Journal of Biomedical Informatics最新文献

{"title":"Community knowledge graph abstraction for enhanced link prediction: A study on PubMed knowledge graph","authors":"Yang Zhao ,&nbsp;Danushka Bollegala ,&nbsp;Shunsuke Hirose ,&nbsp;Yingzi Jin ,&nbsp;Tomotake Kozu","doi":"10.1016/j.jbi.2024.104725","DOIUrl":"10.1016/j.jbi.2024.104725","url":null,"abstract":"<div><h3>Objective:</h3><p>As new knowledge is produced at a rapid pace in the biomedical field, existing biomedical Knowledge Graphs (KGs) cannot be manually updated in a timely manner. Previous work in Natural Language Processing (NLP) has leveraged link prediction to infer the missing knowledge in general-purpose KGs. Inspired by this, we propose to apply link prediction to existing biomedical KGs to infer missing knowledge. Although Knowledge Graph Embedding (KGE) methods are effective in link prediction tasks, they are less capable of capturing relations between communities of entities with specific attributes (Fanourakis et al., 2023).</p></div><div><h3>Methods:</h3><p>To address this challenge, we proposed an entity distance-based method for abstracting a Community Knowledge Graph (CKG) from a simplified version of the pre-existing PubMed Knowledge Graph (PKG) (Xu et al., 2020). For link prediction on the abstracted CKG, we proposed an extension approach for the existing KGE models by linking the information in the PKG to the abstracted CKG. The applicability of this extension was proved by employing six well-known KGE models: TransE, TransH, DistMult, ComplEx, SimplE, and RotatE. Evaluation metrics including Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hits@<span><math><mi>k</mi></math></span> were used to assess the link prediction performance. In addition, we presented a backtracking process that traces the results of CKG link prediction back to the PKG scale for further comparison.</p></div><div><h3>Results:</h3><p>Six different CKGs were abstracted from the PKG by using embeddings of the six KGE methods. The results of link prediction in these abstracted CKGs indicate that our proposed extension can improve the existing KGE methods, achieving a top-10 accuracy of 0.69 compared to 0.5 for TransE, 0.7 compared to 0.54 for TransH, 0.67 compared to 0.6 for DistMult, 0.73 compared to 0.57 for ComplEx, 0.73 compared to 0.63 for SimplE, and 0.85 compared to 0.76 for RotatE on their CKGs, respectively. These improved performances also highlight the wide applicability of the extension approach.</p></div><div><h3>Conclusion:</h3><p>This study proposed novel insights into abstracting CKGs from the PKG. The extension approach indicated enhanced performance of the existing KGE methods and has applicability. As an interesting future extension, we plan to conduct link prediction for entities that are newly introduced to the PKG.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"158 ","pages":"Article 104725"},"PeriodicalIF":4.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1532046424001436/pdfft?md5=1241f4473bb8cac3c0c3666b4968750a&pid=1-s2.0-S1532046424001436-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142243176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extracting lung cancer staging descriptors from pathology reports: A generative language model approach","authors":"Hyeongmin Cho ,&nbsp;Sooyoung Yoo ,&nbsp;Borham Kim ,&nbsp;Sowon Jang ,&nbsp;Leonard Sunwoo ,&nbsp;Sanghwan Kim ,&nbsp;Donghyoung Lee ,&nbsp;Seok Kim ,&nbsp;Sejin Nam ,&nbsp;Jin-Haeng Chung","doi":"10.1016/j.jbi.2024.104720","DOIUrl":"10.1016/j.jbi.2024.104720","url":null,"abstract":"<div><h3>Background</h3><p>In oncology, electronic health records contain textual key information for the diagnosis, staging, and treatment planning of patients with cancer. However, text data processing requires a lot of time and effort, which limits the utilization of these data. Recent advances in natural language processing (NLP) technology, including large language models, can be applied to cancer research. Particularly, extracting the information required for the pathological stage from surgical pathology reports can be utilized to update cancer staging according to the latest cancer staging guidelines.</p></div><div><h3>Objectives</h3><p>This study has two main objectives. The first objective is to evaluate the performance of extracting information from text-based surgical pathology reports and determining pathological stages based on the extracted information using fine-tuned generative language models (GLMs) for patients with lung cancer. The second objective is to determine the feasibility of utilizing relatively small GLMs for information extraction in a resource-constrained computing environment.</p></div><div><h3>Methods</h3><p>Lung cancer surgical pathology reports were collected from the Common Data Model database of Seoul National University Bundang Hospital (SNUBH), a tertiary hospital in Korea. We selected 42 descriptors necessary for tumor-node (TN) classification based on these reports and created a gold standard with validation by two clinical experts. The pathology reports and gold standard were used to generate prompt-response pairs for training and evaluating GLMs which then were used to extract information required for staging from pathology reports.</p></div><div><h3>Results</h3><p>We evaluated the information extraction performance of six trained models as well as their performance in TN classification using the extracted information. The Deductive Mistral-7B model, which was pre-trained with the deductive dataset, showed the best performance overall, with an exact match ratio of 92.24% in the information extraction problem and an accuracy of 0.9876 (predicting T and N classification concurrently) in classification.</p></div><div><h3>Conclusion</h3><p>This study demonstrated that training GLMs with deductive datasets can improve information extraction performance, and GLMs with a relatively small number of parameters at approximately seven billion can achieve high performance in this problem. The proposed GLM-based information extraction method is expected to be useful in clinical decision-making support, lung cancer staging and research.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104720"},"PeriodicalIF":4.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1532046424001382/pdfft?md5=a07a39b7bc41fc8621f04b2757525870&pid=1-s2.0-S1532046424001382-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142132875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Promoting smartphone-based keratitis screening using meta-learning: A multicenter study","authors":"Zhongwen Li ,&nbsp;Yangyang Wang ,&nbsp;Kuan Chen ,&nbsp;Wei Qiang ,&nbsp;Xihang Zong ,&nbsp;Ke Ding ,&nbsp;Shihong Wang ,&nbsp;Shiqi Yin ,&nbsp;Jiewei Jiang ,&nbsp;Wei Chen","doi":"10.1016/j.jbi.2024.104722","DOIUrl":"10.1016/j.jbi.2024.104722","url":null,"abstract":"<div><h3>Objective</h3><p>Keratitis is the primary cause of corneal blindness worldwide. Prompt identification and referral of patients with keratitis are fundamental measures to improve patient prognosis. Although deep learning can assist ophthalmologists in automatically detecting keratitis through a slit lamp camera, remote and underserved areas often lack this professional equipment. Smartphones, a widely available device, have recently been found to have potential in keratitis screening. However, given the limited data available from smartphones, employing traditional deep learning algorithms to construct a robust intelligent system presents a significant challenge. This study aimed to propose a meta-learning framework, cosine nearest centroid-based metric learning (CNCML), for developing a smartphone-based keratitis screening model in the case of insufficient smartphone data by leveraging the prior knowledge acquired from slit-lamp photographs.</p></div><div><h3>Methods</h3><p>We developed and assessed CNCML based on 13,009 slit-lamp photographs and 4,075 smartphone photographs that were obtained from 3 independent clinical centers. To mimic real-world scenarios with various degrees of sample scarcity, we used training sets of different sizes (0 to 20 photographs per class) from the HUAWEI smartphone to train CNCML. We evaluated the performance of CNCML not only on an internal test dataset but also on two external datasets that were collected by two different brands of smartphones (VIVO and XIAOMI) in another clinical center. Furthermore, we compared the performance of CNCML with that of traditional deep learning models on these smartphone datasets. The accuracy and macro-average area under the curve (macro-AUC) were utilized to evaluate the performance of models.</p></div><div><h3>Results</h3><p>With merely 15 smartphone photographs per class used for training, CNCML reached accuracies of 84.59%, 83.15%, and 89.99% on three smartphone datasets, with corresponding macro-AUCs of 0.96, 0.95, and 0.98, respectively. The accuracies of CNCML on these datasets were 0.56% to 9.65% higher than those of the most competitive traditional deep learning models.</p></div><div><h3>Conclusions</h3><p>CNCML exhibited fast learning capabilities, attaining remarkable performance with a small number of training samples. This approach presents a potential solution for transitioning intelligent keratitis detection from professional devices (e.g., slit-lamp cameras) to more ubiquitous devices (e.g., smartphones), making keratitis screening more convenient and effective.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104722"},"PeriodicalIF":4.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MAFT-SO: A novel multi-atlas fusion template based on spatial overlap for ASD diagnosis","authors":"Yuefeng Ma ,&nbsp;Xiaochen Mu ,&nbsp;Tengfei Zhang ,&nbsp;Yu Zhao","doi":"10.1016/j.jbi.2024.104714","DOIUrl":"10.1016/j.jbi.2024.104714","url":null,"abstract":"<div><p>Autism spectrum disorder (ASD) is a common neurological condition. Early diagnosis and treatment are essential for enhancing the life quality of individuals with ASD. However, most existing studies either focus solely on the brain networks of subjects within a single atlas or merely employ simple matrix concatenation to represent the fusion of multi-atlas. These approaches neglected the natural spatial overlap that exists between brain regions across multi-atlas and did not fully capture the comprehensive information of brain regions under different atlases. To tackle this weakness, in this paper, we propose a novel multi-atlas fusion template based on spatial overlap degree of brain regions, which aims to obtain a comprehensive representation of brain networks. Specifically, we formally define a measurement of the spatial overlap among brain regions across different atlases, named spatial overlap degree. Then, we fuse the multi-atlas to obtain brain networks of each subject based on spatial overlap. Finally, the GCN is used to perform the final classification. The experimental results on Autism Brain Imaging Data Exchange (ABIDE) demonstrate that our proposed method achieved an accuracy of 0.757. Overall, our method outperforms SOTA methods in ASD/TC classification.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104714"},"PeriodicalIF":4.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142072869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating graph convolutional networks to enhance prompt learning for biomedical relation extraction","authors":"Bocheng Guo ,&nbsp;Jiana Meng ,&nbsp;Di Zhao ,&nbsp;Xiangxing Jia ,&nbsp;Yonghe Chu ,&nbsp;Hongfei Lin","doi":"10.1016/j.jbi.2024.104717","DOIUrl":"10.1016/j.jbi.2024.104717","url":null,"abstract":"<div><h3>Background and Objective:</h3><p>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.</p></div><div><h3>Methods:</h3><p>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.</p></div><div><h3>Results:</h3><p>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.</p></div><div><h3>Conclusions:</h3><p>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.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104717"},"PeriodicalIF":4.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosis","authors":"Wei A. Huang ,&nbsp;Matthew Engelhard ,&nbsp;Marika Coffman ,&nbsp;Elliot D. Hill ,&nbsp;Qin Weng ,&nbsp;Abby Scheer ,&nbsp;Gary Maslow ,&nbsp;Ricardo Henao ,&nbsp;Geraldine Dawson ,&nbsp;Benjamin A. Goldstein","doi":"10.1016/j.jbi.2024.104711","DOIUrl":"10.1016/j.jbi.2024.104711","url":null,"abstract":"<div><h3>Objective</h3><p>This study aimed to develop a novel approach using routinely collected electronic health records (EHRs) data to improve the prediction of a rare event. We illustrated this using an example of improving early prediction of an autism diagnosis, given its low prevalence, by leveraging correlations between autism and other neurodevelopmental conditions (NDCs).</p></div><div><h3>Methods</h3><p>To achieve this, we introduced a conditional multi-label model by merging conditional learning and multi-label methodologies. The conditional learning approach breaks a hard task into more manageable pieces in each stage, and the multi-label approach utilizes information from related neurodevelopmental conditions to learn predictive latent features. The study involved forecasting autism diagnosis by age 5.5 years, utilizing data from the first 18 months of life, and the analysis of feature importance correlations to explore the alignment within the feature space across different conditions.</p></div><div><h3>Results</h3><p>Upon analysis of health records from 18,156 children, we are able to generate a model that predicts a future autism diagnosis with moderate performance (AUROC=0.76). The proposed conditional multi-label method significantly improves predictive performance with an AUROC of 0.80 (<em>p</em> &lt; 0.001). Further examination shows that both the conditional and multi-label approach alone provided marginal lift to the model performance compared to a one-stage one-label approach. We also demonstrated the generalizability and applicability of this method using simulated data with high correlation between feature vectors for different labels.</p></div><div><h3>Conclusion</h3><p>Our findings underscore the effectiveness of the developed conditional multi-label model for early prediction of an autism diagnosis. The study introduces a versatile strategy applicable to prediction tasks involving limited target populations but sharing underlying features or etiology among related groups.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104711"},"PeriodicalIF":4.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing gait dysfunction severity in Parkinson’s Disease using 2-Stream Spatial–Temporal Neural Network","authors":"","doi":"10.1016/j.jbi.2024.104679","DOIUrl":"10.1016/j.jbi.2024.104679","url":null,"abstract":"<div><p>Parkinson’s Disease (PD), a neurodegenerative disorder, significantly impacts the quality of life for millions of people worldwide. PD primarily impacts dopaminergic neurons in the brain’s substantia nigra, resulting in dopamine deficiency and gait impairments such as bradykinesia and rigidity. Currently, several well-established tools, such as the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and Hoehn and Yahr (H&amp;Y) Scale, are used for evaluating gait dysfunction in PD. While insightful, these methods are subjective, time-consuming, and often ineffective in early-stage diagnosis. Other methods using specialized sensors and equipment to measure movement disorders are cumbersome and expensive, limiting their accessibility. This study introduces a hierarchical approach to evaluating gait dysfunction in PD through videos. The novel 2-Stream Spatial–Temporal Neural Network (2S-STNN) leverages the spatial–temporal features from the skeleton and silhouette streams for PD classification. This approach achieves an accuracy rate of 89% and outperforms other state-of-the-art models. The study also employs saliency values to highlight critical body regions that significantly influence model decisions and are severely affected by the disease. For a more detailed analysis, the study investigates 21 specific gait attributes for a nuanced quantification of gait disorders. Parameters such as walking pace, step length, and neck forward angle are found to be strongly correlated with PD gait severity categories. This approach offers a comprehensive and convenient solution for PD management in clinical settings, enabling patients to receive a more precise evaluation and monitoring of their gait impairments.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104679"},"PeriodicalIF":4.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141457162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive dual-stream contrastive learning for radiology report generation","authors":"Ziqi Zhang,&nbsp;Ailian Jiang","doi":"10.1016/j.jbi.2024.104718","DOIUrl":"10.1016/j.jbi.2024.104718","url":null,"abstract":"<div><p>Radiology report generation automates diagnostic narrative synthesis from medical imaging data. Current report generation methods primarily employ knowledge graphs for image enhancement, neglecting the interpretability and guiding function of the knowledge graphs themselves. Additionally, few approaches leverage the stable modal alignment information from multimodal pre-trained models to facilitate the generation of radiology reports. We propose the Terms-Guided Radiology Report Generation (TGR), a simple and practical model for generating reports guided primarily by anatomical terms. Specifically, we utilize a dual-stream visual feature extraction module comprised of detail extraction module and a frozen multimodal pre-trained model to separately extract visual detail features and semantic features. Furthermore, a Visual Enhancement Module (VEM) is proposed to further enrich the visual features, thereby facilitating the generation of a list of anatomical terms. We integrate anatomical terms with image features and proceed to engage contrastive learning with frozen text embeddings, utilizing the stable feature space from these embeddings to boost modal alignment capabilities further. Our model incorporates the capability for manual input, enabling it to generate a list of organs for specifically focused abnormal areas or to produce more accurate single-sentence descriptions based on selected anatomical terms. Comprehensive experiments demonstrate the effectiveness of our method in report generation tasks, our TGR-S model reduces training parameters by 38.9% while performing comparably to current state-of-the-art models, and our TGR-B model exceeds the best baseline models across multiple metrics.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104718"},"PeriodicalIF":4.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SSGU-CD: A combined semantic and structural information graph U-shaped network for document-level Chemical-Disease interaction extraction","authors":"Pengyuan Nie ,&nbsp;Jinzhong Ning ,&nbsp;Mengxuan Lin ,&nbsp;Zhihao Yang ,&nbsp;Lei Wang","doi":"10.1016/j.jbi.2024.104719","DOIUrl":"10.1016/j.jbi.2024.104719","url":null,"abstract":"<div><p>Document-level interaction extraction for Chemical-Disease is aimed at inferring the interaction relations between chemical entities and disease entities across multiple sentences. Compared with sentence-level relation extraction, document-level relation extraction can capture the associations between different entities throughout the entire document, which is found to be more practical for biomedical text information. However, current biomedical extraction methods mainly concentrate on sentence-level relation extraction, making it difficult to access the rich structural information contained in documents in practical application scenarios. We put forward SSGU-CD, a combined <strong><u>S</u></strong>emantic and <strong><u>S</u></strong>tructural information <strong><u>G</u></strong>raph <strong><u>U</u></strong>-shaped network for document-level <strong><u>C</u></strong>hemical-<strong><u>D</u></strong>isease interaction extraction. This framework effectively stores document semantic and structure information as graphs and can fuse the original context information of documents. Using the framework, we propose a balanced combination of cross-entropy loss function to facilitate collaborative optimization among models with the aim of enhancing the ability to extract Chemical-Disease interaction relations. We evaluated SSGU-CD on the document-level relation extraction dataset CDR and BioRED, and the results demonstrate that the framework can significantly improve the extraction performance.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104719"},"PeriodicalIF":4.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1532046424001370/pdfft?md5=ccbd03895ffdd2c9164f4a506fad5a18&pid=1-s2.0-S1532046424001370-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142107838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MolCFL: A personalized and privacy-preserving drug discovery framework based on generative clustered federated learning","authors":"Yan Guo,&nbsp;Yongqiang Gao,&nbsp;Jiawei Song","doi":"10.1016/j.jbi.2024.104712","DOIUrl":"10.1016/j.jbi.2024.104712","url":null,"abstract":"<div><p>In today’s era of rapid development of large models, the traditional drug development process is undergoing a profound transformation. The vast demand for data and consumption of computational resources are making independent drug discovery increasingly difficult. By integrating federated learning technology into the drug discovery field, we have found a solution that both protects privacy and shares computational power. However, the differences in data held by various pharmaceutical institutions and the diversity in drug design objectives have exacerbated the issue of data heterogeneity, making traditional federated learning consensus models unable to meet the personalized needs of all parties. In this study, we introduce and evaluate an innovative drug discovery framework, MolCFL, which utilizes a multi-layer perceptron (MLP) as the generator and a graph convolutional network (GCN) as the discriminator in a generative adversarial network (GAN). By learning the graph structure of molecules, it generates new molecules in a highly personalized manner and then optimizes the learning process by clustering federated learning, grouping compound data with high similarity. MolCFL not only enhances the model’s ability to protect privacy but also significantly improves the efficiency and personalization of molecular design. MolCFL exhibits superior performance when handling non-independently and identically distributed data compared to traditional models. Experimental results show that the framework demonstrates outstanding performance on two benchmark datasets, with the generated new molecules achieving over 90% in Uniqueness and close to 100% in Novelty. MolCFL not only improves the quality and efficiency of drug molecule design but also, through its highly customized clustered federated learning environment, promotes collaboration and specialization in the drug discovery process while ensuring data privacy. These features make MolCFL a powerful tool suitable for addressing the various challenges faced in the modern drug research and development field.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104712"},"PeriodicalIF":4.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}