Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing最新文献

{"title":"HALO: Hybrid Attention Model for Subcellular Localization.","authors":"Shafayat Ahmed, Nazifa Ahmed Moumi, Liqing Zhang","doi":"10.1142/9789819824755_0032","DOIUrl":"10.1142/9789819824755_0032","url":null,"abstract":"<p><p>Subcellular localization prediction is critical for understanding protein functions and interactions, providing insights into cellular mechanisms and potential therapeutic targets. We propose HALO (Hybrid Attention model for subcellular LOcalization), a framework that integrates semantic embeddings from fine-tuned protein language models (e.g., ESM) with structural information derived from AlphaFold. HALO uses a graph attention network (GAT) to incorporate biochemical, structural, and sequence-derived features into a unified representation, while dynamically balancing their contributions. Crucially, the design allows HALO to operate in two modes: (i) a sequence-only mode, where predictions are made from the fine-tuned protein language model (PLM) when structural data are unavailable, and (ii) a hybrid mode, where structural adjacency and biochemical features complement PLM predictions, especially in low-confidence regions. We evaluate HALO on multiple datasets with minimal homology between training and test sets, where it achieves competitive performance across key metrics. By flexibly combining sequence-based and structure-informed predictions, HALO addresses the limitations of relying on a single modality and offers an adaptable framework for accurate and generalizable subcellular localization.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"452-464"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the influence of demographic factors on the treatment of pediatric patients in the emergency department.","authors":"Helena Coggan, Anne Bischops, Pradip Chaudhari, Yuval Barak-Corren, Andrew M Fine, Ben Y Reis, Jaya Aysola, William G La Cava","doi":"10.1142/9789819824755_0040","DOIUrl":"10.1142/9789819824755_0040","url":null,"abstract":"<p><p>Persistent demographic disparities have been identified in the treatment of patients seeking care in the emergency department (ED). These may be driven in part by subconscious biases, which providers themselves may struggle to identify. To better understand the operation of these biases, we performed a retrospective cross-sectional analysis using electronic health records describing 339,400 visits to the ED of a single US pediatric medical center between 2019-2024. Odds ratios were calculated using propensity-score matching. Analyses were adjusted for confounding variables, including chief complaint, insurance type, socioeconomic deprivation, and patient comorbidities. We also trained a machine learning [ML] model on this dataset to identify predictors of admission. We found significant demographic disparities in admission (Non-Hispanic Black [NHB] relative to Non-Hispanic White [NHW]: OR 0.77, 95% CI 0.73-0.81; Hispanic relative to NHW: OR 0.80, 95% CI 0.76-0.83). We also identified disparities in individual decisions taken during the ED stay. For example, NHB patients were significantly less likely than NHW patients to be assigned an 'emergent' triage acuity score of (OR 0.70, 95% CI 0.67-0.72), but emergent NHB patients were also significantly less likely to be admitted than NHW patients with the same triage acuity (OR 0.86, 95% CI 0.80-0.93). Demographic disparities were particularly acute wherever patients had normal vital signs, public insurance, moderate socio-economic deprivation, or a home address distant from the hospital. An ML model assigned higher importance to triage score for NHB than NHW patients when predicting admission, reflecting these disparities in assignment. We conclude that many visit characteristics, clinical and otherwise, may influence the operation of subconscious biases and affect ML-driven decision support tools.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"551-565"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310686","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":"DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs.","authors":"Ananya Rajagopalan, Tram Anh Nguyen, Lindsay A Guare, Andre Luis Garao Rico, Rasika Venkatesh, Lannawill Caruth, Regeneron Genetics Center, Penn Medicine BioBank, Anurag Verma, Marylyn D Ritchie, Molly A Hall, Joseph D Romano, Shefali Setia-Verma","doi":"10.1142/9789819824755_0060","DOIUrl":"10.1142/9789819824755_0060","url":null,"abstract":"<p><p>Multi-omics data are instrumental in obtaining a comprehensive picture of complex biological systems. This is particularly useful for women's health conditions such as endometriosis, which has been historically understudied despite having a high prevalence (around 10% of women of reproductive age). Subsequently, endometriosis has limited genetic characterization: current genome-wide association studies explain only 11% of its 47% total estimated heritability, underscoring the need for integrative approaches. Graph representations provide an intuitive and meaningful way to harmonize biological data, using nodes to represent biological concepts (e.g., genes, single nucleotide polymorphisms, proteins, and phenotypes) and edges to represent their relationships. We present DRIVE-KG (Disease Risk Inference and Variant Exploration Knowledge Graph), which uses a heterogeneous graph representation to integrate data from diverse multi-omics datasets. We trained two distinct models using DRIVE-KG: a link prediction model to suggest associations between SNPs and two pilot phenotypes (endometriosis and obesity), and a graph convolutional network (GCN) for patient-level classification of endometriosis/adenomyosis as a combined phenotype. We conducted patient-level classification using data from 1,441 Penn Medicine BioBank participants with gold standard chart-reviewed endometriosis/adenomyosis status. The link prediction model uncovered 66 high-confidence (model score ≥ 0.95) candidate SNP-endometriosis associations, representing largely distinct genetic signals (R2 < 0.1). These variants were enriched for obesity/body mass index traits (24.2%), lipid metabolism (6%), and depressive disorders (4.5%), showing agreement with emerging hypotheses about endometriosis etiology. In contrast, of the high-confidence, candidate SNP-obesity associations that could be evaluated using LDlink, 38.22% were in high linkage disequilibrium (R2 ≥ 0.8) with known obesity or comorbidity associations. The GCN to classify patient endometriosis/adenomyosis status had an F1 score of 0.752 compared to 0.698 for a genetic risk score. Despite this moderate improvement, we found that the GCN learned meaningful stratification of underlying adenomyosis signal and severe endometriosis grades. Together, these results demonstrate that heterogeneous integration of multi-omics data is valuable for diverse downstream tasks-including discovery and clinical prediction-particularly for understudied diseases where traditional genomic approaches are insufficient.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"830-848"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310727","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":"Leveraging Large Language Models for Adverse Drug Event Detection: A Comparative Study of Token and Span-Based Named Entity Recognition.","authors":"Howard Prioleau, Saurav K Aryal, Jeremy Blackstone","doi":"10.1142/9789819824755_0015","DOIUrl":"10.1142/9789819824755_0015","url":null,"abstract":"<p><p>Adverse Drug Events (ADEs) pose a persistent threat to patient safety and public health. This study investigates the use of large language models (LLMs) fine-tuned for both token classification and span-based named entity recognition (NER) to improve ADE detection in clinical text. Using the 2018 n2c2 Track 2 dataset, we evaluate models under both predefined (gold label) and end-to-end settings. RoBERTa Large consistently outperforms other models, particularly in identifying ADEs, which remain more challenging due to their contextual ambiguity. Token-based models generally deliver stronger performance than span-based approaches, and ensemble methods, especially majority voting and XGBoost-based aggregation, further enhance end-to-end relation extraction by mitigating individual model weaknesses. These findings highlight the potential of fine-tuned LLMs, augmented by strategic ensembling, to advance clinical NLP pipelines and support safer, more personalized healthcare.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"205-218"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310847","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":"Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset.","authors":"Sumon Kanti Dey, Jeanne M Powell, Azra Ismail, Jeanmarie Perrone, Abeed Sarker","doi":"10.1142/9789819824755_0002","DOIUrl":"10.1142/9789819824755_0002","url":null,"abstract":"<p><p>Nonmedical opioid use is an urgent public health challenge, with far-reaching clinical and social consequences that are often underreported in traditional healthcare settings. Social media platforms, where individuals candidly share first-person experiences, offer a valuable yet underutilized source of insight into these impacts. In this study, we present a named entity recognition (NER) framework to extract two categories of self-reported consequences from social media narratives related to opioid use: ClinicalImpacts (e.g., withdrawal, depression) and SocialImpacts (e.g., job loss). To support this task, we introduce RedditImpacts 2.0, a high-quality dataset with refined annotation guidelines and a focus on first-person disclosures, addressing key limitations of prior work. We evaluate both fine-tuned encoderbased models and state-of-the-art large language models (LLMs) under zero- and few-shot in-context learning settings. Our fine-tuned DeBERTa-large model achieves a relaxed tokenlevel F1 of 0.61 [95% CI: 0.43-0.62], consistently outperforming LLMs in precision, span accuracy, and adherence to task-specific guidelines. Furthermore, we show that strong NER performance can be achieved with substantially less labeled data, emphasizing the feasibility of deploying robust models in resource-limited settings. Our findings underscore the value of domain-specific fine-tuning for clinical NLP tasks and contribute to the responsible development of AI tools that may enhance addiction surveillance, improve interpretability, and support real-world healthcare decision-making. The best performing model, however, still significantly underperforms compared to inter-expert agreement (Cohen's kappa: 0.81), demonstrating that a gap persists between expert intelligence and current state-of-the-art NER/AI capabilities for tasks requiring deep domain knowledge. The dataset, annotation guidelines, appendix, and training scripts are publicly available to support future research.**https://github.com/SumonKantiDey/Reddit_Impacts_NER.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"31 ","pages":"12-26"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310842","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":"Amyloid, Tau, and APOE in Alzheimer's Disease: Impact on White Matter Tracts.","authors":"Bramsh Qamar Chandio, Julio E Villalon-Reina, Talia M Nir, Sophia I Thomopoulos, Yixue Feng, Sebastian Benavidez, Neda Jahanshad, Jaroslaw Harezlak, Eleftherios Garyfallidis, Paul M Thompson","doi":"10.1142/9789819807024_0029","DOIUrl":"10.1142/9789819807024_0029","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (Aβ) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brain's neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populations in vivo. In this work, we applied BUAN (Bundle Analytics) tractometry to 3D diffusion MRI data from 730 participants in ADNI3 (phase 3 of the Alzheimer's Disease Neuroimaging Initiative; age range: 55-95 years, 349M/381F, 214 with mild cognitive impairment, 69 with AD, and 447 cognitively healthy controls). Using along-tract statistical analysis, we assessed the localized impact of amyloid, tau, and APOE genetic variants on the brain's neural pathways. BUAN quantifies microstructural properties of white matter tracts, supporting along-tract statistical analyses that identify factors associated with brain microstructure. We visualize the 3D profile of white matter tract associations with tau and amyloid burden in Alzheimer's disease; strong associations near the cortex may support models of disease propagation along neural pathways. Relative to the neutral genotype, APOE ϵ3/ϵ3, carriers of the AD-risk conferring APOE ϵ4 genotype show microstructural abnormalities, while carriers of the protective ϵ2 genotype also show subtle differences. Of all the microstructural metrics, mean diffusivity (MD) generally shows the strongest associations with AD pathology, followed by axial diffusivity (AxD) and radial diffusivity (RD), while fractional anisotropy (FA) is typically the least sensitive metric. Along-tract microstructural metrics are sensitive to tau and amyloid accumulation, showing the potential of diffusion MRI to track AD pathology and map its impact on neural pathways.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"394-411"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Evaluation of Antibiotic Prescribing Guideline Concordance in Pediatric Sinusitis Clinical Notes.","authors":"Davy Weissenbacher, Lauren Dutcher, Mickael Boustany, Leigh Cressman, Karen O'Connor, Keith W Hamilton, Jeffrey Gerber, Robert Grundmeier, Graciela Gonzalez-Hernandez","doi":"10.1142/9789819807024_0011","DOIUrl":"10.1142/9789819807024_0011","url":null,"abstract":"<p><strong>Background: </strong>Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their effectiveness and is a key focus of public health initiatives worldwide. In cases of sinusitis, among the most common reasons for antibiotic prescriptions in children, healthcare providers must distinguish between bacterial and viral causes based on clinical signs and symptoms. However, due to the overlap between symptoms of acute sinusitis and viral upper respiratory infections, antibiotics are often over-prescribed.</p><p><strong>Objectives: </strong>Currently, there are no electronic health record (EHR)-based methods, such as lab tests or ICD-10 codes, to retroactively assess the appropriateness of prescriptions for sinusitis, making manual chart reviews the only available method for evaluation, which is time-intensive and not feasible at a large scale. In this study, we propose using natural language processing to automate this assessment.</p><p><strong>Methods: </strong>We developed, trained, and evaluated generative models to classify the appropriateness of antibiotic prescriptions in 300 clinical notes from pediatric patients with sinusitis seen at a primary care practice in the Children's Hospital of Philadelphia network. We utilized standard prompt engineering techniques, including few-shot learning and chain-of-thought prompting, to refine an initial prompt. Additionally, we employed Parameter-Efficient Fine-Tuning to train a medium-sized generative model Llama 3 70B-instruct.</p><p><strong>Results: </strong>While parameter-efficient fine-tuning did not enhance performance, the combination of few-shot learning and chain-of-thought prompting proved beneficial. Our best results were achieved using the largest generative model publicly available to date, the Llama 3.1 405B-instruct. On our evaluation set, the model correctly identified 94.7% of the 152 notes where antibiotic prescription was appropriate and 66.2% of the 83 notes where it was not appropriate. However, 15 notes that were insufficiently, vaguely, or ambiguously documented by physicians posed a challenge to our model, as none were accurately classified.</p><p><strong>Conclusion: </strong>Our generative model demonstrated good performance in the challenging task of chart review. This level of performance may be sufficient for deploying the model within the EHR, where it can assist physicians in real-time to prescribe antibiotics in concordance with the guidelines, or for monitoring antibiotic stewardship on a large scale.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"138-153"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI in Point-of-Care - A Sustainable Healthcare Revolution at the Edge.","authors":"Yousuf Rajput, Tarek Tarif, Akira Wolfe, Eric Dawson, Keolu Fox","doi":"10.1142/9789819807024_0055","DOIUrl":"10.1142/9789819807024_0055","url":null,"abstract":"<p><p>This paper examines the integration of artificial intelligence (AI) in point-of-care testing (POCT) to enhance diagnostic speed, accuracy, and accessibility, particularly in underserved regions. AI-driven POCT is shown to optimize clinical decision-making, reduce diagnostic times, and offer personalized healthcare solutions, with applications in genome sequencing and infectious disease management. The paper highlights the environmental challenges of AI, including high energy consumption and electronic waste, and proposes solutions such as energy-efficient algorithms and edge computing. It also addresses ethical concerns, emphasizing the reduction of algorithmic bias and the need for equitable access to AI technologies. While AI in POCT can improve healthcare and promote sustainability, collaboration within the POCT ecosystem-among researchers, healthcare providers, and policymakers-is essential to overcome the ethical, environmental, and technological challenges.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"734-747"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequency of adding salt is a stronger predictor of chronic kidney disease in individuals with genetic risk.","authors":"Manu Shivakumar, Yanggyun Kim, Sang-Hyuk Jung, Jakob Woerner, Dokyoon Kim","doi":"10.1142/9789819807024_0039","DOIUrl":"10.1142/9789819807024_0039","url":null,"abstract":"<p><p>The incidence of chronic kidney disease (CKD) is increasing worldwide, but there is no specific treatment available. Therefore, understanding and controlling the risk factors for CKD are essential for preventing disease occurrence. Salt intake raises blood pressure by increasing fluid volume and contributes to the deterioration of kidney function by enhancing the renin-angiotensin system and sympathetic tone. Thus, a low-salt diet is important to reduce blood pressure and prevent kidney diseases. With recent advancements in genetic research, our understanding of the etiology and genetic background of CKD has deepened, enabling the identification of populations with a high genetic predisposition to CKD. It is thought that the impact of lifestyle or environmental factors on disease occurrence or prevention may vary based on genetic factors. This study aims to investigate whether frequency of adding salt has different effects depending on genetic risk for CKD. CKD polygenic risk scores (PRS) were generated using CKDGen Consortium GWAS (N= 765,348) summary statics. Then we applied the CKD PRS to UK Biobank subjects. A total of 331,318 European individuals aged 40-69 without CKD were enrolled in the study between 2006-2010. The average age at enrollment of the participants in this study was 56.69, and 46% were male. Over an average follow-up period of 8 years, 12,279 CKD cases were identified. The group that developed CKD had a higher percentage of individuals who added salt (46.37% vs. 43.04%) and higher CKD high-risk PRS values compared to the group that did not develop CKD (23.53% vs. 19.86%). We classified the individuals into four groups based on PRS: low (0-19%), intermediate (20-79%), high (80-94%), very high (≥ 95%). Incidence of CKD increased incrementally according to CKD PRS even after adjusting for age, sex, race, Townsend deprivation index, body mass index, estimated glomerular filtration rate, smoking, alcohol, physical activity, diabetes mellitus, dyslipidemia, hypertension, coronary artery diseases, cerebrovascular diseases at baseline. Compared to the \"never/rarely\" frequency of adding salt group, \"always\" frequency of adding salt group had an increasing incidence of CKD proportionate to the degree of frequency of adding salt. However, the significant association of \"always\" group on incident CKD disappeared in the low PRS group. This study validated the signal from PRSs for CKD across a large cohort and confirmed that frequency of adding salt contributes to the occurrence of CKD. Additionally, it confirmed that the effect of frequency of \"always\" adding salt on CKD incidence is greater in those with more than intermediate CKD-PRS. This study suggests that increased salt intake is particularly concerning for individuals with genetic risk factors for CKD, underscoring the clinical importance of reducing salt intake for these individuals.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"551-564"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819529","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":"Uncovering Important Diagnostic Features for Alzheimer's, Parkinson's and Other Dementias Using Interpretable Association Mining Methods.","authors":"Kazi Noshin, Mary Regina Boland, Bojian Hou, Victoria Lu, Carol Manning, Li Shen, Aidong Zhang","doi":"10.1142/9789819807024_0045","DOIUrl":"10.1142/9789819807024_0045","url":null,"abstract":"<p><p>Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. The majority of research in ADRD is conducted using post-mortem samples of brain tissue or carefully recruited clinical trial patients. While these resources are excellent, they suffer from lack of sex/gender, and racial/ethnic inclusiveness. Electronic Health Records (EHR) data has the potential to bridge this gap by including real-world ADRD patients treated during routine clinical care. In this study, we utilize EHR data from a cohort of 70,420 ADRD patients diagnosed and treated at Penn Medicine. Our goal is to uncover important risk features leading to three types of Neuro-Degenerative Disorders (NDD), including Alzheimer's Disease (AD), Parkinson's Disease (PD) and Other Dementias (OD). We employ a variety of Machine Learning (ML) Methods, including uni-variate and multivariate ML approaches and compare accuracies across the ML methods. We also investigate the types of features identified by each method, the overlapping features and the unique features to highlight important advantages and disadvantages of each approach specific for certain NDD types. Our study is important for those interested in studying ADRD and NDD in EHRs as it highlights the strengths and limitations of popular approaches employed in the ML community. We found that the uni-variate approach was able to uncover features that were important and rare for specific types of NDD (AD, PD, OD), which is important from a clinical perspective. Features that were found across all methods represent features that are the most robust.</p>","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing","volume":"30 ","pages":"631-646"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}