the most careful way. The platform itself has been built in order to protect your data.

The complexity of a neural network with an easy to use interface

Artificial Intelligence systems, Machine Learning and Artificial neural networks are extremely complex mathematical and statistical models. Unfortunately, these techniques are as complex as hard to implement. GeneRecommender will allow you to get the benefits from the AI without having to deal with its complexity, thanks to an easy-to-use interface, with no need for coding skills.

  • Innovation
  • Robustness
  • Security and safety
  • User Oriented

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