AI for Biomedicine in the Era of Large Language Models

• URL: http://arxiv.org/abs/2403.15673v1
• Date: Sat, 23 Mar 2024 01:40:22 GMT
• Title: AI for Biomedicine in the Era of Large Language Models
• Authors: Zhenyu Bi, Sajib Acharjee Dip, Daniel Hajialigol, Sindhura Kommu, Hanwen Liu, Meng Lu, Xuan Wang,
• Abstract summary: Recent advancements in large language models have showcased prowess in natural language tasks. Can we harness the potential of these models to drive biomedical knowledge discoveries? We will explore the application of large language models to three crucial categories of biomedical data.
• Score: 5.860347634642287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The capabilities of AI for biomedicine span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, predicting chemical or protein structures, and further extending to societal predictions like infectious disease outbreaks. Recent advancements in large language models, exemplified by models like ChatGPT, have showcased significant prowess in natural language tasks, such as translating languages, constructing chatbots, and answering questions. When we consider biomedical data, we observe a resemblance to natural language in terms of sequences: biomedical literature and health records presented as text, biological sequences or sequencing data arranged in sequences, or sensor data like brain signals as time series. The question arises: Can we harness the potential of recent large language models to drive biomedical knowledge discoveries? In this survey, we will explore the application of large language models to three crucial categories of biomedical data: 1) textual data, 2) biological sequences, and 3) brain signals. Furthermore, we will delve into large language model challenges in biomedical research, including ensuring trustworthiness, achieving personalization, and adapting to multi-modal data representation

Related papers

• Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey [75.47055414002571]
  The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology. We provide an analysis of recent advancements achieved through cross modeling of biomolecules and natural language.
  arXiv  Detail & Related papers  (2024-03-03T14:59:47Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• Large language models in bioinformatics: applications and perspectives [14.16418711188321]
  Large language models (LLMs) are artificial intelligence models based on deep learning. This review focuses on exploring the applications of large language models in genomics, transcriptomics, drug discovery and single cell analysis.
  arXiv  Detail & Related papers  (2024-01-08T17:26:59Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights [15.952942443163474]
  We propose a new state-of-the-art approach for obtaining high-fidelity representations of biomedical concepts and sentences. We demonstrate consistent and substantial performance improvements over the previous state of the art. Besides our new state-of-the-art biomedical model for English, we also distill and release a multilingual model compatible with 50+ languages.
  arXiv  Detail & Related papers  (2023-11-27T18:46:17Z)
• Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking [4.8882241537236455]
  This research investigates a method that exploits the in-context learning capabilities of large models for biomedical concept linking. The proposed approach adopts a two-stage retrieve-and-rank framework. It achieved an accuracy of 90.% in BC5CDR disease entity normalization and 94.7% in chemical entity normalization.
  arXiv  Detail & Related papers  (2023-07-03T16:19:50Z)
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day [85.19963303642427]
  We propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images. The model first learns to align biomedical vocabulary using the figure-caption pairs as is, then learns to master open-ended conversational semantics. This enables us to train a Large Language and Vision Assistant for BioMedicine in less than 15 hours (with eight A100s)
  arXiv  Detail & Related papers  (2023-06-01T16:50:07Z)
• BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining [140.61707108174247]
  We propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. We get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks respectively, and 78.2% accuracy on PubMedQA.
  arXiv  Detail & Related papers  (2022-10-19T07:17:39Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.