Health Information Science and Systems最新文献

{"title":"Multi-omics prognostic signatures of IPO11 mRNA expression and clinical outcomes in colorectal cancer using bioinformatics approaches.","authors":"Mohammed Othman Aljahdali, Mohammad Habibur Rahman Molla","doi":"10.1007/s13755-023-00259-2","DOIUrl":"10.1007/s13755-023-00259-2","url":null,"abstract":"<p><p>The most prevalent malignant illness of the gastrointestinal system, colorectal cancer, is the third most prevalent cancer in males and the second most prevalent cancer in women. Importin-11 is a protein that acts as a regulator of cancer cell proliferation in colorectal tumours by conveying <math><mi>β</mi></math>-catenin to the cell nucleus. However, the IPO11 gene was found to encode a protein called Importin-11, which functions as a nucleus importer for the cell. As a result, preventing <math><mi>β</mi></math>-catenin from entering the nucleus requires blocking Importin-11. As a result, we conducted a multi-omics investigation to assess IPO11 gene potential as a therapeutic biomarker for human colorectal cancer (CC). Oncomine, GEPIA2, immunohisto-chemistry, and UALCAN databases were used to analyses the mRNA expression profiles of IPO11 in CC. The investigation has yielded clear evidence of the increase of IPO11 expression in CC subtypes, as indicated by the data acquired. Analysing CC research from the cBioPortal database, the study discovered three new missense mutations in the importin-11 protein sequence at a frequency of 0.00-1.50% copy number changes. Additionally, the Kaplan-Meier plots demonstrated a strong connection concerning IPO11 downregulation and a poorer CC patient survival rate. The co-expressed gene profile of IPO11 was likewise associated with the onset of CC. IPO11 co-expressed gene profile was also linked to CC development. Moreover, the correlation analysis using bc-GenExMiner and the UCSC Xena server identified KIF2A as the most positively co-expressed gene. The study found that KIF2A and its co-expressed genes were involved in a wide variety of cancer progression pathways using the Enrichr database. Cumulatively, this result will not only provide new information about the expression of IPO11 associated with CC progression and patient survival, but could also serve as a therapeutic biomarker for treating CC in a significant and worthwhile manner.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13755-023-00259-2.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"57"},"PeriodicalIF":4.7,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10678892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138463416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated lead toxicity prediction using computational modelling framework.","authors":"Priyanka Chaurasia, Sally I McClean, Abbas Ali Mahdi, Pratheepan Yogarajah, Jamal Akhtar Ansari, Shipra Kunwar, Mohammad Kaleem Ahmad","doi":"10.1007/s13755-023-00257-4","DOIUrl":"10.1007/s13755-023-00257-4","url":null,"abstract":"<p><strong>Background: </strong>Lead, an environmental toxicant, accounts for 0.6% of the global burden of disease, with the highest burden in developing countries. Lead poisoning is very much preventable with adequate and timely action. Therefore, it is important to identify factors that contribute to maternal BLL and minimise them to reduce the transfer to the foetus. Literacy and awareness related to its impact are low and the clinical establishment for biological monitoring of blood lead level (BLL) is low, costly, and time-consuming. A significant contribution to an infant's BLL load is caused by maternal lead transfer during pregnancy. This acts as the first pathway to the infant's lead exposure. The social and demographic information that includes lifestyle and environmental factors are key to maternal lead exposure.</p><p><strong>Results: </strong>We propose a novel approach to build a computational model framework that can predict lead toxicity levels in maternal blood using a set of sociodemographic features. To illustrate our proposed approach, maternal data comprising socio-demographic features and blood samples from the pregnant woman is collected, analysed, and modelled. The computational model is built that learns from the maternal data and then predicts lead level in a pregnant woman using a set of questionnaires that relate to the maternal's social and demographic information as the first point of testing. The range of features identified in the built models can estimate the underlying function and provide an understanding of the toxicity level. Following feature selection methods, the 12-feature set obtained from the Boruta algorithm gave better prediction results (<i>k</i>NN = 76.84%, DT = 74.70%, and NN = 73.99%).</p><p><strong>Conclusion: </strong>The built prediction model can be beneficial in improving the point of care and hence reducing the cost and the risk involved. It is envisaged that in future, the proposed methodology will become a part of a screening process to assist healthcare experts at the point of evaluating the lead toxicity level in pregnant women. Women screened positive could be given a range of facilities including preliminary counselling to being referred to the health centre for further diagnosis. Steps could be taken to reduce maternal lead exposure; hence, it could also be possible to mitigate the infant's lead exposure by reducing transfer from the pregnant woman.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"56"},"PeriodicalIF":6.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138463414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.","authors":"Gaoshi Li, Xinlong Luo, Zhipeng Hu, Jingli Wu, Wei Peng, Jiafei Liu, Xiaoshu Zhu","doi":"10.1007/s13755-023-00252-9","DOIUrl":"10.1007/s13755-023-00252-9","url":null,"abstract":"<p><p>Essential proteins play a vital role in development and reproduction of cells. The identification of essential proteins helps to understand the basic survival of cells. Due to time-consuming, costly and inefficient with biological experimental methods for discovering essential proteins, computational methods have gained increasing attention. In the initial stage, essential proteins are mainly identified by the centralities based on protein-protein interaction (PPI) networks, which limit their identification rate due to many false positives in PPI networks. In this study, a purified PPI network is firstly introduced to reduce the impact of false positives in the PPI network. Secondly, by analyzing the similarity relationship between a protein and its neighbors in the PPI network, a new centrality called neighborhood similarity centrality (NSC) is proposed. Thirdly, based on the subcellular localization and orthologous data, the protein subcellular localization score and ortholog score are calculated, respectively. Fourthly, by analyzing a large number of methods based on multi-feature fusion, it is found that there is a special relationship among features, which is called dominance relationship, then, a novel model based on dominance relationship is proposed. Finally, NSC, subcellular localization score, and ortholog score are fused by the dominance relationship model, and a new method called NSO is proposed. In order to verify the performance of NSO, the seven representative methods (ION, NCCO, E_POC, SON, JDC, PeC, WDC) are compared on yeast datasets. The experimental results show that the NSO method has higher identification rate than other methods.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"55"},"PeriodicalIF":4.7,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interrelated feature selection from health surveys using domain knowledge graph.","authors":"Markian Jaworsky, Xiaohui Tao, Lei Pan, Shiva Raj Pokhrel, Jianming Yong, Ji Zhang","doi":"10.1007/s13755-023-00254-7","DOIUrl":"10.1007/s13755-023-00254-7","url":null,"abstract":"<p><p>Finding patterns among risk factors and chronic illness can suggest similar causes, provide guidance to improve healthy lifestyles, and give clues for possible treatments for outliers. Prior studies have typically isolated data challenges from single-disease datasets. However, the predictive power of multiple diseases is more helpful in establishing a healthy lifestyle than investigating one disease. Most studies typically focus on single-disease datasets; however, to ensure that health advice is generalized and contemporary, the features that predict the likelihood of many diseases can improve health advice effectiveness when considering the patient's point of view. We construct and present a novel knowledge-based qualitative method to remove redundant features from a dataset and redefine the outliers. The results of our trials upon five annual chronic disease health surveys demonstrate that our Knowledge Graph-based feature selection, when applied to many machine learning and deep learning multi-label classifiers, can improve classification performance. Our methodology is compatible with future directions, such as graph neural networks. It provides clinicians with an efficient process to select the most relevant health survey questions and responses regarding single or many human organ systems.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"54"},"PeriodicalIF":4.7,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138055584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EAPR: explainable and augmented patient representation learning for disease prediction","authors":"Jiancheng Zhang, Yonghui Xu, Bicui Ye, Yibowen Zhao, Xiaofang Sun, Qi Meng, Yang Zhang, Lizhen Cui","doi":"10.1007/s13755-023-00256-5","DOIUrl":"https://doi.org/10.1007/s13755-023-00256-5","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"33 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ADHD-KG: a knowledge graph of attention deficit hyperactivity disorder","authors":"Emmanuel Papadakis, George Baryannis, Sotiris Batsakis, Marios Adamou, Zhisheng Huang, Grigoris Antoniou","doi":"10.1007/s13755-023-00253-8","DOIUrl":"https://doi.org/10.1007/s13755-023-00253-8","url":null,"abstract":"Abstract Purpose Attention Deficit Hyperactivity Disorder (ADHD) is a widespread condition that affects human behaviour and can interfere with daily activities and relationships. Medication or medical information about ADHD can be found in several data sources on the Web. Such distribution of knowledge raises notable obstacles since researchers and clinicians must manually combine various sources to deeply explore aspects of ADHD. Knowledge graphs have been widely used in medical applications due to their data integration capabilities, offering rich data stores of information built from heterogeneous sources; however, general purpose knowledge graphs cannot represent knowledge in sufficient detail, thus there is an increasing interest in domain-specific knowledge graphs. Methods In this work we propose a Knowledge Graph of ADHD. In particular, we introduce an automated procedure enabling the construction of a knowledge graph that covers knowledge from a wide range of data sources primarily focusing on adult ADHD. These include relevant literature and clinical trials, prescribed medication and their known side-effects. Data integration between these data sources is accomplished by employing a suite of information linking procedures, which aim to connect resources by relating them to common concepts found in medical thesauri. Results The usability and appropriateness of the developed knowledge graph is evaluated through a series of use cases that illustrate its ability to enhance and accelerate information retrieval. Conclusion The Knowledge Graph of ADHD can provide valuable assistance to researchers and clinicians in the research, training, diagnostic and treatment processes for ADHD.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"13 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135087208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Liver fibrosis MR images classification based on higher-order interaction and sample distribution rebalancing.","authors":"Ling Zhang, Zhennan Xiao, Wenchao Jiang, Chengbin Luo, Ming Ye, Guanghui Yue, Zhiyuan Chen, Shuman Ouyang, Yupin Liu","doi":"10.1007/s13755-023-00255-6","DOIUrl":"10.1007/s13755-023-00255-6","url":null,"abstract":"<p><p>The fractal features of liver fibrosis MR images exhibit an irregular fragmented distribution, and the diffuse feature distribution lacks interconnectivity, result- ing in incomplete feature learning and poor recognition accuracy. In this paper, we insert recursive gated convolution into the ResNet18 network to introduce spatial information interactions during the feature learning process and extend it to higher orders using recursion. Higher-order spatial information interactions enhance the correlation between features and enable the neural network to focus more on the pixel-level dependencies, enabling a global interpretation of liver MR images. Additionally, the existence of light scattering and quantum noise during the imaging process, coupled with environmental factors such as breathing artifacts caused by long time breath holding, affects the quality of the MR images. To improve the classification performance of the neural network and better cap- ture sample features, we introduce the Adaptive Rebalance loss function and incorporate the feature paradigm as a learnable adaptive attribute into the angular margin auxiliary function. Adaptive Rebalance loss function can expand the inter-class distance and narrow the intra-class difference to further enhance discriminative ability of the model. We conduct extensive experiments on liver fibrosis MR imaging involving 209 patients. The results demonstrate an average improvement of two percent in recognition accuracy compared to ResNet18. The github is at https://github.com/XZN1233/paper.git.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"51"},"PeriodicalIF":4.7,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89719974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting drug-drug interactions based on multi-view and multichannel attention deep learning.","authors":"Liyu Huang, Qingfeng Chen, Wei Lan","doi":"10.1007/s13755-023-00250-x","DOIUrl":"10.1007/s13755-023-00250-x","url":null,"abstract":"<p><p>Predicting drug-drug interactions (DDIs) has become a major concern in the drug research field because it helps explore the pharmacological function of drugs and enables the development of new therapeutic drugs. Existing prediction methods simply integrate multiple drug attributes or perform tasks on a biomedical knowledge graph (KG). Though effective, few methods can fully utilize multi-source drug data information. In this paper, a multi-view and multichannel attention deep learning (MMADL) model is proposed, which not only extracts rich drug features containing both drug attributes and drug-related entity information from multi-source databases, but also considers the consistency and complementarity of different drug feature representation learning approaches to improve the effectiveness and accuracy of DDI prediction. A single-layer perceptron encoder is applied to encode multi-source drug information to obtain multi-view drug representation vectors in the same linear space. Then, the multichannel attention mechanism is introduced to obtain the attention weight by adaptively learning the importance of drug features according to their contributions to DDI prediction. Further, the representation vectors of multi-view drug pairs with attention weights are used as inputs of the deep neural network to predict potential DDI. The accuracy and precision-recall curves of MMADL are 93.05 and 95.94, respectively. The results indicate that the proposed method outperforms other state-of-the-art methods.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"50"},"PeriodicalIF":4.7,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71522917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thyroidkeeper: a healthcare management system for patients with thyroid diseases.","authors":"Jing Zhang, Jianhua Li, Yi Zhu, Yu Fu, Lixia Chen","doi":"10.1007/s13755-023-00251-w","DOIUrl":"10.1007/s13755-023-00251-w","url":null,"abstract":"<p><p>Thyroid diseases, especially thyroid tumors, have a huge population in China. The postoperative patients, under China's incomplete tertiary diagnosis and treatment system, will frequently go to tertiary hospitals for follow-up and medication adjustment, resulting in heavy burdens on both specialists and patients. To help postoperative patients recover better against the above adverse conditions, a novel mobile application ThyroidKeeper is proposed as a collaborative AI-based platform that benefits both patients and doctors. In addition to routine health records and management functions, ThyroidKeeper has achieved several innovative points. First, it can automatically adjust medication dosage for patients during their rehabilitation based on their medical history, laboratory indicators, physical health status, and current medication. Second, it can comprehensively predict the possible complications based on the patient's health status and the health status of similar groups utilizing graph neural networks. Finally, the employing of graph neural network models can improve the efficiency of online communication between doctors and patients, help doctors obtain medical information for patients more quickly and precisely, and make more accurate diagnoses. The preliminary evaluation in both laboratory and real-world environments shows the advantages of the proposed ThyroidKeeper system.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"49"},"PeriodicalIF":4.7,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49683477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan.","authors":"Chun-Te Huang, Tsai-Jung Wang, Li-Kuo Kuo, Ming-Ju Tsai, Cong-Tat Cia, Dung-Hung Chiang, Po-Jen Chang, Inn-Wen Chong, Yi-Shan Tsai, Yuan-Chia Chu, Chia-Jen Liu, Cheng-Hsu Chen, Kai-Chih Pai, Chieh-Liang Wu","doi":"10.1007/s13755-023-00248-5","DOIUrl":"10.1007/s13755-023-00248-5","url":null,"abstract":"<p><strong>Purpose: </strong>To address the contentious data sharing across hospitals, this study adopted a novel approach, federated learning (FL), to establish an aggregate model for acute kidney injury (AKI) prediction in critically ill patients in Taiwan.</p><p><strong>Methods: </strong>This study used data from the Critical Care Database of Taichung Veterans General Hospital (TCVGH) from 2015 to 2020 and electrical medical records of the intensive care units (ICUs) between 2018 and 2020 of four referral centers in different areas across Taiwan. AKI prediction models were trained and validated thereupon. An FL-based prediction model across hospitals was then established.</p><p><strong>Results: </strong>The study included 16,732 ICU admissions from the TCVGH and 38,424 ICU admissions from the other four hospitals. The complete model with 60 features and the parsimonious model with 21 features demonstrated comparable accuracies using extreme gradient boosting, neural network (NN), and random forest, with an area under the receiver-operating characteristic (AUROC) curve of approximately 0.90. The Shapley Additive Explanations plot demonstrated that the selected features were the key clinical components of AKI for critically ill patients. The AUROC curve of the established parsimonious model for external validation at the four hospitals ranged from 0.760 to 0.865. NN-based FL slightly improved the model performance at the four centers.</p><p><strong>Conclusion: </strong>A reliable prediction model for AKI in ICU patients was developed with a lead time of 24 h, and it performed better when the novel FL platform across hospitals was implemented.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13755-023-00248-5.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"48"},"PeriodicalIF":6.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41215739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}