ChatGPT and BioGPT as tools for life science research

Written by Benjamin Raven, PhD student researching cellular senescence at the University of Sheffield, UK.

In recent years, the use of artificial intelligence (AI) technology in the field of life sciences has been rapidly increasing. One such AI tool that has gained popularity in the research community is ChatGPT. In this blog post, we will explore what ChatGPT is, how it works, and how it can be used by life scientists. Keep reading for a surprise at the end!


What is ChatGPT?

ChatGPT is a large language, machine learning model developed by OpenAI, a research company dedicated to creating safe and beneficial AI. ChatGPT is trained on a large body of text data, including books, articles, and websites. It is designed to generate human-like responses to natural language queries, making it a useful tool for a wide range of applications.


What is ChatGPT used for?

ChatGPT is primarily used for natural language processing tasks, such as language translation, text summarization, and question answering. However, its potential uses in the field of life sciences are also becoming more apparent.

One of the most significant challenges in life science research is analyzing vast amounts of scientific literature to identify patterns, relationships, and potential new discoveries. The sheer volume of published research is overwhelming, and traditional methods of analysis can be time-consuming and error-prone.

ChatGPT offers a new approach to analyzing scientific literature, generating new hypotheses, and discovering insights that may have been missed using traditional methods.


Can you use ChatGPT for research?

Yes, ChatGPT can be used for research in a variety of ways. For example, it can be used to analyze scientific literature and extract relevant information. This can help researchers stay up-to-date with the latest research and identify new research opportunities. ChatGPT can also be used to generate hypotheses based on existing data, helping researchers to identify new avenues for exploration.

One of the most promising applications of ChatGPT in life sciences research lies in drug discovery. The process of drug discovery can be time-consuming and expensive, requiring extensive experimentation and analysis. ChatGPT can be used to predict the activity of potential drug candidates based on their chemical structure, reducing the need for extensive experimentation.

However, ChatGPT’s usefulness extends beyond research and discovery, and it is particularly powerful as a tool for communication. ChatGPT’s ability to generate abstracts, introductions, and even entire blog posts, is extraordinary. While the work it produces requires polishing and fact-checking, the speed with which it can create the basis of a body of work has the capacity to dramatically streamline scientific writing. This will help writers produce high-quality pieces of work in a fraction of the time, something desperately needed given the vast quantity of high-quality research produced daily.


How does ChatGPT work?

ChatGPT works by using a technique called “unsupervised learning.” This means that it is trained on a large amount of text data without any explicit instructions on what to learn or how to learn it. Instead, it is given the task of predicting the next word in a sequence of text. By doing this repeatedly, it learns to recognize patterns in the language and develop an understanding of the underlying structure of the text.

Once it has been trained, ChatGPT can be used to generate text by providing it with a prompt. For example, a researcher might input a question or a statement, and ChatGPT will generate a response based on its understanding of the language. This response can be used to generate new insights, test hypotheses, or identify trends in the data.

ChatGPT uses a neural network to generate responses to natural language queries. The neural network is trained on a large body of text data, allowing it to learn patterns and relationships between words and phrases. When a user inputs a natural language query, ChatGPT uses its neural network to generate a response that is similar to human speech.

Neural networks are machine learning models inspired by the structure and function of the human brain. They consist of layers of interconnected nodes that process and transform input data to produce output predictions. During training, the network adjusts the strength of connections between nodes to minimize the difference between predicted and actual output, increasing the accuracy of the information produced. This process of optimization is achieved using a mathematical technique called backpropagation. Neural networks are capable of learning complex patterns and relationships in data. They have been successfully applied in a wide range of applications, such as image recognition, natural language processing, and game playing.

ChatGPT’s neural network is designed to generate contextually appropriate and grammatically correct responses. It uses a combination of statistical analysis and pattern recognition to generate relevant responses to the user’s query. However, because ChatGPT is a machine learning model, its responses are not always accurate or reliable. Users should exercise caution when using ChatGPT for research purposes and should always verify its responses with other sources.


Limitations of ChatGPT and BioGPT

BioGPT is a large pre-trained language model developed by OpenAI specifically for the domain of biology. It is based on the same architecture as OpenAI’s popular GPT series of models but has been fine-tuned on a large corpus of scientific literature from the field of biology. This fine-tuning enables BioGPT to generate more accurate and relevant text in response to biology-related queries and prompts. BioGPT has shown promise in a number of natural language processing tasks, including question answering, document classification, and named entity recognition. It has the potential to greatly facilitate scientific research and discovery in the field of biology.

However, as these language models are trained on existing scientific literature, they are vulnerable to the biases present in this source material. Ultimately, a neural network can only be as accurate as the information it is fed. Given the vast quantity of fraudulent/flawed work present in scientific literature, it is inevitable that biases will continue to creep in.

ChatGPT has been known to provide incorrect references when references are requested. This further highlights that, while the technology is immensely impressive, care must be taken to research the answers given and validate them to ensure that misinformation is not further disseminated. It is essential that any biases in the answers given by any AI language model are investigated and balanced, and not simply mirrored in published research. ChatGPT and BioGPT are crucially solely text-based models and cannot learn from images and videos (such as pathology slides and CT scans).

There are already some language models that can use images as an information source, such as the Vision-Language Pretraining (VLP) models. These models are trained on both image and text data, enabling them to generate captions for images and answer questions based on visual input.

In the future, we can expect more advancements in the development of language models that incorporate multiple modalities, including images, to enhance their ability to understand language in different contexts and provide more accurate responses.



In conclusion, ChatGPT is a powerful AI tool that can be used for a wide range of natural language processing tasks, including research in the field of life sciences. It could revolutionize how researchers analyze scientific literature, generate hypotheses and methods, and discover new drugs. However, it is essential to exercise caution when using ChatGPT for research purposes and to verify its responses with other sources. As AI technology continues to evolve, we will likely see even more advanced tools and applications that can help life scientists unlock the secrets of the human body and ultimately improve human health.


Surprise reveal: Who really wrote this blog?

I also hope that ChatGPT’s ability to produce high-quality, human-like responses to prompts has been demonstrated effectively by this blog post, which was largely written by the software. Prompts were given (such as “Summarize how neural networks work in 100 words”), and the responses were gathered, edited, and fact-checked. This massively accelerated my ability to write accurately on an area I was not familiar with, helping me to communicate my ideas effectively. The software helped guide my writing on the subject and shape my research questions.

Ultimately, a human touch is still required to produce easily digestible writing, but the speed at which this technology is advancing is truly staggering. It will not be long before AI is firmly embedded into all media (scientific and non-scientific) that we interact with daily.

It is difficult to argue that AI has not already become a day-to-day feature of writing. The spelling and grammar-checking abilities of AI-powered software such as Grammarly or Microsoft’s Editor can gauge the tone of a sentence, helping to ensure consistency of tense and context across a piece of writing. These pieces of technology are already used by billions of people worldwide and are only advancing in accuracy and complexity. Ultimately, this technology has already become a staple of literature and research output, and it is only the advancement and wider adoption of the technology that is new. From advertising and marketing to literature reviews, it will be fascinating to see what this technology can do next and what new applications can be uncovered!

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