Recently, an artificial intelligence-driven analytics platform, DrBioRight, has been developed for biomedical research, which is published at Cancer Cell, one of the top scientific journals.

The main function of this tool is to, by utilizing natural language processing (NLP) technology, enable biomedical researchers without expertise in bioinformatics or programming to perform computational analysis of large omics datasets.

Specifically, DrBioRight allows the user to “talk” to it in natural language instead of programming scripts. Then, it will **“translate”** the natural language to a specific bioinformatic task by applying the pre-trained neural network. After confirming the predicted requested analysis with the user, DrBioRight will perform the analysis on the cloud and report the final results.

The idea is so cool because the entire process is automated, which only needs the user to provide several keywords in a simple question, such as “What is the relationship between gene X and the survival rate in breast cancer?”

Conventionally, the analytics of omics datasets are so complex that experts in bioinformatics and biostatistics are required to involve in the project. However, DrBioRight seems to break the rule by arming the pure biologists with tons of automated analysis pipelines.

Tools like DrBioRight are trying to define a new generation of analytics in biomedical science, which will definitely make the research progress more intuitive and efficient.

So, are the computational biologists losing their jobs?

No, not in the near future.

I list some major challenges that keep such platforms from automating all the necessary computational work in a project.

#bioinformatics #naturallanguageprocessing #biostatistics #ai #biology