Briefings in bioinformatics最新文献_第8页

A two-task predictor for discovering phase separation proteins and their undergoing mechanism. 发现相分离蛋白质及其作用机制的双任务预测器

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae528

Yetong Zhou, Shengming Zhou, Yue Bi, Quan Zou, Cangzhi Jia

{"title":"A two-task predictor for discovering phase separation proteins and their undergoing mechanism.","authors":"Yetong Zhou, Shengming Zhou, Yue Bi, Quan Zou, Cangzhi Jia","doi":"10.1093/bib/bbae528","DOIUrl":"10.1093/bib/bbae528","url":null,"abstract":"Liquid-liquid phase separation (LLPS) is one of the mechanisms mediating the compartmentalization of macromolecules (proteins and nucleic acids) in cells, forming biomolecular condensates or membraneless organelles. Consequently, the systematic identification of potential LLPS proteins is crucial for understanding the phase separation process and its biological mechanisms. A two-task predictor, Opt_PredLLPS, was developed to discover potential phase separation proteins and further evaluate their mechanism. The first task model of Opt_PredLLPS combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) through a fully connected layer, where the CNN utilizes evolutionary information features as input, and BiLSTM utilizes multimodal features as input. If a protein is predicted to be an LLPS protein, it is input into the second task model to predict whether this protein needs to interact with its partners to undergo LLPS. The second task model employs the XGBoost classification algorithm and 37 physicochemical properties following a three-step feature selection. The effectiveness of the model was validated on multiple benchmark datasets, and in silico saturation mutagenesis was used to identify regions that play a key role in phase separation. These findings may assist future research on the LLPS mechanism and the discovery of potential phase separation proteins.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11492799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142458361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AptaDiff: de novo design and optimization of aptamers based on diffusion models. AptaDiff：基于扩散模型的全新设计和优化适配体。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae517

Zhen Wang, Ziqi Liu, Wei Zhang, Yanjun Li, Yizhen Feng, Shaokang Lv, Han Diao, Zhaofeng Luo, Pengju Yan, Min He, Xiaolin Li

{"title":"AptaDiff: de novo design and optimization of aptamers based on diffusion models.","authors":"Zhen Wang, Ziqi Liu, Wei Zhang, Yanjun Li, Yizhen Feng, Shaokang Lv, Han Diao, Zhaofeng Luo, Pengju Yan, Min He, Xiaolin Li","doi":"10.1093/bib/bbae517","DOIUrl":"10.1093/bib/bbae517","url":null,"abstract":"Aptamers are single-stranded nucleic acid ligands, featuring high affinity and specificity to target molecules. Traditionally they are identified from large DNA/RNA libraries using $in vitro$ methods, like Systematic Evolution of Ligands by Exponential Enrichment (SELEX). However, these libraries capture only a small fraction of theoretical sequence space, and various aptamer candidates are constrained by actual sequencing capabilities from the experiment. Addressing this, we proposed AptaDiff, the first in silico aptamer design and optimization method based on the diffusion model. Our Aptadiff can generate aptamers beyond the constraints of high-throughput sequencing data, leveraging motif-dependent latent embeddings from variational autoencoder, and can optimize aptamers by affinity-guided aptamer generation according to Bayesian optimization. Comparative evaluations revealed AptaDiff's superiority over existing aptamer generation methods in terms of quality and fidelity across four high-throughput screening data targeting distinct proteins. Moreover, surface plasmon resonance experiments were conducted to validate the binding affinity of aptamers generated through Bayesian optimization for two target proteins. The results unveiled a significant boost of $87.9%$ and $60.2%$ in RU values, along with a 3.6-fold and 2.4-fold decrease in KD values for the respective target proteins. Notably, the optimized aptamers demonstrated superior binding affinity compared to top experimental candidates selected through SELEX, underscoring the promising outcomes of our AptaDiff in accelerating the discovery of superior aptamers.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142458363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MetaDegron: multimodal feature-integrated protein language model for predicting E3 ligase targeted degrons. MetaDegron：用于预测 E3 连接酶靶向脱胶子的多模态特征整合蛋白质语言模型。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae519

Mengqiu Zheng, Shaofeng Lin, Kunqi Chen, Ruifeng Hu, Liming Wang, Zhongming Zhao, Haodong Xu

{"title":"MetaDegron: multimodal feature-integrated protein language model for predicting E3 ligase targeted degrons.","authors":"Mengqiu Zheng, Shaofeng Lin, Kunqi Chen, Ruifeng Hu, Liming Wang, Zhongming Zhao, Haodong Xu","doi":"10.1093/bib/bbae519","DOIUrl":"10.1093/bib/bbae519","url":null,"abstract":"Protein degradation through the ubiquitin proteasome system at the spatial and temporal regulation is essential for many cellular processes. E3 ligases and degradation signals (degrons), the sequences they recognize in the target proteins, are key parts of the ubiquitin-mediated proteolysis, and their interactions determine the degradation specificity and maintain cellular homeostasis. To date, only a limited number of targeted degron instances have been identified, and their properties are not yet fully characterized. To tackle on this challenge, here we develop a novel deep-learning framework, namely MetaDegron, for predicting E3 ligase targeted degron by integrating the protein language model and comprehensive featurization strategies. Through extensive evaluations using benchmark datasets and comparison with existing method, such as Degpred, we demonstrate the superior performance of MetaDegron. Among functional features, MetaDegron allows batch prediction of targeted degrons of 21 E3 ligases, and provides functional annotations and visualization of multiple degron-related structural and physicochemical features. MetaDegron is freely available at http://modinfor.com/MetaDegron/. We anticipate that MetaDegron will serve as a useful tool for the clinical and translational community to elucidate the mechanisms of regulation of protein homeostasis, cancer research, and drug development.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142458377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review. 代谢物功能注释的当前方法和突出挑战：全面综述。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae498

Quang-Huy Nguyen, Ha Nguyen, Edwin C Oh, Tin Nguyen

{"title":"Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.","authors":"Quang-Huy Nguyen, Ha Nguyen, Edwin C Oh, Tin Nguyen","doi":"10.1093/bib/bbae498","DOIUrl":"https://doi.org/10.1093/bib/bbae498","url":null,"abstract":"Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142458370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gene expression prediction from histology images via hypergraph neural networks. 通过超图神经网络从组织学图像中预测基因表达。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae500

Bo Li, Yong Zhang, Qing Wang, Chengyang Zhang, Mengran Li, Guangyu Wang, Qianqian Song

{"title":"Gene expression prediction from histology images via hypergraph neural networks.","authors":"Bo Li, Yong Zhang, Qing Wang, Chengyang Zhang, Mengran Li, Guangyu Wang, Qianqian Song","doi":"10.1093/bib/bbae500","DOIUrl":"https://doi.org/10.1093/bib/bbae500","url":null,"abstract":"Spatial transcriptomics reveals the spatial distribution of genes in complex tissues, providing crucial insights into biological processes, disease mechanisms, and drug development. The prediction of gene expression based on cost-effective histology images is a promising yet challenging field of research. Existing methods for gene prediction from histology images exhibit two major limitations. First, they ignore the intricate relationship between cell morphological information and gene expression. Second, these methods do not fully utilize the different latent stages of features extracted from the images. To address these limitations, we propose a novel hypergraph neural network model, HGGEP, to predict gene expressions from histology images. HGGEP includes a gradient enhancement module to enhance the model's perception of cell morphological information. A lightweight backbone network extracts multiple latent stage features from the image, followed by attention mechanisms to refine the representation of features at each latent stage and capture their relations with nearby features. To explore higher-order associations among multiple latent stage features, we stack them and feed into the hypergraph to establish associations among features at different scales. Experimental results on multiple datasets from disease samples including cancers and tumor disease, demonstrate the superior performance of our HGGEP model than existing methods.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11472757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142458373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Making PBPK models more reproducible in practice. 使 PBPK 模型在实践中更具可重复性。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae569

Elena Domínguez-Romero, Stanislav Mazurenko, Martin Scheringer, Vítor A P Martins Dos Santos, Chris T Evelo, Mihail Anton, John M Hancock, Anže Županič, Maria Suarez-Diez

{"title":"Making PBPK models more reproducible in practice.","authors":"Elena Domínguez-Romero, Stanislav Mazurenko, Martin Scheringer, Vítor A P Martins Dos Santos, Chris T Evelo, Mihail Anton, John M Hancock, Anže Županič, Maria Suarez-Diez","doi":"10.1093/bib/bbae569","DOIUrl":"https://doi.org/10.1093/bib/bbae569","url":null,"abstract":"Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model's behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools. Guidelines to increase model reproducibility have been published. However, reproducibility remains a major challenge in this field. In this paper, we tackle this challenge for physiologically-based pharmacokinetic (PBPK) models, which represent the pharmacokinetics of chemicals following exposure in humans or animals. We summarize recommendations for PBPK model reporting that should apply during model development and implementation, in order to ensure model reproducibility and comprehensibility. We make a proposal aiming to harmonize abbreviations used in PBPK models. To illustrate these recommendations, we present an original and reproducible PBPK model code in MATLAB, alongside an example of MATLAB code converted to Systems Biology Markup Language format using MOCCASIN. As directions for future improvement, more tools to convert computational PBPK models from different software platforms into standard formats would increase the interoperability of these models. The application of other systems biology standards to PBPK models is encouraged. This work is the result of an interdisciplinary collaboration involving the ELIXIR systems biology community. More interdisciplinary collaborations like this would facilitate further harmonization and application of good modeling practices in different systems biology fields.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering. 基于结构对比学习的多级多视角网络，用于 scRNA-seq 数据聚类。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae562

Zhenqiu Shu, Min Xia, Kaiwen Tan, Yongbing Zhang, Zhengtao Yu

{"title":"Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering.","authors":"Zhenqiu Shu, Min Xia, Kaiwen Tan, Yongbing Zhang, Zhengtao Yu","doi":"10.1093/bib/bbae562","DOIUrl":"https://doi.org/10.1093/bib/bbae562","url":null,"abstract":"Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering. Firstly, the proposed method constructs shallow views through the $k$-nearest neighbor ($k$NN) and diffusion mapping (DM) algorithms, and then deep views are generated by utilizing the graph Laplacian filters. These deep multi-view data serve as the input for representation learning. To improve the clustering performance of scRNA-seq data, contrastive learning is introduced to enhance the discrimination ability of our network. Specifically, we construct a group contrastive loss for representation features and a structural consistency contrastive loss for structural relationships. Extensive experiments on eight real scRNA-seq datasets show that the proposed method outperforms other state-of-the-art methods in scRNA-seq data clustering tasks. Our source code has already been available at https://github.com/szq0816/scMMN.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity. HIP：一种考虑亚组异质性的高维多视角数据整合与预测方法。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae470

Jessica Butts, Leif Verace, Christine Wendt, Russel P Bowler, Craig P Hersh, Qi Long, Lynn Eberly, Sandra E Safo

{"title":"HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity.","authors":"Jessica Butts, Leif Verace, Christine Wendt, Russel P Bowler, Craig P Hersh, Qi Long, Lynn Eberly, Sandra E Safo","doi":"10.1093/bib/bbae470","DOIUrl":"10.1093/bib/bbae470","url":null,"abstract":"Epidemiologic and genetic studies in many complex diseases suggest subgroup disparities (e.g. by sex, race) in disease course and patient outcomes. We consider this from the standpoint of integrative analysis where we combine information from different views (e.g. genomics, proteomics, clinical data). Existing integrative analysis methods ignore the heterogeneity in subgroups, and stacking the views and accounting for subgroup heterogeneity does not model the association among the views. We propose Heterogeneity in Integration and Prediction (HIP), a statistical approach for joint association and prediction that leverages the strengths in each view to identify molecular signatures that are shared by and specific to a subgroup. We apply HIP to proteomics and gene expression data pertaining to chronic obstructive pulmonary disease (COPD) to identify proteins and genes shared by, and unique to, males and females, contributing to the variation in COPD, measured by airway wall thickness. Our COPD findings have identified proteins, genes, and pathways that are common across and specific to males and females, some implicated in COPD, while others could lead to new insights into sex differences in COPD mechanisms. HIP accounts for subgroup heterogeneity in multi-view data, ranks variables based on importance, is applicable to univariate or multivariate continuous outcomes, and incorporates covariate adjustment. With the efficient algorithms implemented using PyTorch, this method has many potential scientific applications and could enhance multiomics research in health disparities. HIP is available at https://github.com/lasandrall/HIP, a video tutorial at https://youtu.be/O6E2OLmeMDo and a Shiny Application at https://multi-viewlearn.shinyapps.io/HIP_ShinyApp/ for users with limited programming experience.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11440091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep contrastive learning for predicting cancer prognosis using gene expression values. 利用基因表达值预测癌症预后的深度对比学习。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae544

Anchen Sun, Elizabeth J Franzmann, Zhibin Chen, Xiaodong Cai

{"title":"Deep contrastive learning for predicting cancer prognosis using gene expression values.","authors":"Anchen Sun, Elizabeth J Franzmann, Zhibin Chen, Xiaodong Cai","doi":"10.1093/bib/bbae544","DOIUrl":"10.1093/bib/bbae544","url":null,"abstract":"Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor transcriptomes and clinical data to learn feature representations in a low-dimensional space. We then utilized these learned features to train a classifier to categorize tumors into a high- or low-risk group of recurrence. Using data from The Cancer Genome Atlas (TCGA), we demonstrated that CL can significantly improve classification accuracy. Specifically, our CL-based classifiers achieved an area under the receiver operating characteristic curve (AUC) greater than 0.8 for 14 types of cancer, and an AUC greater than 0.9 for 3 types of cancer. We also developed CL-based Cox (CLCox) models for predicting cancer prognosis. Our CLCox models trained with the TCGA data outperformed existing methods significantly in predicting the prognosis of 19 types of cancer under consideration. The performance of CLCox models and CL-based classifiers trained with TCGA lung and prostate cancer data were validated using the data from two independent cohorts. We also show that the CLCox model trained with the whole transcriptome significantly outperforms the Cox model trained with the 16 genes of Oncotype DX that is in clinical use for breast cancer patients. The trained models and the Python codes are publicly accessible and provide a valuable resource that will potentially find clinical applications for many types of cancer.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11521346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142543788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis. 开发并验证用于预测住院肝硬化患者多维虚弱程度的可解释机器学习模型。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae491

Fang Yang, Chaoqun Li, Wanting Yang, Yumei He, Liping Wu, Kui Jiang, Chao Sun

{"title":"Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis.","authors":"Fang Yang, Chaoqun Li, Wanting Yang, Yumei He, Liping Wu, Kui Jiang, Chao Sun","doi":"10.1093/bib/bbae491","DOIUrl":"10.1093/bib/bbae491","url":null,"abstract":"We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This study enrolled 622 patients hospitalized due to decompensating episodes at a tertiary hospital. The cohort data were randomly divided into training and test sets. External validation was carried out using 131 patients from other tertiary hospitals. The frail phenotype was defined according to a self-reported questionnaire (Frailty Index). The area under the receiver operating characteristics curve was adopted to compare the performance of five ML models. The importance of the features and interpretation of the ML models were determined using the SHAP method. The proportions of cirrhotic patients with nonfrail and frail phenotypes in combined training and test sets were 87.8% and 12.2%, respectively, while they were 88.5% and 11.5% in the external validation dataset. Five ML algorithms were used, and the random forest (RF) model exhibited substantially predictive performance. Regarding the external validation, the RF algorithm outperformed other ML models. Moreover, the SHAP method demonstrated that neutrophil-to-lymphocyte ratio, age, lymphocyte-to-monocyte ratio, ascites, and albumin served as the most important predictors for frailty. At the patient level, the SHAP force plot and decision plot exhibited a clinically meaningful explanation of the RF algorithm. We constructed an ML model (RF) providing accurate prediction of frail phenotype in decompensated cirrhosis. The explainability and generalizability may foster clinicians to understand contributors to this physiologically vulnerable situation and tailor interventions.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0