Guided diffusion for molecular generation with interaction prompt.

Molecular generative models have exhibited promising capabilities in designing molecules from scratch with high binding affinities in a predetermined protein pocket, offering potential synergies with traditional structural-based drug design strategy. However, the generative processes of such models are random and the atomic interaction information between ligand and protein are ignored. On the other hand, the ligand has high propensity to bind with residues called hotspots. Hotspot residues contribute to the majority of the binding free energies and have been recognized as appealing targets for designed molecules. In this work, we develop an interaction prompt guided diffusion model, InterDiff to deal with the challenges. Four kinds of atomic interactions are involved in our model and represented as learnable vector embeddings. These embeddings serve as conditions for individual residue to guide the molecular generative process. Comprehensive in silico experiments evince that our model could generate molecules with desired ligand-protein interactions in a guidable way. Furthermore, we validate InterDiff on two realistic protein-based therapeutic agents. Results show that InterDiff could generate molecules with better or similar binding mode compared to known targeted drugs.

Adapting differential molecular representation with hierarchical prompts for multi-label property prediction.

Accurate prediction of molecular properties is crucial in drug discovery. Traditional methods often overlook that real-world molecules typically exhibit multiple property labels with complex correlations. To this end, we propose a novel framework, HiPM, which stands for Hierarchical Prompted Molecular representation learning framework. HiPM leverages task-aware prompts to enhance the differential expression of tasks in molecular representations and mitigate negative transfer caused by conflicts in individual task information. Our framework comprises two core components: the Molecular Representation Encoder (MRE) and the Task-Aware Prompter (TAP). MRE employs a hierarchical message-passing network architecture to capture molecular features at both the atom and motif levels. Meanwhile, TAP utilizes agglomerative hierarchical clustering algorithm to construct a prompt tree that reflects task affinity and distinctiveness, enabling the model to consider multi-granular correlation information among tasks, thereby effectively handling the complexity of multi-label property prediction. Extensive experiments demonstrate that HiPM achieves state-of-the-art performance across various multi-label datasets, offering a novel perspective on multi-label molecular representation learning.

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering.

Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational methods have been developed to predict drug metabolites, but most of them suffer from two main obstacles: the lack of model generalization due to restrictions on metabolic transformation rules or specific enzyme families, and high rate of false-positive predictions. Here, we presented MetaPredictor, a rule-free, end-to-end and prompt-based method to predict possible human metabolites of small molecules including drugs as a sequence translation problem. We innovatively introduced prompt engineering into deep language models to enrich domain knowledge and guide decision-making. The results showed that using prompts that specify the sites of metabolism (SoMs) can steer the model to propose more accurate metabolite predictions, achieving a 30.4% increase in recall and a 16.8% reduction in false positives over the baseline model. The transfer learning strategy was also utilized to tackle the limited availability of metabolic data. For the adaptation to automatic or non-expert prediction, MetaPredictor was designed as a two-stage schema consisting of automatic identification of SoMs followed by metabolite prediction. Compared to four available drug metabolite prediction tools, our method showed comparable performance on the major enzyme families and better generalization that could additionally identify metabolites catalyzed by less common enzymes. The results indicated that MetaPredictor could provide a more comprehensive and accurate prediction of drug metabolism through the effective combination of transfer learning and prompt-based learning strategies.

Integrating graph convolutional networks to enhance prompt learning for biomedical relation extraction.

BACKGROUND AND OBJECTIVE: Biomedical relation extraction aims to reveal the relation between entities in medical texts. Currently, the relation extraction models that have attracted much attention are mainly to fine-tune the pre-trained language models (PLMs) or add template prompt learning, which also limits the ability of the model to deal with grammatical dependencies. Graph convolutional networks (GCNs) can play an important role in processing syntactic dependencies in biomedical texts. METHODS: In this work, we propose a biomedical relation extraction model that fuses GCNs enhanced prompt learning to handle limitations in syntactic dependencies and achieve good performance. Specifically, we propose a model that combines prompt learning with GCNs for relation extraction, by integrating the syntactic dependency information analyzed by GCNs into the prompt learning model, by predicting the correspondence with [MASK] tokens labels for relation extraction. RESULTS: Our model achieved F1 scores of 85.57%, 80.15%, 95.10%, and 84.11% in the biomedical relation extraction datasets GAD, ChemProt, PGR, and DDI, respectively, all of which outperform some existing baseline models. CONCLUSIONS: In this paper, we propose enhancing prompt learning through GCNs, integrating syntactic information into biomedical relation extraction tasks. Experimental results show that our proposed method achieves excellent performance in the biomedical relation extraction task.

Prompt Engineering Paradigms for Medical Applications: Scoping Review.

BACKGROUND: Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored. OBJECTIVE: The aim of the study is to review research efforts and technical approaches in prompt engineering for medical applications as well as provide an overview of opportunities and challenges for clinical practice. METHODS: Databases indexing the fields of medicine, computer science, and medical informatics were queried in order to identify relevant published papers. Since prompt engineering is an emerging field, preprint databases were also considered. Multiple data were extracted, such as the prompt paradigm, the involved LLMs, the languages of the study, the domain of the topic, the baselines, and several learning, design, and architecture strategies specific to prompt engineering. We include studies that apply prompt engineering-based methods to the medical domain, published between 2022 and 2024, and covering multiple prompt paradigms such as prompt learning (PL), prompt tuning (PT), and prompt design (PD). RESULTS: We included 114 recent prompt engineering studies. Among the 3 prompt paradigms, we have observed that PD is the most prevalent (78 papers). In 12 papers, PD, PL, and PT terms were used interchangeably. While ChatGPT is the most commonly used LLM, we have identified 7 studies using this LLM on a sensitive clinical data set. Chain-of-thought, present in 17 studies, emerges as the most frequent PD technique. While PL and PT papers typically provide a baseline for evaluating prompt-based approaches, 61% (48/78) of the PD studies do not report any nonprompt-related baseline. Finally, we individually examine each of the key prompt engineering-specific information reported across papers and find that many studies neglect to explicitly mention them, posing a challenge for advancing prompt engineering research. CONCLUSIONS: In addition to reporting on trends and the scientific landscape of prompt engineering, we provide reporting guidelines for future studies to help advance research in the medical field. We also disclose tables and figures summarizing medical prompt engineering papers available and hope that future contributions will leverage these existing works to better advance the field.

Enhancing Zero-Shot Stance Detection with Contrastive and Prompt Learning.

In social networks, the occurrence of unexpected events rapidly catalyzes the widespread dissemination and further evolution of network public opinion. The advent of zero-shot stance detection aligns more closely with the characteristics of stance detection in today's digital age, where the absence of training examples for specific models poses significant challenges. This task necessitates models with robust generalization abilities to discern target-related, transferable stance features within training data. Recent advances in prompt-based learning have showcased notable efficacy in few-shot text classification. Such methods typically employ a uniform prompt pattern across all instances, yet they overlook the intricate relationship between prompts and instances, thereby failing to sufficiently direct the model towards learning task-relevant knowledge and information. This paper argues for the critical need to dynamically enhance the relevance between specific instances and prompts. Thus, we introduce a stance detection model underpinned by a gated multilayer perceptron (gMLP) and a prompt learning strategy, which is tailored for zero-shot stance detection scenarios. Specifically, the gMLP is utilized to capture semantic features of instances, coupled with a control gate mechanism to modulate the influence of the gate on prompt tokens based on the semantic context of each instance, thereby dynamically reinforcing the instance-prompt connection. Moreover, we integrate contrastive learning to empower the model with more discriminative feature representations. Experimental evaluations on the VAST and SEM16 benchmark datasets substantiate our method's effectiveness, yielding a 1.3% improvement over the JointCL model on the VAST dataset.

MedKPL: A heterogeneous knowledge enhanced prompt learning framework for transferable diagnosis.

Artificial Intelligence (AI) based diagnosis systems have emerged as powerful tools to reform traditional medical care. Each clinician now wants to have his own intelligent diagnostic partner to expand the range of services he can provide. However, the implementation of intelligent decision support systems based on clinical note has been hindered by the lack of extensibility of end-to-end AI diagnosis algorithms. When reading a clinical note, expert clinicians make inferences with relevant medical knowledge, which serve as prompts for making accurate diagnoses. Therefore, external medical knowledge is commonly employed as an augmentation for medical text classification tasks. Existing methods, however, cannot integrate knowledge from various knowledge sources as prompts nor can fully utilize explicit and implicit knowledge. To address these issues, we propose a Medical Knowledge-enhanced Prompt Learning (MedKPL) diagnostic framework for transferable clinical note classification. Firstly, to overcome the heterogeneity of knowledge sources, such as knowledge graphs or medical QA databases, MedKPL uniform the knowledge relevant to the disease into text sequences of fixed format. Then, MedKPL integrates medical knowledge into the prompt designed for context representation. Therefore, MedKPL can integrate knowledge into the models to enhance diagnostic performance and effectively transfer to new diseases by using relevant disease knowledge. The results of our experiments on two medical datasets demonstrate that our method yields superior medical text classification results and performs better in cross-departmental transfer tasks under few-shot or even zero-shot settings. These findings demonstrate that our MedKPL framework has the potential to improve the interpretability and transferability of current diagnostic systems.

Biomedical document relation extraction with prompt learning and KNN.

Document-level relation extraction is designed to recognize connections between entities a cross sentences or between sentences. The current mainstream document relation extraction model is mainly based on the graph method or combined with the pre-trained language model, which leads to the relatively complex process of the whole workflow. In this work, we propose biomedical relation extraction based on prompt learning to avoid complex relation extraction processes and obtain decent performance. Particularity, we present a model that combines prompt learning with T5 for document relation extraction, by integrating a mask template mechanism into the model. In addition, this work also proposes a few-shot relation extraction method based on the K-nearest neighbor (KNN) algorithm with prompt learning. We select similar semantic labels through KNN, and subsequently conduct the relation extraction. The results acquired from two biomedical document benchmarks indicate that our model can improve the learning of document semantic information, achieving improvements in the relation F1 score of 3.1% on CDR.

MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning.

Prompt learning has demonstrated impressive efficacy in the fine-tuning of multimodal large models to a wide range of downstream tasks. Nonetheless, applying existing prompt learning methods for the diagnosis of neurological disorder still suffers from two issues: (i) existing methods typically treat all patches equally, despite the fact that only a small number of patches in neuroimaging are relevant to the disease, and (ii) they ignore the structural information inherent in the brain connection network which is crucial for understanding and diagnosing neurological disorders. To tackle these issues, we introduce a novel prompt learning model by learning graph prompts during the fine-tuning process of multimodal models for diagnosing neurological disorders. Specifically, we first leverage GPT-4 to obtain relevant disease concepts and compute semantic similarity between these concepts and all patches. Secondly, we reduce the weight of irrelevant patches according to the semantic similarity between each patch and disease-related concepts. Moreover, we construct a graph among tokens based on these concepts and employ a graph convolutional network layer to extract the structural information of the graph, which is used to prompt the pre-trained multimodal models for diagnosing neurological disorders. Extensive experiments demonstrate that our method achieves superior performance for neurological disorder diagnosis compared with state-of-the-art methods and validated by clinicians.

Dual modality prompt learning for visual question-grounded answering in robotic surgery.

With recent advancements in robotic surgery, notable strides have been made in visual question answering (VQA). Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the image. This limitation restricts the interpretative capacity of the VQA models and their ability to explore specific image regions. To address this issue, this study proposes a grounded VQA model for robotic surgery, capable of localizing a specific region during answer prediction. Drawing inspiration from prompt learning in language models, a dual-modality prompt model was developed to enhance precise multimodal information interactions. Specifically, two complementary prompters were introduced to effectively integrate visual and textual prompts into the encoding process of the model. A visual complementary prompter merges visual prompt knowledge with visual information features to guide accurate localization. The textual complementary prompter aligns visual information with textual prompt knowledge and textual information, guiding textual information towards a more accurate inference of the answer. Additionally, a multiple iterative fusion strategy was adopted for comprehensive answer reasoning, to ensure high-quality generation of textual and grounded answers. The experimental results validate the effectiveness of the model, demonstrating its superiority over existing methods on the EndoVis-18 and EndoVis-17 datasets.

GloGen: PPG prompts for few-shot transfer learning in blood pressure estimation.

With the rapid advancements in machine learning, its applications in the medical field have garnered increasing interest, particularly in non-invasive health monitoring methods. Blood pressure (BP) estimation using Photoplethysmogram (PPG) signals presents a promising opportunity for real-time, continuous monitoring. However, existing models often struggle with generalization, especially for high-risk groups like hypotension and hypertension, where precise predictions are crucial. In this study, we propose Global Prompt and Prompt Generator (GloGen), a robust few-shot transfer learning framework designed to improve BP estimation using PPG signals. GloGen employs a dual-prompt learning approach, combining Global Prompt (GP) for capturing shared features across signals and an Instance-wise Prompt (IP) for generating personalized prompts for each signal. To enhance model robustness, we also introduce Variance Penalty (VP) that ensures diversity among the generated prompts. Experimental results on benchmark datasets demonstrate that GloGen significantly outperforms conventional methods, both in terms of accuracy and robustness, particularly in underrepresented BP groups, even in scenarios with limited training data. GloGen thus stands out as an efficient solution for real-time, non-invasive BP estimation, with great potential for use in healthcare settings where data is scarce and diverse populations need to be accurately monitored.

Acupuncture and tuina knowledge graph with prompt learning.

Introduction: Acupuncture and tuina, acknowledged as ancient and highly efficacious therapeutic modalities within the domain of Traditional Chinese Medicine (TCM), have provided pragmatic treatment pathways for numerous patients. To address the problems of ambiguity in the concept of Traditional Chinese Medicine (TCM) acupuncture and tuina treatment protocols, the lack of accurate quantitative assessment of treatment protocols, and the diversity of TCM systems, we have established a map-filling technique for modern literature to achieve personalized medical recommendations. Methods: (1) Extensive acupuncture and tuina data were collected, analyzed, and processed to establish a concise TCM domain knowledge base. (2)A template-free Chinese text NER joint training method (TemplateFC) was proposed, which enhances the EntLM model with BiLSTM and CRF layers. Appropriate rules were set for ERE. (3) A comprehensive knowledge graph comprising 10,346 entities and 40,919 relationships was constructed based on modern literature. Results: A robust TCM KG with a wide range of entities and relationships was created. The template-free joint training approach significantly improved NER accuracy, especially in Chinese text, addressing issues related to entity identification and tokenization differences. The KG provided valuable insights into acupuncture and tuina, facilitating efficient information retrieval and personalized treatment recommendations. Discussion: The integration of KGs in TCM research is essential for advancing diagnostics and interventions. Challenges in NER and ERE were effectively tackled using hybrid approaches and innovative techniques. The comprehensive TCM KG our built contributes to bridging the gap in TCM knowledge and serves as a valuable resource for specialists and non-specialists alike.

Identifying and Extracting Rare Diseases and Their Phenotypes with Large Language Models.

Purpose: Phenotyping is critical for informing rare disease diagnosis and treatment, but disease phenotypes are often embedded in unstructured text. While natural language processing (NLP) can automate extraction, a major bottleneck is developing annotated corpora. Recently, prompt learning with large language models (LLMs) has been shown to lead to generalizable results without any (zero-shot) or few annotated samples (few-shot), but none have explored this for rare diseases. Our work is the first to study prompt learning for identifying and extracting rare disease phenotypes in the zero- and few-shot settings. Methods: We compared the performance of prompt learning with ChatGPT and fine-tuning with BioClinicalBERT. We engineered novel prompts for ChatGPT to identify and extract rare diseases and their phenotypes (e.g., diseases, symptoms, and signs), established a benchmark for evaluating its performance, and conducted an in-depth error analysis. Results: Overall, fine-tuning BioClinicalBERT resulted in higher performance (F1 of 0.689) than ChatGPT (F1 of 0.472 and 0.610 in the zero- and few-shot settings, respectively). However, ChatGPT achieved higher accuracy for rare diseases and signs in the one-shot setting (F1 of 0.778 and 0.725). Conversational, sentence-based prompts generally achieved higher accuracy than structured lists. Conclusion: Prompt learning using ChatGPT has the potential to match or outperform fine-tuning BioClinicalBERT at extracting rare diseases and signs with just one annotated sample. Given its accessibility, ChatGPT could be leveraged to extract these entities without relying on a large, annotated corpus. While LLMs can support rare disease phenotyping, researchers should critically evaluate model outputs to ensure phenotyping accuracy.

Multi-modal long document classification based on Hierarchical Prompt and Multi-modal Transformer.

In the realm of long document classification (LDC), previous research has predominantly focused on modeling unimodal texts, overlooking the potential of multi-modal documents incorporating images. To address this gap, we introduce an innovative approach for multi-modal long document classification based on the Hierarchical Prompt and Multi-modal Transformer (HPMT). The proposed HPMT method facilitates multi-modal interactions at both the section and sentence levels, enabling a comprehensive capture of hierarchical structural features and complex multi-modal associations of long documents. Specifically, a Multi-scale Multi-modal Transformer (MsMMT) is tailored to capture the multi-granularity correlations between sentences and images. This is achieved through the incorporation of multi-scale convolutional kernels on sentence features, enhancing the model's ability to discern intricate patterns. Furthermore, to facilitate cross-level information interaction and promote learning of specific features at different levels, we introduce a Hierarchical Prompt (HierPrompt) block. This block incorporates section-level prompts and sentence-level prompts, both derived from a global prompt via distinct projection networks. Extensive experiments are conducted on four challenging multi-modal long document datasets. The results conclusively demonstrate the superiority of our proposed method, showcasing its performance advantages over existing techniques.

PLPMpro: Enhancing promoter sequence prediction with prompt-learning based pre-trained language model.

The promoter region, positioned proximal to the transcription start sites, exerts control over the initiation of gene transcription by modulating the interaction with RNA polymerase. Consequently, the accurate recognition of promoter regions represents a critical focus within the bioinformatics domain. Although some methods leveraging pre-trained language models (PLMs) for promoter prediction have been proposed, the full potential of such PLMs remains largely untapped. In this study, we introduce PLPMpro, a model that capitalizes on prompt-learning and the pre-trained language model to enhance the prediction of promoter sequences. PLPMpro effectively harnesses the prompt learning paradigm to fully exploit the inherent capacities of the PLM, resulting in substantial improvements in prediction performance. Experiment results unequivocally demonstrate the efficacy of prompt learning in bolstering the capabilities of the pre-trained model. Consequently, PLPMpro surpasses both typical pre-trained model-based methods for promoter prediction and typical deep learning methods. Furthermore, we conduct various experiments to meticulously scrutinize the effects of different prompt learning settings and different numbers of soft modules on the model performance. More importantly, the interpretation experiment reveals that the pre-trained model captures biological semantics. Collectively, this research imparts a novel perspective on the optimal utilization of PLMs for addressing biological problems.

Towards More Generalizable and Accurate Sentence Classification in Medical Abstracts with Less Data.

With the unprecedented growth of biomedical publications, it is important to have structured abstracts in bibliographic databases (i.e., PubMed), thus, to facilitate the information retrieval and knowledge synthesis in needs of researchers. Here, we propose a few-shot prompt learning-based approach to classify sentences in medical abstracts of randomized clinical trials (RCT) and observational studies (OS) to subsections of Introduction, Background, Methods, Results, and Conclusion, using an existing corpus of RCT (PubMed 200k/20k RCT) and a newly built corpus of OS (PubMed 20k OS). Five manually designed templates in a combination of 4 BERT model variants were tested and compared to a previous hierarchical sequential labeling network architecture and traditional BERT-based sentence classification method. On the PubMed 200k and 20k RCT datasets, we achieved overall F1 scores of 0.9508 and 0.9401, respectively. Under few-shot settings, we demonstrated that only 20% of training data is sufficient to achieve a comparable F1 score by the HSLN model (0.9266 by us and 0.9263 by HSLN). When trained on the RCT dataset, our method achieved a 0.9065 F1 score on the OS dataset. When trained on the OS dataset, our method achieved a 0.9203 F1 score on the RCT dataset. We show that the prompt learning-based method outperformed the existing method, even when fewer training samples were used. Moreover, the proposed method shows better generalizability across two types of medical publications when compared with the existing approach. We make the datasets and codes publicly available at: https://github.com/YanHu-or-SawyerHu/prompt-learning-based-sentence-classifier-in-medical-abstracts.