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Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides

ACS Chemical Biology 2023年第3期18卷 476-483页

作者： Jian, Bo-Siyuan Chiou, Shao-Lun Hsu, Chun-Chia Ho, Josh Wu, Yu-Wei Chu, John Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Chemistry National Taiwan University Taipei 10617 Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 10675 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei 10675 Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei 10675 Taiwan

The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms. © 2023 The Authors. Published by American Chemical Society.

关键词： Algorithms Bacteria Bioinformatics and computational biology Peptides and proteins Pharmaceuticals

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Effects of Tirzepatide in Type 2 Diabetes: Individual Variation and Relationship to Cardiometabolic Outcomes

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Journal of the American College of Cardiology 2025年第19期85卷 1858-1872页

作者： Aminorroaya, Arya Oikonomou, Evangelos K. Biswas, Dhruva Jastreboff, Ania M. Khera, Rohan Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New Haven CT United States Cardiovascular Data Science (CarDS) Lab Yale School of Medicine New Haven CT United States Section of Endocrinology and Metabolism Department of Medicine Yale School of Medicine New Haven CT United States Section of Pediatric Endocrinology Department of Pediatrics Yale School of Medicine New Haven CT United States Yale Obesity Research Center (Y-Weight) Yale School of Medicine New Haven CT United States Center for Outcomes Research and Evaluation (CORE) Yale New Haven Hospital New Haven CT United States Section of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Section of Health Informatics Department of Biostatistics Yale School of Public Health New Haven CT United States

Background: Tirzepatide—a dual GIP/GLP-1 receptor agonist—exerts pleiotropic effects on cardiometabolic health. Objectives: The authors sought to investigate the efficacy of tirzepatide in improving different cardiometabolic risk factors across individuals and subpopulations. Methods: Using an independent, global data-sharing and analytics platform, we performed an individual participant data meta-analysis by pooling data from 7 Phase 3 randomized clinical trials that compared tirzepatide with placebo or standard antihyperglycemic agents in individuals with type 2 diabetes. The study outcomes were the presence of a range of cardiometabolic abnormalities, representing components of metabolic syndrome (MetS) (elevated waist circumference, triglycerides, blood pressure, and fasting blood glucose, and decreased high-density lipoprotein cholesterol), as well as elevated body mass index and MetS (≥3 cardiometabolic abnormalities). Outcomes were modeled using mixed-effects models, with inverse probability weighting to account for study design differences. Results: We included 7,805 participants with a weighted median age of 59 years (Q1-Q3: 51-66 years) and 43.2% women. Over a weighted median treatment duration of 41.0 weeks, tirzepatide reduced the odds of all cardiometabolic abnormalities, ranging from 34% reduction for the odds of decreased high-density lipoprotein cholesterol (OR: 0.66 [95% CI: 0.52-0.84]) to 96% reduction in the odds of elevated body mass index (OR: 0.04 [95% CI:0.02-0.08]), and 72% reduction for the odds of MetS (OR: 0.28 [95% CI: 0.24-0.33]). Tirzepatide's superior efficacy in resolving MetS was consistent across demographic and clinical subpopulations, with higher efficacy in age <65 years vs ≥65 years, and in individuals without vs with baseline use of sodium-glucose cotransporter 2 inhibitors (P for interaction = 0.008 and 0.009, respectively). Conclusions: This pooled analysis suggests that tirzepatide may improve cardiometabolic abnormalities a

关键词： diabetes mellitus type 2 heart disease risk factors meta-analysis randomized controlled trial tirzepatide

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Building an i2b2-Based Population Repository for COVID-19 Research 32

Building an i2b2-Based Population Repository for COVID-19 Re...

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32nd Medical informatics Europe Conference, MIE 2022

作者： Pedrera-Jimenez, Miguel Garcia-Barrio, Noelia Hernandez-Ibarburu, Gema Baselga, Blanca Blanco, Alvar Calvo-Boyero, Fernando Gutierrez-Sacristan, Alba Quiros, Víctor Cruz-Bermudez, Juan Luis Bernal, José Luis Meloni, Laura Perez-Rey, David Palchuk, Matvey Kohane, Isaac Serrano, Pablo Data Science Group Hospital Universitario 12 de Octubre Av. de Andalucía S/N Madrid28041 Spain Etsi Telecomunicación Universidad Politécnica de Madrid Madrid Spain Biomedical Informatics Group Universidad Politécnica de Madrid Madrid Spain Department of Biomedical Informatics Harvard Medical School BostonMA United States TriNetX Llc CambridgeMA United States

ISBN: (纸本)9781643682846

Reuse of Electronic Health Records (EHRs) for specific diseases such as COVID-19 requires data to be recorded and persisted according to international standards. Since the beginning of the COVID-19 pandemic, Hospital Universitario 12 de Octubre (H12O) evolved its EHRs: it identified, modeled and standardized the concepts related to this new disease in an agile, flexible and staged way. Thus, data from more than 200,000 COVID-19 cases were extracted, transformed, and loaded into an i2b2 repository. This effort allowed H12O to share data with worldwide networks such as the TriNetX platform and the 4CE Consortium. © 2022 European Federation for Medical informatics (EFMI) and IOS Press.

关键词： Semantics

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A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Model for Classifying DNA Barcodes

A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Mo...

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Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), International Conference on

作者： Abhivyakti Yadav Divya S I R Oviya Kalpana Raja Department of Computer Science and Engineering (Artificial Intelligence) Amrita School of Computing Amrita Vishwa Vidyapetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization.

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T2 Coach: A Qualitative Study of an Automated Health Coach for Diabetes Self-Management 25

T2 Coach: A Qualitative Study of an Automated Health Coach f...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Mitchell, Elliot G Desai, Pooja Smaldone, Arlene Cassells, Andrea Tobin, Jonathan N. Albers, David Levine, Matthew Mamykina, Lena Geisinger Health Danville PA United States Department of Biomedical Informatics Columbia University Irving Medical Center New York NY United States College of Dental Medicine Columbia University New York NY United States School of Nursing Columbia University New York NY United States Clinical Directors Network New York NY United States Center for Clinical and Translational Science The Rockefeller University New York NY United States Department of Pediatrics Section of Informatics and Data Science University of Colorado Anschutz Medical Center Aurora CO United States Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge MA United States Department of Biomedical Informatics Columbia University New York NY United States

ISBN: (纸本)9798400713941

Computational intelligence is increasingly common in interactive systems in many domains, including health. Health coaching with conversational agents (CA) can reach wide populations, but the level of computational intelligence needed for a positive coaching experience is unclear. We conducted a study with sixteen individuals with diabetes and prediabetes who used a CA for health coaching, T2 Coach. Qualitative interviews revealed that participants saw T2 Coach as reliable in helping them stay on track with self-management, appreciated the flexibility in choosing personally meaningful goals and engaging on their own terms, and felt it provided encouragement and even compared it favorably with human coaches. However, they also noted that coaching experience could be improved with more fluid conversations, more tailoring to their personal preferences and lifestyles, and more sensitivity to specific contexts, all of which require more computational intelligence. We discuss implications and design directions for more intelligent coaching CA in health. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： chatbots coaching conversational agents diabetes Health mHealth self-management self-tracking

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Physical activity exercise behavior in individuals with a history of stroke: a retrospective study

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Discover Medicine 2025年第1期2卷 1-7页

作者： Arora, Shiv Ayyathan, Smriti Sudhir Burzynski, Emily Phillips, Vidith Department of Neurology Sardar Patel Medical College Bikaner India Department of Neurology Brihanmumbai Municipal Corporation (BMC) Mumbai India Department of Neurology Medical University of Warsaw Warsaw Poland Department of Internal Medicine Division of Biomedical Informatics and Data Science School of Medicine Johns Hopkins University Baltimore USA

Stroke remains a significant public health challenge in the United States, impacting the physical capabilities and quality of life of affected individuals. This study assesses the prevalence of self-reported physical activity among stroke patients in relation to demographic, socioeconomic, and healthcare access factors. To examine the levels of physical activity among individuals with a history of stroke and identify the variables influencing these levels. Utilizing the Behavioral Risk Factor Surveillance System (BRFSS) database for 2021, this retrospective study analyzed responses from participants identified with a stroke history. data were assessed using the BRFSS Web Enabled Analysis Tool, focusing on self-reported physical activity and including variables such as age, gender, race, education level, employment status, income, and timing of last routine checkup. Of 17,158 respondents reporting a stroke history, 58.5% engaged in physical activity. Physical activity rates were higher among younger individuals, males, and those with higher socioeconomic status. Physical activity in past one month is significantly associated with a lower prevalence of stroke in age groups 25–44 years, 45–64 years, and 65 + years. Younger individuals (18–24 years) do not show a significant association between physical activity and stroke incidence. While a considerable percentage of stroke patients reported engaging in physical activity, disparities exist influenced by age, gender, and socioeconomic factors. These findings highlight the need for targeted interventions to promote physical activities among stroke survivors, particularly those in lower socioeconomic groups or older age brackets.

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Unsupervised Characterization of Temporal dataset Shifts as Early Indicator of AI Performance Variations

SSRN

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SSRN 2025年

作者： Fernández-Narro, David Ferri, Pablo Gutiérrez-Sacristán, Alba García-Gómez, Juan M. Sáez, Carlos Biomedical Data Science Lab Instituto Universitario de Tecnologías de la Información y Comunicaciones Universitat Politècnica de Valéncia Camino de Vera s/n Valencia46022 Spain Department of Biomedical Informatics Harvard Medical School BostonMA United States

Background and Objective:Temporal dataset shifts, characterized by evolving data distributions over time, pose significant challenges to the reliability and effectiveness of AI models in medicine. Leveraging long-term data from electronic health records (EHRs) is essential for accurate predictions in healthcare. However, intrinsic changes in health data over time can compromise model performance, leading to outdated or biased decisions. This study aims to assess the association between unsupervised temporal dataset shifts characterization, i.e. using the original data while being model-agnostic, and AI model performance to provide early indicators of potential variations before AI model ***:Using the MIMIC-IV EHR database, encompassing data from over 40,000 patients from 2008 to 2019, we characterized its inherent dataset shift patterns through an unsupervised approach based on information-geometry. We developed and evaluated Random Forests and Gradient Boosting models annually to predict in-hospital mortality, allowing us to assess the impact of these shifts on model ***:Our results reveal a significant association between temporal shift patterns and model performance (p © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives

arXiv

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arXiv 2025年

作者： Hossain, Delower Chen, Jake Y. Dept. of Computer Science The University of Alabama Birmingham United States Dept. of Genetic Biomedical Engineering Informatics and Data Science Heersink School of Medicine The University of Alabama Birmingham United States Systems Pharmacology AI Research Center The University of Alabama Birmingham United States

Over the last few decades, Artificial Intelligence (AI) scientists have been conducting investigations to attain human-level performance by a machine in accomplishing a cognitive task. Within machine learning, the ultimate aspiration is to attain Artificial General Intelligence (AGI) through a machine. This pursuit has led to the exploration of two distinct AI paradigms. Symbolic AI, also known as classical or GOFAI (Good Old-Fashioned AI) and Connectionist (Sub-symbolic) AI, represented by Neural Systems, are two mutually exclusive paradigms. Symbolic AI excels in reasoning, explainability, and knowledge representation but faces challenges in processing complex real-world data with noise. Conversely, deep learning (Black-Box systems) research breakthroughs in neural networks are notable, yet they lack reasoning and interpretability. Neuro-symbolic AI (NeSy), an emerging area of AI research, attempts to bridge this gap by integrating logical reasoning into neural networks, enabling them to learn and reason with symbolic representations. While a long path, this strategy has made significant progress towards achieving common sense reasoning by systems. This article conducts an extensive review of over 977 studies from prominent scientific databases (DBLP, ACL, IEEExplore, Scopus, PubMed, ICML, ICLR), thoroughly examining the multifaceted capabilities of Neuro-Symbolic AI, with a particular focus on its healthcare applications, particularly in drug discovery, and Protein engineering research. The survey addresses vital themes, including reasoning, explainability, integration strategies, 41 healthcare-related use cases, benchmarking, datasets, current approach limitations from both healthcare and broader perspectives, and proposed novel approaches for future experiments. Moreover, it identifies critical challenges in applying Neuro-Symbolic AI to medicine and represents a comprehensive exploration of its transformative potential in the biomedical field. © 2025, CC BY-NC

关键词： Knowledge representation

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Predictive analysis of COVID-19 occurrence and vaccination impacts across the 50 US states

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Computers in Biology and Medicine 2025年 185卷 109493-109493页

作者： Rayguru, Chinmayee Husnayain, Atina Chiu, Hua-Sheng Sumazin, Pavel Su, Emily Chia-Yu Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Public Health Monash University Indonesia Department of Pediatrics Baylor College of Medicine Texas Children's Hospital Cancer Center HoustonTX United States Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan

Objective: This study aimed to outline a machine learning model to assess the effectiveness of vaccination in COVID-19 confirmed cases and fatalities. The proposed model was evaluated using external validation to ensure optimal protection of vaccinated populations, distinguishing between males and females. Methods: The data from the Centers for Disease Control and Prevention (CDC) in the US, collected between 2021 and 2023, were preprocessed through merging and imputation. A deep learning long short-term memory (LSTM) model was developed to analyze the effectiveness of vaccination in predicting COVID-19 cases and fatalities. The model, which was validated internally and externally, examined the impact of vaccination according to sex. The performance was assessed against current state-of-the-art models, with the LSTM model exhibiting lower root mean square error (RMSE) values. Results: We performed intra-, inter-, and external-validation analyses. First, one- and two-dose vaccinations significantly reduced the number of COVID-19 cases and mortality in highly affected states. Second, in the inter-model analysis, the LSTM outperformed the autoregressive integrated moving average (ARIMA) model in predicting cases and deaths, yielding superior results for Texas, California, and Florida. Third, with external validation, our LSTM model effectively predicted vaccination impacts regardless of sex. Conclusions: Our study demonstrates the effectiveness of COVID-19 vaccination, showing that full vaccination significantly reduced the number of confirmed cases and deaths, influencing future public health policies. © 2024 Elsevier Ltd

关键词： COVID-19

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Technical light-field setup for 3D imaging of the human nerve head validated with an eye model

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Current Directions in biomedical Engineering 2021年第2期7卷 433-436页

作者： Schramm, Stefan Dietzel, Alexander Blum, Maren-Christina Link, Dietmar Klee, Sascha Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria

With the new technology of 3D light field (LF) imaging, fundus photography can be expanded to provide depth information. This increases the diagnostic possibilities and additionally improves image quality by digitally refocusing. To provide depth information in the human optic nerve head such as in glaucoma diagnostics, a mydriatic fundus camera was upgraded with an LF imager. The aim of the study presented here was the validation of the technical setup and resulting depth estimations with an appropriate eye model. The technical setup consisted of a mydriatic fundus camera (FF450, Carl Zeiss Meditec AG, Jena, Germany) and an LF imager (R12, Raytrix GmbH, Kiel, Germany). The field of view was set to 30°. The eye model (24.65 mm total length) consisted of a two-lens optical system and interchangeable fundus models with papilla excavations from 0.2 to 1 mm in steps of 0.2 mm. They were coated with red acrylic lacquer and vessels were drawn with a thin brush. 15 images were taken for each papilla depth illuminated with green light (wavelength 520 nm ± 20 nm). Papilla depth was measured from the papilla ground to the surrounding flat region. All 15 measurements for each papilla depth were averaged and compared to the printed depth. It was possible to perform 3D fundus imaging in an eye model by means of a novel LF-based optical setup. All LF images could be digitally refocused subsequently. Depth estimation in the eye model was successfully performed over a 30° field of view. The measured virtual depth and the printed model papilla depth is linear correlated. The presented LF setup allowed high-quality 3D one-shot imaging and depth estimation of the optic nerve head in an eye model. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Optical systems

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