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Reply to comment on “Estimating individual risk of catheter-associated urinary tract infections using explainable artificial intelligence on clinical data”

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American Journal of Infection Control 2025年第7期53卷 802-803页

作者： Herdiantri Sufriyana Chieh Chen Hua-Sheng Chiu Pavel Sumazin Po-Yu Yang Jiunn-Horng Kang Emily Chia-Yu Su Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Texas Children's Cancer Center Baylor College of Medicine Houston TX USA School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University Taipei Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan

SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials

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informatics in Medicine Unlocked 2024年 50卷 101589页

作者： Lee, Kyeryoung Paek, Hunki Huang, Liang-Chin Hilton, C Beau Datta, Surabhi Higashi, Josh Ofoegbu, Nneka Wang, Jingqi Rubinstein, Samuel M. Cowan, Andrew J. Kwok, Mary Warner, Jeremy L. Xu, Hua Wang, Xiaoyan IMO Health Rosemont IL United States Division of Hematology and Oncology Vanderbilt University Nashville TN United States Division of Hematology University of North Carolina Chapel Hill NC United States Division of Hematology and Oncology University of Washington Seattle WA United States Lifespan Cancer Institute Rhode Island Hospital Providence RI United States Center for Clinical Cancer Informatics and Data Science Legorreta Cancer Center Brown University Providence RI United States Biomedical Informatics and Data Science Yale University New Haven CT United States

Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making. Methods: We collected clinical trial abstracts from key conferences and PubMed (2012–2023). The SEETrials system was developed with three modules: preprocessing, prompt engineering with knowledge ingestion, and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types— multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies. Results: SEETrials achieved high precision (0.964), recall (sensitivity) (0.988), and F1 score (0.974) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.979–0.992 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88 %, 95 % CI: 84–92 %;CR: 95 %, 95 % CI: 53–66 %), bispecific antibodies (ORR: 64 %, 95 % CI: 55–73 %;CR: 27 %, 95 % CI: 16–37 %), and ADC (ORR: 51 %, 95 % CI: 37–65 %;CR: 26 %, 95 % CI: 1–51 %). Notable study heterogeneity was identified (>75 % I² heterogeneity index scores) across several outcome entities analyzed within therapy subgroups. Conclusion: SEETrials demonstrated highly accurate data extraction and

关键词： Automated safety and efficacy extraction Conference abstracts GPT-4 Large language models Large scale analysis Oncology clinical trial

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

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Classification of Breast Thermal Images into Healthy/Cancer ...

2022 International Conference on Machine Learning and data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 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 Tamil Nadu Chennai602105 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. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation

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arXiv 2024年

作者： Tang, Xiangru Dai, Howard Knight, Elizabeth Wu, Fang Li, Yunyang Li, Tianxiao Gerstein, Mark Department of Computer Science Yale University New HavenCT06520 United States School of Medicine Yale University New HavenCT06520 United States Computer Science Department Stanford University CA94305 United States Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Department of Statistics & Data Science Yale University New HavenCT06520 United States Department of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://***/gersteinlab/GenAI4Drug. © 2024, CC BY.

关键词： Proteins

Single-cell technologies: current and near future

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science China(Life sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

On the Convergence of Optimizing Persistent-Homology-Based Losses

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arXiv 2022年

作者： Zhang, Yikai Yao, Jiachen Wang, Yusu Chen, Chao ML Research Morgan Stanley United States Department of Computer Science Stony Brook University United States Halicioǧlu Data Science Institute University of California San Diego United States Department of Biomedical Informatics Stony Brook University United States

Topological loss based on persistent homology has shown promise in various applications. A topological loss enforces the model to achieve certain desired topological property. Despite its empirical success, less is known about the optimization behavior of the loss. In fact, the topological loss involves combinatorial configurations that may oscillate during optimization. In this paper, we introduce a general purpose regularized topology-aware loss. We propose a novel regularization term and also modify existing topological loss. These contributions lead to a new loss function that not only enforces the model to have desired topological behavior, but also achieves satisfying convergence behavior. Our main theoretical result guarantees that the loss can be optimized efficiently, under mild assumptions. © 2022, CC BY-NC-ND.

关键词： Topology

DNABERT-S: PIONEERING SPECIES DIFFERENTIATION WITH SPECIES-AWARE DNA EMBEDDINGS

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arXiv 2024年

作者： Zhou, Zhihan Wu, Weimin Ho, Harrison Wang, Jiayi Shi, Lizhen Davuluri, Ramana V. Wang, Zhong Liu, Han Department of Computer Science Northwestern University United States Department of Statistics and Data Science Northwestern University United States School of Natural Sciences University of California at Merced United States Department of Biomedical Informatics Stony Brook University United States Department of Energy Joint Genome Institute Lawrence Berkeley National Laboratory United States Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory United States

We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e., DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trained genome foundation model named DNABERT-2. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C2LR) strategy. Empirical results on 23 diverse datasets show DNABERT-S's effectiveness, especially in realistic label-scarce scenarios. For example, it identifies twice more species from a mixture of unlabeled genomic sequences, doubles the Adjusted Rand Index (ARI) in species clustering, and outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training. Model, codes, and data is publicly available at https://***/MAGICS-LAB/DNABERT_S. © 2024, CC BY.

关键词： Genome

Leveraging Undecided Cases in Chart-Reviewed Phenotypes to Enhance Ehr-Based Association Studies

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SSRN

SSRN 2024年

作者： Jian, Xinyao Zhang, Dazheng Yu, Zehao Xu, Hua Bian, Jiang Wu, Yonghui Tong, Jiayi Chen, Yong University of Pennsylvania Perelman School of Medicine PhiladelphiaPA United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States Cancer Informatics Shared Resource University of Florida Health Cancer Center GainesvilleFL United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States The Graduate Group in Applied Mathematics and Computational Science School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Leonard Davis Institute of Health Economics PhiladelphiaPA United States PhiladelphiaPA United States PhiladelphiaPA United States

Objectives: In electronic health record (EHR)-based association studies, phenotyping algorithms efficiently classify patient clinical outcomes into binary categories but are susceptible to misclassification errors. The gold standard, manual chart review, involves clinicians determining the true disease status based on their assessment of health records. These clinicians-labeled phenotypes are labor-intensive and typically limited to a small subset of patients, potentially introducing a third "undecided" category when phenotypes are indeterminate. We aim to effectively integrate the algorithm-derived and chart-reviewed outcomes when both are available in EHR-based association *** and Methods: We propose an augmented estimation method that combines the binary algorithm-derived phenotypes for the entire cohort with the trinary chart-reviewed phenotypes for a small, selected subset. Additionally, a cost-effective outcome-dependent sampling strategy is used to address the rare disease scenarios. The proposed trinary chart-reviewed phenotype integrated cost-effective augmented estimation (TriCA) was evaluated through a wide range of simulation settings and real-world applications in Alzheimer's disease and related dementias using EHR data from the OneFlorida+ Clinical Research ***: Compared to estimation based on random sampling, our augmented method improved mean square error by up to 43.5% in simulation studies;compared to estimation using trinary chart-reviewed phenotypes only, our method improved efficiency by up to 32.1% in the real-world data ***: Our simulation studies and real-world application demonstrate that, compared to existing methods, the proposed method provides unbiased estimates with higher statistical ***: The proposed method effectively combined binary algorithm-derived phenotypes for the whole cohort with trinary chart-reviewed outcomes for a limited validation set, making it applicable to

关键词： Electronic health record

MultiNet with Transformers: A Model for Cancer Diagnosis Using Images

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arXiv 2023年

作者： Barzekar, Hosein Patel, Yash Tong, Ling Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Health Informatics and Administration University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Biomedical Engineering University of Wisconsin-Milwaukee MilwaukeeWI53211 United States

Cancer is a leading cause of death in many countries. An early diagnosis of cancer based on biomedical imaging ensures effective treatment and a better prognosis. However, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial cost. The cost makes large-scale biological image classification impractical. In this study, we provide unique deep neural network designs for multiclass classification of medical images, in particular cancer images. We incorporated transformers into a multiclass framework to take advantage of data-gathering capability and perform more accurate classifications. We evaluated models on publicly accessible datasets using various measures to ensure the reliability of the models. Extensive assessment metrics suggest this method can be used for a multitude of classification tasks. © 2023, CC BY.

关键词： Computer aided diagnosis