ChatGPT opens a new door for bioinformatics

Abstract

ChatGPT is an artificial intelligence (AI) system that can perform sophisticated writing and dialogs after learning from vast amounts of linguistic data.The success of ChatGPT is phenomenal.AI-based humanmachine language interaction has been at the center of AI competition in recent years.The major players in this game have been Google, Meta, and OpenAI.Google was in the best position from the outset, given its invention of Transformer (the cornerstone of all cuttingedge language models) and its significant edge in reinforcement learning.Yet, Google's efforts in this area were rather diffusing.It kept generating language model variants with incremental innovations but failed to reach the next level.Meta has a strong AI team, including many top AI researchers in the world.Nevertheless, their faith in self-supervised learning to solve humanmachine interaction did not deliver high-impact success.Conversely, OpenAI, with a small team, stayed focused on a single product line (GPT, including its latest release of GPT-4).It moved in the right direction of using human input to "align" the language model based on the Reinforcement Learning from Human Feedback (RLHF) approach.The fact that OpenAI ultimately prevailed in this game shows that the model alignment to human labeling through supervised and reinforcement learning is critical for human-machine interaction.However, a chatbot's actions rely heavily on cues (prompts) provided by human operators.To properly utilize ChatGPT's capabilities, prompts to instruct or mentor the chatbot must be carefully designed to get valuable, valid, and robust responses.This process becomes another "alignment" problem of using prompt engineering to best probe ChatGPT's knowledge graph for best serving users' needs.ChatGPT has sparked tremendous interests in many fields.It has potential uses in facilitating and tutoring programming-heavy data analysis, like bioinformatics.However, the investigation of ChatGPT in biology and medicine is less extensive than in other domains.As of March 4, 2023, ChatGPT had 5380 appearances in Google Scholar's general publication database, but just 21 and 8 in the bio-specific preprint repositories medRxiv and bioRxiv, respectively.Within the 5380 articles in Google Scholar, 75 articles mentioned ChatGPT and bioinformatics simultaneously.In contrast, 3540, 2560, and 2550 concurrently incorporated ChatGPT, and education, economics, or law, respectively.Such statistics reflect the general trends in different fields consistently, but among the 75 articles in bioinformatics, only one editorial focused on ChatGPT's usage in bioinformatics [1], while others just passingly mentioned it.Meanwhile, ChatGPT has demonstrated excellent promise in performing intricate biomedical tasks.For instance, ChatGPT passed the United States Medical Licensure Test with a 60% accuracy score without the assistance of human trainers [2].ChatGPT has several benefits for bioinformatics: (i) Given the multidisciplinary nature of bioinformatics, ChatGPT can assist bioinformatics researchers in staying current on a variety of relevant research subjects.(ii) It can facilitate complex tasks involving substantial biodata, particularly for time-sensitive biomedical applications.(iii) ChatGPT may be tailored to suit diverse bioinformatics jobs with its strong domain-adaptation capabilities.(iv) ChatGPT variants can generate effective language

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