Proceedings. IEEE International Conference on Bioinformatics and Biomedicine最新文献

{"title":"Aligning Orphanet Classification to Identify Disease Characteristics among Rare Disease Clusters.","authors":"Sungrim Moon, Jessica Maine, Ewy Mathe, Qian Zhu","doi":"10.1109/bibm62325.2024.10822379","DOIUrl":"10.1109/bibm62325.2024.10822379","url":null,"abstract":"<p><p>Understanding the underlying etiologies of rare diseases may facilitate research across multiple conditions, enabling basket trail design and drug repurposing. In this study, we aligned clusters of rare diseases with Orphanet classifications to represent their shared etiologies and establish a foundation for further investigation on underly biological mechanism discovery. By utilizing the linearized Orphanet categories, we connected 35 clusters of rare diseases into 18 classifications. Significant associations were found between the categories \"Rare Developmental Defects During Embryogenesis\" and \"Rare Inborn Errors of Metabolism\" and the clusters in this study, suggesting that many rare diseases originating in the prenatal period or related to metabolism may present a substantial opportunity for success in future investigation.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2024 ","pages":"4561-4563"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Drug Repurposing Candidates for CLN3 Targeting Proteomics Expression Profile.","authors":"Shixue Sun, Rosemary Mejia, An N Dang Do, Qian Zhu","doi":"10.1109/bibm62325.2024.10822002","DOIUrl":"10.1109/bibm62325.2024.10822002","url":null,"abstract":"<p><p>Juvenile neuronal ceroid lipofuscinosis (CLN3) is a rare neurodegenerative disorder lacking effective therapies. This study aimed at developing a drug repurposing approach to identify potential therapeutic candidates for CLN3 using its protein expression profile (CPEP) constructed from proteomics data. Differentially expressed proteins were identified and applied to query the iLINCS database, resulting in 60 FDA-approved drugs with reversal effects on CPEP. These candidates were further prioritized based on regulation strength, coverage, and blood-brain barrier permeability. Top candidates include Vorinostat and Cyclosporine, which have shown promise due to their significant regulation scores and blood-brain barrier permeation probability. These results provide opportunities for further investigation on novel therapies for CLN3.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2024 ","pages":"4572-4574"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12434628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting HIV Diagnosis Among Emerging Adults Using Electronic Health Records and Health Survey Data in All of Us Research Program.","authors":"Balu Bhasuran, Yiyang Liu, Mattia Prosperi, Karen MacDonell, Sylvie Naar, Zhe He","doi":"10.1109/bibm62325.2024.10822296","DOIUrl":"10.1109/bibm62325.2024.10822296","url":null,"abstract":"<p><p>The global decline in HIV incidence has not been mirrored in the United States, where young adults (ages 18-29) continue to account for a significant portion of new infections. In this study, we leverage the All of Us (AoU) Research Program's extensive electronic health records (EHRs) and health survey data to develop machine learning models capable of predicting HIV diagnoses at least three months before clinical identification. Among various models tested, the Support Vector Machine (SVM) model demonstrated a balanced performance, integrating clinically relevant features with robust predictive accuracy (AUC = 0.91). Risky drinking behaviors emerged as consistent top predictors across models, highlighting the importance of targeted interventions in this age group. Our findings underscore the potential of predictive analytics in enhancing HIV prevention strategies and informing public health efforts aimed at reducing HIV transmission among emerging adults.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2024 ","pages":"5433-5440"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpreting Lung Cancer Health Disparity between African American Males and European American Males.","authors":"Masrur Sobhan, Md Mezbahul Islam, Ananda Mohan Mondal","doi":"10.1109/bibm62325.2024.10822014","DOIUrl":"10.1109/bibm62325.2024.10822014","url":null,"abstract":"<p><p>Lung cancer remains a predominant cause of cancer-related deaths, with notable disparities in incidence and outcomes across racial and gender groups. This study addresses these disparities by developing a computational framework leveraging explainable artificial intelligence (XAI) to identify both patient- and cohort-specific biomarker genes in lung cancer. Specifically, we focus on two lung cancer subtypes, Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC), examining distinct racial and sex-specific cohorts: African American males (AAMs) and European American males (EAMs). This study innovatively structures classification tasks based on disease conditions rather than racial labels to avoid race-specific imbalance. We constructed four classification tasks- one three-class problem (LUAD-LUSC-HEALTHY) and three two-class problems (LUAD-LUSC, LUAD-HEALTHY, LUSC-HEALTHY)- to interpret the disease behavior of the patients in terms of genes and pathways. This methodology allows a LUAD or LUSC patient to be analyzed via multiple classifications, yielding robust disparity information for every patient. This preliminary work reports the disparity information for LUAD only. Utilizing Transcriptome data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects, we processed samples for LUAD, LUSC, and HEALTHY cohorts. We applied machine learning models, including convolutional neural network (CNN), logistic regression (LR), naïve Bayesian classifier (NB), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) for the classification. The SHapley Additive exPlanation (SHAP)-based interpretation of the best performing classification model uncovered cohort-specific genes and pathways related to health disparities between LUAD-AAM and LUAD-EAM cohorts.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2024 ","pages":"7141-7143"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11753458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking Distance Functions in Siamese Networks for Current and Prior Mammogram Image Analysis.","authors":"Sahand Hamzehei, Afsana Ahsan Jeny, Annie Jin, Clifford Yang, Sheida Nabavi","doi":"10.1109/bibm62325.2024.10822291","DOIUrl":"10.1109/bibm62325.2024.10822291","url":null,"abstract":"<p><p>Mammogram image analysis has benefited from advancements in artificial intelligence (AI), particularly through the use of Siamese networks, which, similar to radiologists, compare current and prior mammogram images to enhance diagnostic accuracy. One of the main challenges in employing Siamese networks for this purpose is selecting an effective distance function. Given the complexity of mammogram images and the high correlation between current and prior images, traditional distance functions in Siamese networks often fall short in capturing the subtle, non-linear differences between these correlated features. This study explores the impact of incorporating non-linear and correlation-sensitive distance functions within a Siamese network framework for analyzing paired mammogram images. We benchmarked different distance functions, including Euclidean, Manhattan, Mahalanobis, Radial Basis Function (RBF), and cosine, and introduced a novel combination of RBF with Matern Covariance. Our evaluation revealed that the RBF with Matern Covariance consistently outperformed other functions, emphasizing the importance of addressing non-linearity and correlation in this context. For instance, the ResNet50 model, when paired with this distance function, achieved an accuracy of 0.938, sensitivity of 0.921, precision of 0.955, specificity of 0.958, F1 score of 0.930, and AUC of 0.940. We observed similarly strong performance across other models as well. Furthermore, the robustness of our approach was confirmed through evaluation on a dataset of 30 cross-validation samples, demonstrating its generalizability. These findings underscore the effectiveness of non-linear and correlation-based distance functions in Siamese networks for improving the performance and generalization of mammogram image analysis. All codes used in this paper are available at https://github.com/NabaviLab/Benchmarking_Distance_Functions_in_Siamese_Networks.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2024 ","pages":"1996-2003"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12250141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144628015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parsing Clinical Trial Eligibility Criteria for Cohort Query by a Multi-Input Multi-Output Sequence Labeling Model.","authors":"Shubo Tian, Pengfei Yin, Hansi Zhang, Arslan Erdengasileng, Jiang Bian, Zhe He","doi":"10.1109/bibm58861.2023.10385876","DOIUrl":"10.1109/bibm58861.2023.10385876","url":null,"abstract":"<p><p>To enable electronic screening of eligible patients for clinical trials, free-text clinical trial eligibility criteria should be translated to a computable format. Natural language processing (NLP) techniques have the potential to automate this process. In this study, we explored a supervised multi-input multi-output (MIMO) sequence labelling model to parse eligibility criteria into combinations of fact and condition tuples. Our experiments on a small manually annotated training dataset showed that that the performance of the MIMO framework with a BERT-based encoder using all the input sequences achieved an overall lenient-level AUROC of 0.61. Although the performance is suboptimal, representing eligibility criteria into logical and semantically clear tuples can potentially make subsequent translation of these tuples into database queries more reliable.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"4426-4430"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11251129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Prediction Models for 30-Day Readmissions Among ICU Patients Using Both Structured and Unstructured Data in Electronic Health Records.","authors":"Alex Moerschbacher, Zhe He","doi":"10.1109/bibm58861.2023.10385612","DOIUrl":"10.1109/bibm58861.2023.10385612","url":null,"abstract":"<p><p>ICU readmissions are associated with poor outcomes for patients and poor performance of hospitals. Patients who are readmitted have an increased risk of in-hospital deaths; hospitals with a higher read-mission rate have a reduced profitability, due to an increase in cost and reduced payments from Medicare and Medicaid programs. Predicting a patient's likelihood of being readmitted to the ICU can help reduce early discharges, the risk of in-hospital deaths, and help in-crease profitability. In this study, we built and evaluated multiple machine learning models to predict 30-day readmission rates of ICU patients in the MIMIC-III database. We used both the structured data including demographics, laboratory tests, comorbidities, and unstructured discharge summaries as the predictors and evaluated different combinations of features. The best performing model in this study Logistic Regression achieved an AUROC of 75.7%. This study shows the potential of leveraging machine learning and deep learning for predicting ICU readmissions.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"4368-4373"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11271049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating Sex-Specific Disease Dynamics in Incident Dementia.","authors":"Muskan Garg, Xingyi Liu, Maria Vassilaki, Ronald C Petersen, Jennifer St Sauver, Sunghwan Sohn","doi":"10.1109/bibm58861.2023.10385324","DOIUrl":"10.1109/bibm58861.2023.10385324","url":null,"abstract":"<p><p>Dementia is among the leading causes of cognitive and functional loss and disability in older adults. Past studies suggested sex differences in health conditions and progression of cognitive decline. Existing studies on the temporal trajectory of health conditions for patient characterization after dementia diagnosis are scarce and ambiguous. Thus, there's limited and unclear research on how health conditions change over time after a dementia diagnosis. To this end, we aim to analyze the shift in medical conditions and examine sex-specific changes in patterns of chronic health conditions after dementia diagnosis. We centered our analysis on a 15-year window around the point of dementia diagnosis, encompassing the 5 years leading up to the diagnosis and the 10 years following it. We introduce (i) MedMet, a network metric to quantify the contribution of each medical condition, and (ii) growth and decay function for temporal trajectory analysis of medical conditions. Our experiments demonstrate that certain health conditions are more prevalent among females than males. Thus, our findings underscore the pressing need to examine differences between men and women, which could be important for healthcare utilization after a dementia diagnosis.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"4065-4072"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10883293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139974920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Assessment of Pneumocystosis with MIMIC Data.","authors":"Huanfei Wang, Qian Zhu, Jian Pei","doi":"10.1109/BIBM58861.2023.10385603","DOIUrl":"10.1109/BIBM58861.2023.10385603","url":null,"abstract":"<p><p>Pneumocystosis remains a life-threatening disease with a high mortality rate. It's critical to understand its clinical course and risk factors for better disease management. In this retrospective analysis, we aimed to elucidate the prognostic determinants of in-hospital mortality among patients diagnosed with pneumocystosis. Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database, encompassing all recorded cases of pneumocystosis. The dataset included patient admission records, comprehensive laboratory results, and medication administration data, which were meticulously analyzed to identify relevant features. Employing logistic regression and random forest, we discerned that the administration of micafungin sodium and vasopressin have significant impacts as risk factors on the survival rate of pneumocystosis patients.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"2751-2753"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12451645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ASD-GResTM: Deep Learning Framework for ASD classification using Gramian Angular Field.","authors":"Fahad Almuqhim, Fahad Saeed","doi":"10.1109/bibm58861.2023.10385743","DOIUrl":"10.1109/bibm58861.2023.10385743","url":null,"abstract":"<p><p>Autism Spectrum Disorder (ASD) is a heterogeneous disorder in children, and the current clinical diagnosis is accomplished using behavioral, cognitive, developmental, and language metrics. These clinical metrics can be imperfect measures as they are subject to high test-retest variability, and are influenced by assessment factors such as environment, social structure, or comorbid disorders. Advances in neuroimaging coupled with machine-learning provides an opportunity to develop methods that are more quantifiable, and reliable than existing clinical techniques. In this paper, we design and develop a deep-learning model that operates on functional magnetic resonance imaging (fMRI) data, and can classify between ASD and neurotypical brains. We introduce a novel strategy to transform time-series data extracted from fMRI signals into Gramian Angular Field (GAF) while locking in the temporal and spatial patterns in the data. Our motivation is to design and develop a novel framework that could encode the time-series, acquired from fMRI data, into images that can be used by deep-learning architectures that have been successful in computer vision. In our proposed framework called <i>ASD-GResTM</i>, we used a Convolutional Neural Network (CNN) to extract useful features from GAF images. We then used a Long Short-Term Memory (LSTM) layer to learn the activities between the regions. Finally, the output representations of the last LSTM layer are applied to a single-layer perceptron (SPL) to get the final classification. Our extensive experimentation demonstrates high accuracy across 4 centers, and outperforms state-of-the-art models on two centers with an increase in the accuracy of 17.58% and 6.7%, respectively as compared to the state of the art. Our model achieved the maximum accuracy of 81.78% with high degree of sensitivity and specificity. All training, validation, and testing was accomplished using openly available ABIDE-I benchmarking dataset.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"2837-2843"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11254319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}