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The classical iterative HHL-based hemodynamic simulation quantum linear equation algorithm for abdominal aortic aneurysm

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The European Physical Journal Special Topics 2024年 1-11页

作者： You, Yujie Zhou, Fei Yue, Yimin Qiu, Yue Wang, Xinhang Yu, Yangchao Li, Bing Li, Ran Zhang, Le College of Computer Science Sichuan University Chengdu China Jinan Institute of Quantum Technology Jinan China Jinan Branch Hefei National Laboratory Jinan China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu People’s Republic of China Med-X Center for Informatics Sichuan University Chengdu People’s Republic of China West China Hospital- SenseTime Joint Lab Chengdu People’s Republic of China Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

The abdominal aortic aneurysm (AAA) rupture is an extremely dangerous surgical acute abdominal disease. An important preventive method for AAA rupture is to simulate the hemodynamic parameters of AAA for medical evaluation through computational fluid dynamics. To fill the gap between the application of quantum computing and the field of biomedical engineering, we report a classical iterative HHL-based quantum algorithm for AAA hemodynamic simulation, and provide a Hamiltonian simulation approach for the coefficient matrix of linearized one-dimensional hemodynamic equations. Simulation experiments on IBM’s Qiskit (virtual machine) show that our proposed algorithm cannot only significantly reduce quantum resource consumption, but also improve simulation accuracy compared with the HHL algorithm. In addition, we have established a search-based circuit mapping which considers the hardware structure of the ‘Xiaohong’ quantum computer and selects mapping circuits with lower noise to improve the accuracy of measured results.

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Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias

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The journal of prevention of Alzheimer's disease 2025年 100169页

作者： Sonia Akter Zhandi Liu Eduardo J Simoes Praveen Rao Institute for Data Science and Informatics University of Missouri USA. Department of Electrical Engineering and Computer Science University of Missouri USA. Department of Biomedical Informatics Biostatics and Medical Epidemiology University of Missouri USA. Institute for Data Science and Informatics University of Missouri USA Department of Electrical Engineering and Computer Science University of Missouri USA. Electronic address: praveen.rao@missouri.edu.

BACKGROUND:Over 6 million patients in the United States are affected by Alzheimer's Disease and Related Dementias (ADRD). Early detection of ADRD can significantly improve patient outcomes through timely treatment. OBJECTIVE:To develop and validate machine learning (ML) models for early ADRD diagnosis and prediction using de-identified EHR data from the University of Missouri (MU) Healthcare. DESIGN:Retrospective case-control study. SETTING:The study used de-identified EHR data provided by the MU NextGen biomedical informatics, modeled with the PCORnet Common data Model (CDM). PARTICIPANTS:An initial cohort of 380,269 patients aged 40 or older with at least two healthcare encounters was narrowed to a final dataset of 4,012 ADRD cases and 119,723 controls. METHODS:Six ML classifier models: Gradient-Boosted Trees (GBT), Light Gradient-Boosting Machine (LightGBM), Random Forest (RF), eXtreme Gradient-Boosting (XGBoost), Logistic Regression (LR), and Adaptive Boosting (AdaBoost) were evaluated using Area Under the Receiver Operating Characteristic Curve (AUC-ROC), accuracy, sensitivity, specificity, and F1 score. SHAP (SHapley Additive exPlanations) analysis was applied to interpret predictions. RESULTS:The GBT model achieved the best AUC-ROC scores of 0.809-0.833 across 1- to 5-year prediction windows. SHAP analysis identified depressive disorder, age groups 80-90 yrs and 70-80 yrs, heart disease, anxiety, and the novel risk factors of sleep apnea, and headache. CONCLUSION:This study underscores the potential of ML models for leveraging EHR data to enable early ADRD prediction, supporting timely interventions, and improving patient outcomes. By identifying both established and novel risk factors, these findings offer new opportunities for personalized screening and management strategies, advancing both clinical and informatics science.

关键词： Alzheimer's disease Dementias Early prediction Electronic health record data Machine learning (ML)

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The Occurrence and Genetic Characteristics of Carbapenemase-Producing Enterobacteriaceae Isolated from Shared Water Bodies for Livestock and Human Utilization in Central Thailand

SSRN

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SSRN 2024年

作者： Songsaeng, Wipawee Kurilung, Alongkorn Prapasarakul, Nuvee Wongsurawat, Thidathip Am-In, Nutthee Lugsomya, Kittitat Sirichokchatchawan, Wandee College of Public Health Science Chulalongkorn University Bangkok Thailand Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR United States Department of Veterinary Microbiology Faculty of Veterinary Science Chulalongkorn University Bangkok Thailand Bangkok Thailand Division of Bioinformatics and Data Management for Research Research Group and Research Network Division Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Department of Obstetrics Gynecology and Reproduction Faculty of Veterinary Science Chulalongkorn University Bangkok Thailand Veterinary and Life Science Murdoch University MurdochWA6150 Australia

The environmental dissemination of antimicrobial resistant Enterobacteriaceae poses a remarkable threat to public health. This study investigates the environmental presence and dissemination of carbapenemase-producing Enterobacteriaceae (CPE) in 30 important water bodies selected according to the interconnection and utilization by livestock and community people in central Thailand. From all selected samples, 6 isolates (20%) were identified as multi-drug resistant CPE including one E. coli, one K. pneumoniae and four E. roggemkampii carrying blaNDM-5, blaNDM-1 and blaIMI-1 genes respectively. The blaNDM-5 and blaNDM-1 genes were located on plasmids, p0111 and IncC, while blaIMI-1 was located on chromosomes. The plasmids also consisted of components that closely resembled those found in resistance plasmids obtained from clinical and environmental isolates worldwide. Additionally, through plasmid conjugation experiment, carbapenemase genes were transferable with high rate among bacterial species. These findings indicated that water bodies are polluted and there is an urgent need for integrated strategies to monitor and mitigate the spread of antibiotic resistance across human, animal, and environmental health domains in aquatic environments. © 2024, The Authors. All rights reserved.

关键词： Genes

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Devider: Long-Read Reconstruction of Many Diverse Haplotypes 29th

Devider: Long-Read Reconstruction of Many Diverse Haplotype...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Shaw, Jim Boucher, Christina Yu, Yun William Noyes, Noelle Li, Heng Department of Data Science Dana-Farber Cancer Institute BostonMA02215 United States Department of Biomedical Informatics Harvard Medical School BostonMA02215 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Ray and Stephanie Lane Computational Biology Department Carnegie Mellon University PittsburghPA15213 United States Department of Veterinary Population Medicine University of Minnesota St. PaulMN55421 United States

ISBN: (纸本)9783031902512

Reconstructing haplotypes through sequencing of a mixture of similar sequences is a fundamental problem. Long-read sequencing technologies can connect distant alleles to disentangle similar haplotypes, but handling elevated sequencing error rates requires specialized techniques. We present devider, an algorithm for haplotyping small sequences—such as viruses or genes—from long-read sequencing. devider uses a positional de Bruijn graph with sequence-to-graph alignment on an alphabet of informative alleles to provide a fast assembly-inspired approach compatible with various long-read sequencing technologies. Benchmarking on synthetic mixtures of antimicrobial resistance (AMR) genes showed that devider recovered 83% of haplotypes, 23% points higher than the next best method. On real PacBio and Nanopore datasets, devider recapitulates previously known results in seconds, disentangling a bacterial community with >10 strains and an HIV-1 co-infection dataset. We used devider to investigate the within-host diversity of a long-read bovine gut metagenome enriched for AMR genes, discovering a history of recombination for diverse AMR gene haplotypes and showcasing devider ’s ability to unveil ecological signals for heterogeneous mixtures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mammals

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RePhine: An Integrative Method for Identification of Drug Response-related Transcriptional Regulators

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Genomics, Proteomics & Bioinformatics 2021年第4期19卷 534-548页

作者： Xujun Wang Zhengtao Zhang Wenyi Qin Shiyi Liu Cong Liu Georgi ZGenchev Lijian Hui Hongyu Zhao Hui Lu SJTU-Yale Joint Center for Biostatistics and Data Science Department of Bioinformatics and BiostatisticsSchool of Life Science and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Cell Biology CAS Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyShanghai 200031China Department of Bioengineering University of Illinois at ChicagoIL 60607USA Department of Genetics School of MedicineYale UniversityNew HavenCT 06511USA Department of Medical Informatics Columbia UniversityNew YorkNY 10032USA Bulgarian Institute for Genomics and Precision Medicine Sofia 1000Bulgaria Department of Biostatistics Yale School of Public Health.New HavenCT 06511USA Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China Center for Biomedical informatics Shanghai Children’s HospitalShanghai 200040China

Transcriptional regulators(TRs)participate in essential processes in cancer pathogenesis and are critical therapeutic *** of drug response-related TRs from cell line-based compound screening data is often challenging due to low m RNA abundance of TRs,protein modifications,and other confounders(CFs).In this study,we developed a regression-based pharmacogenomic and Ch IP-seq data integration method(Re Phine)to infer the impact of TRs on drug response through integrative analyses of pharmacogenomic and Ch IP-seq *** Phine was evaluated in simulation and pharmacogenomic data and was applied to pan-cancer datasets with the goal of biological *** simulation data with added noises or CFs and in pharmacogenomic data,Re Phine demonstrated an improved performance in comparison with three commonly used methods(including Pearson correlation analysis,logistic regression model,and gene set enrichment analysis).Utilizing Re Phine and Cancer Cell Line Encyclopedia data,we observed that Re Phinederived TR signatures could effectively cluster drugs with different mechanisms of *** Phine predicted that loss-offunction of EZH2/PRC2 reduces cancer cell sensitivity toward the BRAF inhibitor *** validation confirmed that pharmacological EZH2 inhibition increases the resistance of cancer cells to PLX4720 *** results support that Re Phine is a useful tool for inferring drug response-related TRs and for potential therapeutic *** source code for Re Phine is freely available at https://***/coexps/Re Phine.

关键词： Pharmacogenomics ChIP-seq Transcriptional regulator BRAF inhibitor resistance Drug resistance

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Geometric Deep Learning for Structure-Based Drug Design: A Survey

arXiv

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arXiv 2023年

作者： Zhang, Zaixi Yan, Jiaxian Huang, Yining Liu, Qi Chen, Enhong Wang, Mengdi Zitnik, Marinka Princeton University United States School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China State Key Laboratory of Cognitive Intelligence Anhui Hefei230088 China Harvard University United States the Department of Biomedical Informatics Harvard Medical School Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Harvard Data Science Initiative United States Broad Institute of MIT and Harvard United States

Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Traditional approaches, rooted in physicochemical modeling and domain expertise, are often resource-intensive. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, alongside breakthroughs in accurate protein structure predictions from tools like AlphaFold, have significantly propelled the field forward. This paper systematically reviews the state-of-the-art in geometric deep learning for SBDD. We begin by outlining foundational tasks in SBDD, discussing prevalent 3D protein representations, and highlighting representative predictive and generative models. Next, we provide an in-depth review of key tasks, including binding site prediction, binding pose generation, de novo molecule generation, linker design, protein pocket generation, and binding affinity prediction. For each task, we present formal problem definitions, key methods, datasets, evaluation metrics, and performance benchmarks. Lastly, we explore current challenges and future opportunities in SBDD. Challenges include oversimplified problem formulations, limited out-of-distribution generalization, biosecurity concerns related to the misuse of structural data, insufficient evaluation metrics and large-scale benchmarks, and the need for experimental validation and enhanced model interpretability. Opportunities lie in leveraging multimodal datasets, integrating domain knowledge, developing comprehensive benchmarks, establishing criteria aligned with clinical outcomes, and designing foundation models to expand the scope of design tasks. We also curate https://***/zaixizhang/Awesome-SBDD, reflecting ongoing contributions and new datasets in SBDD. © 2023, CC BY-NC-SA.

关键词： Molecular docking

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CAUSAL RULE ENSEMBLE APPROACH FOR MULTI-ARM data

arXiv

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arXiv 2025年

作者： Wan, Ke Tanioka, Kensuke Shimokawa, Toshio Department of Medical Data Science Wakayama Medical University Japan Department of Biomedical Sciences and Informatics Doshisha University Japan Department of Biostatistics Wakayama Medical University Japan

Heterogeneous treatment effect (HTE) estimation is critical in medical research. It provides insights into how treatment effects vary among individuals, which can provide statistical evidence for precision medicine. While most existing methods focus on binary treatment situations, real-world applications often involve multiple interventions. However, current HTE estimation methods are primarily designed for binary comparisons and often rely on black-box models, which limit their applicability and interpretability in multi-arm settings. To address these challenges, we propose an interpretable machine learning framework for HTE estimation in multi-arm trials. Our method employs a rule-based ensemble approach consisting of rule generation, rule ensemble, and HTE estimation, ensuring both predictive accuracy and interpretability. Through extensive simulation studies and real data applications, the performance of our method was evaluated against state-of-the-art multi-arm HTE estimation approaches. The results indicate that our approach achieved lower bias and higher estimation accuracy compared with those of existing methods. Furthermore, the interpretability of our framework allows clearer insights into how covariates influence treatment effects, facilitating clinical decision making. By bridging the gap between accuracy and interpretability, our study contributes a valuable tool for multi-arm HTE estimation, supporting precision medicine. Copyright © 2025, The Authors. All rights reserved.

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A needle for Alzheimer's in a haystack of claims data (Vol 2, Pg 90, 2022)

NATURE AGING

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NATURE AGING 2022年第1期2卷 90-90页

作者： Guney, Emre Athie, Alejandro Research Programme on Biomedical Informatics Hospital del Mar Medical Research Institute (IMIM) Pompeu Fabra University (UPF) Barcelona Spain Discovery and Data Science (DDS) Unit STALICLA R&D SL Barcelona Spain Accenture Barcelona Spain

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When accurate prediction models yield harmful self-fulfilling prophecies

arXiv

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arXiv 2023年

作者： van Amsterdam, Wouter A.C. van Geloven, Nan Krijthe, Jesse H. Ranganath, Rajesh Cinà, Giovanni Department of Data Science and Biostatistics Julius Center of Health Sciences Primary Care University Medical Center Utrecht Heidelberglaan 100 Utrecht3584 CX Netherlands University of Utrecht Heidelberglaan 100 Utrecht3584 CX Netherlands Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Pattern Recognition & Bioinformatics Delft University of Technology Delft Netherlands Courant Institute of Mathematical Science Department of Computer Science Center for Data Science New York University New York City United States Department of Medical Informatics Amsterdam University Medical Center Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands

Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisions, and are often hailed as the poster children for personalized, data-driven healthcare. We show however, that using prediction models for decision making can lead to harmful decisions, even when the predictions exhibit good discrimination after deployment. These models are harmful self-fulfilling prophecies: their deployment harms a group of patients but the worse outcome of these patients does not invalidate the predictive power of the model. Our main result is a formal characterization of a set of such prediction models. Next we show that models that are well calibrated before and after deployment are useless for decision making as they made no change in the data distribution. These results point to the need to revise standard practices for validation, deployment and evaluation of prediction models that are used in medical decisions. © 2023, CC BY.

关键词： Prediction models

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia Computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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